The I-STAR Labs
Imaging for Surgery, Therapy, and Radiology
Johns Hopkins University
| 1. | Grace J. Gang, Tom Russ, Yiqun Ma, Christian Toennes, Lothar R. Schad, J. Webster Stayman, Jeffrey H. Siewerdsen Metal-Tolerant Noncircular Orbit Design and Implementation on Robotic C-Arm Systems Journal Article In: Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography, pp. 400-403, 2020. @article{Gang2020b, title = {Metal-Tolerant Noncircular Orbit Design and Implementation on Robotic C-Arm Systems}, author = {Grace J. Gang and Tom Russ and Yiqun Ma and Christian Toennes and Lothar R. Schad and J. Webster Stayman and Jeffrey H. Siewerdsen}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643882/}, year = {2020}, date = {2020-11-05}, journal = {Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography}, pages = {400-403}, abstract = {Metal artifacts are a major confounding factor for image quality in CT, especially in image-guided surgery scenarios where surgical tools and implants frequently occur in the field-of-view. Traditional metal artifact correction methods typically use algorithmic solutions to interpolate over the highly attenuated projection measurements where metal is present but cannot recover the missing information obstructed by the metal. In this work, we treat metal artifacts as a missing data problem and employ noncircular orbits to maximize data completeness in the presence of metal. We first implement a local data completeness metric based on Tuy’s condition as the percentage of great circles sampled by a particular orbit and accounted for the presence of metal by discounting any rays that pass through metal. We then compute the metric over many locations and many possible metal locations to reflect data completeness for arbitrary metal placements within a volume of interest. We used this metric to evaluate the effectiveness of sinusoidal orbits of different magnitudes and frequencies in metal artifact reduction. We also evaluated noncircular orbits in two imaging systems for phantoms with different metal objects and metal arrangements. Among a circular, tilted circular, and a sinusoidal orbit of two cycles per rotation, the latter is shown to most effectively remove metal artifacts. The noncircular orbit not only reduce the extent of streaks, but allows better visualization of spatial frequencies that cannot be recovered by metal artifact correction algorithms. These results illustrate the potential of relatively simple noncircular orbits to be robust against metal implants which ordinarily present significant challenges in interventional imaging.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Metal artifacts are a major confounding factor for image quality in CT, especially in image-guided surgery scenarios where surgical tools and implants frequently occur in the field-of-view. Traditional metal artifact correction methods typically use algorithmic solutions to interpolate over the highly attenuated projection measurements where metal is present but cannot recover the missing information obstructed by the metal. In this work, we treat metal artifacts as a missing data problem and employ noncircular orbits to maximize data completeness in the presence of metal. We first implement a local data completeness metric based on Tuy’s condition as the percentage of great circles sampled by a particular orbit and accounted for the presence of metal by discounting any rays that pass through metal. We then compute the metric over many locations and many possible metal locations to reflect data completeness for arbitrary metal placements within a volume of interest. We used this metric to evaluate the effectiveness of sinusoidal orbits of different magnitudes and frequencies in metal artifact reduction. We also evaluated noncircular orbits in two imaging systems for phantoms with different metal objects and metal arrangements. Among a circular, tilted circular, and a sinusoidal orbit of two cycles per rotation, the latter is shown to most effectively remove metal artifacts. The noncircular orbit not only reduce the extent of streaks, but allows better visualization of spatial frequencies that cannot be recovered by metal artifact correction algorithms. These results illustrate the potential of relatively simple noncircular orbits to be robust against metal implants which ordinarily present significant challenges in interventional imaging. |
| 2. | Stephen Z Liu, Qian Cao, Matthew Tivnan, Steven Wayne Tilley II, Joseph Webster Stayman, Wojciech Zbijewski, Jeffrey H Siewerdsen Model-based dual-energy tomographic image reconstruction of objects containing known metal components Journal Article In: Institute of Physics and Engineering in Medicine, 2020. @article{Liu2020, title = {Model-based dual-energy tomographic image reconstruction of objects containing known metal components}, author = {Stephen Z Liu and Qian Cao and Matthew Tivnan and Steven Wayne Tilley II and Joseph Webster Stayman and Wojciech Zbijewski and Jeffrey H Siewerdsen }, url = {https://iopscience.iop.org/article/10.1088/1361-6560/abc5a9}, doi = {10.1088/1361-6560/abc5a9}, year = {2020}, date = {2020-10-28}, journal = {Institute of Physics and Engineering in Medicine}, abstract = {Dual-energy (DE) decomposition has been adopted in orthopedic imaging to measure bone composition and visualize intraarticular contrast enhancement. One of the potential applications involves monitoring of callus mineralization for longitudinal assessment of fracture healing. However, fracture repair usually involves internal fixation hardware that can generate significant artifacts in reconstructed images. To address this challenge, we develop a novel algorithm that combines simultaneous reconstruction-decomposition using a previously reported method for Model-Based Material Decomposition (MBMD) augmented by the Known-Component (KC) reconstruction framework to mitigate metal artifacts. We apply the proposed algorithm to simulated DE data representative of a dedicated extremity cone-beam CT (CBCT) employing an x-ray unit with three vertically arranged sources. The scanner generates DE data with non-coinciding high- and low-energy projection rays when the central source is operated at high tube potential and the peripheral sources at low potential. The proposed algorithm was validated using a digital extremity phantom containing varying concentrations of Ca-water mixtures and Ti implants. Decomposition accuracy was compared to MBMD without the KC model. The proposed method suppressed metal artifacts and yielded estimated Ca concentrations that approached the reconstructions of an implant-free phantom for most mixture regions. In the vicinity of simple components, the errors of Ca density estimates obtained by incorporating KC in MBMD were ~1.5 – 5x lower than the errors of conventional MBMD; for cases with complex implants, the errors were ~3 – 5x lower. In conclusion, the proposed method can achieve accurate bone mineral density measurements in the presence of metal implants using non-coinciding DE projections acquired on a multisource CBCT system.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Dual-energy (DE) decomposition has been adopted in orthopedic imaging to measure bone composition and visualize intraarticular contrast enhancement. One of the potential applications involves monitoring of callus mineralization for longitudinal assessment of fracture healing. However, fracture repair usually involves internal fixation hardware that can generate significant artifacts in reconstructed images. To address this challenge, we develop a novel algorithm that combines simultaneous reconstruction-decomposition using a previously reported method for Model-Based Material Decomposition (MBMD) augmented by the Known-Component (KC) reconstruction framework to mitigate metal artifacts. We apply the proposed algorithm to simulated DE data representative of a dedicated extremity cone-beam CT (CBCT) employing an x-ray unit with three vertically arranged sources. The scanner generates DE data with non-coinciding high- and low-energy projection rays when the central source is operated at high tube potential and the peripheral sources at low potential. The proposed algorithm was validated using a digital extremity phantom containing varying concentrations of Ca-water mixtures and Ti implants. Decomposition accuracy was compared to MBMD without the KC model. The proposed method suppressed metal artifacts and yielded estimated Ca concentrations that approached the reconstructions of an implant-free phantom for most mixture regions. In the vicinity of simple components, the errors of Ca density estimates obtained by incorporating KC in MBMD were ~1.5 – 5x lower than the errors of conventional MBMD; for cases with complex implants, the errors were ~3 – 5x lower. In conclusion, the proposed method can achieve accurate bone mineral density measurements in the presence of metal implants using non-coinciding DE projections acquired on a multisource CBCT system. |
| 3. | Sarah Capostagno, Alejandro Sisniega, Joseph Webster Stayman, Tina Ehtiati, Clifford Raabe Weiss, Jeffrey H Siewerdsen Deformable motion compensation for interventional cone-beam CT Journal Article In: Institute of Physics and Engineering in Medicine, 2020. @article{2020Capostagno, title = {Deformable motion compensation for interventional cone-beam CT}, author = {Sarah Capostagno and Alejandro Sisniega and Joseph Webster Stayman and Tina Ehtiati and Clifford Raabe Weiss and Jeffrey H Siewerdsen}, url = {https://doi.org/10.1088/1361-6560/abb16e}, doi = {10.1088/1361-6560/abb16e}, year = {2020}, date = {2020-08-21}, journal = {Institute of Physics and Engineering in Medicine}, abstract = {Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5–30 sec). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42–92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5–5 mm-2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ~17% for simple motion and ~21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g., truncation and scatter).}, keywords = {}, pubstate = {published}, tppubtype = {article} } Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5–30 sec). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42–92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5–5 mm-2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ~17% for simple motion and ~21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g., truncation and scatter). |
| 4. | Pengwe Wu, Niral Sheth, Alejandro Sisniega, Ali Uneri, Runze Han, Rohan Vijayan, Prasad Vagdargi, B. Kreher, Holger Kunze, Gerhard Kleinszig, Sebastian Vogt, Sheng-Fu Lo, Nicolas Theodore, Jeffrey H. Siewerdsen C-arm orbits for metal artifact avoidance (MAA) in cone-beam CT Journal Article In: Physics in Medicine & Biology, 65 (16m), 2020. @article{Wu2020d, title = {C-arm orbits for metal artifact avoidance (MAA) in cone-beam CT}, author = {Pengwe Wu and Niral Sheth and Alejandro Sisniega and Ali Uneri and Runze Han and Rohan Vijayan and Prasad Vagdargi and B. Kreher and Holger Kunze and Gerhard Kleinszig and Sebastian Vogt and Sheng-Fu Lo and Nicolas Theodore and Jeffrey H. Siewerdsen}, url = {https://doi.org/10.1088/1361-6560/ab9454}, doi = {10.1088/1361-6560/ab9454}, year = {2020}, date = {2020-08-14}, journal = {Physics in Medicine & Biology}, volume = {65}, number = {16m}, abstract = {Metal artifacts present a challenge to cone-beam CT (CBCT) image-guided surgery, obscuring visualization of metal instruments and adjacent anatomy—often in the very region of interest pertinent to the imaging/surgical tasks. We present a method to reduce the influence of metal artifacts by prospectively defining an image acquisition protocol—viz., the C-arm source-detector orbit—that mitigates metal-induced biases in the projection data. The metal artifact avoidance (MAA) method is compatible with simple mobile C-arms, does not require exact prior information on the patient or metal implants, and is consistent with 3D filtered backprojection (FBP), more advanced (e.g. polyenergetic) model-based image reconstruction (MBIR), and metal artifact reduction (MAR) post-processing methods. The MAA method consists of: (i) coarse localization of metal objects in the field-of-view (FOV) via two or more low-dose scout projection views and segmentation (e.g. a simple U-Net) in coarse backprojection; (ii) model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices accessible by the imaging system (e.g. gantry rotation and tilt angles); and (iii) identification of a circular or non-circular orbit that reduces the variation in spectral shift. The method was developed, tested, and evaluated in a series of studies presenting increasing levels of complexity and realism, including digital simulations, phantom experiment, and cadaver experiment in the context of image-guided spine surgery (pedicle screw implants). The MAA method accurately predicted tilted circular and non-circular orbits that reduced the magnitude of metal artifacts in CBCT reconstructions. Realistic distributions of metal instrumentation were successfully localized (0.71 median Dice coefficient) from 2–6 low-dose scout views even in complex anatomical scenes. The MAA-predicted tilted circular orbits reduced root-mean-square error (RMSE) in 3D image reconstructions by 46%–70% and 'blooming' artifacts (apparent width of the screw shaft) by 20–45%. Non-circular orbits defined by MAA achieved a further ~46% reduction in RMSE compared to the best (tilted) circular orbit. The MAA method presents a practical means to predict C-arm orbits that minimize spectral bias from metal instrumentation. Resulting orbits—either simple tilted circular orbits or more complex non-circular orbits that can be executed with a motorized multi-axis C-arm—exhibited substantial reduction of metal artifacts in raw CBCT reconstructions by virtue of higher fidelity projection data, which are in turn compatible with subsequent MAR post-processing and/or polyenergetic MBIR to further reduce artifacts.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Metal artifacts present a challenge to cone-beam CT (CBCT) image-guided surgery, obscuring visualization of metal instruments and adjacent anatomy—often in the very region of interest pertinent to the imaging/surgical tasks. We present a method to reduce the influence of metal artifacts by prospectively defining an image acquisition protocol—viz., the C-arm source-detector orbit—that mitigates metal-induced biases in the projection data. The metal artifact avoidance (MAA) method is compatible with simple mobile C-arms, does not require exact prior information on the patient or metal implants, and is consistent with 3D filtered backprojection (FBP), more advanced (e.g. polyenergetic) model-based image reconstruction (MBIR), and metal artifact reduction (MAR) post-processing methods. The MAA method consists of: (i) coarse localization of metal objects in the field-of-view (FOV) via two or more low-dose scout projection views and segmentation (e.g. a simple U-Net) in coarse backprojection; (ii) model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices accessible by the imaging system (e.g. gantry rotation and tilt angles); and (iii) identification of a circular or non-circular orbit that reduces the variation in spectral shift. The method was developed, tested, and evaluated in a series of studies presenting increasing levels of complexity and realism, including digital simulations, phantom experiment, and cadaver experiment in the context of image-guided spine surgery (pedicle screw implants). The MAA method accurately predicted tilted circular and non-circular orbits that reduced the magnitude of metal artifacts in CBCT reconstructions. Realistic distributions of metal instrumentation were successfully localized (0.71 median Dice coefficient) from 2–6 low-dose scout views even in complex anatomical scenes. The MAA-predicted tilted circular orbits reduced root-mean-square error (RMSE) in 3D image reconstructions by 46%–70% and 'blooming' artifacts (apparent width of the screw shaft) by 20–45%. Non-circular orbits defined by MAA achieved a further ~46% reduction in RMSE compared to the best (tilted) circular orbit. The MAA method presents a practical means to predict C-arm orbits that minimize spectral bias from metal instrumentation. Resulting orbits—either simple tilted circular orbits or more complex non-circular orbits that can be executed with a motorized multi-axis C-arm—exhibited substantial reduction of metal artifacts in raw CBCT reconstructions by virtue of higher fidelity projection data, which are in turn compatible with subsequent MAR post-processing and/or polyenergetic MBIR to further reduce artifacts. |
| 5. | Sophia A Doerr, Tharindu De Silva, Rohan Vijayan, Runze Han, Ali Uneri, Michael D Ketcha, Xiaoxuan Zhang, Nishanth Khanna, Erick Westbroek, Bowen Jiang, Corinna Zygourakis, Nafi Aygun, Nicholas Theodore, Jeffrey H Siewerdsen Automatic Analysis of Global Spinal Alignment From Simple Annotation of Vertebral Bodies Journal Article In: Journal of Medical Imaging , pp. 16 PAGES, 2020. @article{Doerr2020, title = {Automatic Analysis of Global Spinal Alignment From Simple Annotation of Vertebral Bodies }, author = { Sophia A Doerr and Tharindu De Silva and Rohan Vijayan and Runze Han and Ali Uneri and Michael D Ketcha and Xiaoxuan Zhang and Nishanth Khanna and Erick Westbroek and Bowen Jiang and Corinna Zygourakis and Nafi Aygun and Nicholas Theodore and Jeffrey H Siewerdsen }, url = {https://doi.org/10.1117/1.JMI.7.3.035001}, doi = {10.1117/1.JMI.7.3.035001}, year = {2020}, date = {2020-05-13}, journal = {Journal of Medical Imaging }, pages = {16 PAGES}, abstract = { Purpose: Measurement of global spinal alignment (GSA) is an important aspect of diagnosis and treatment evaluation for spinal deformity but is subject to a high level of inter-reader variability. Approach: Two methods for automatic GSA measurement are proposed to mitigate such variability and reduce the burden of manual measurements. Both approaches use vertebral labels in spine computed tomography (CT) as input: the first (EndSeg) segments vertebral endplates using input labels as seed points; and the second (SpNorm) computes a two-dimensional curvilinear fit to the input labels. Studies were performed to characterize the performance of EndSeg and SpNorm in comparison to manual GSA measurement by five clinicians, including measurements of proximal thoracic kyphosis, main thoracic kyphosis, and lumbar lordosis. Results: For the automatic methods, 93.8% of endplate angle estimates were within the inter-reader 95% confidence interval (CI95). All GSA measurements for the automatic methods were within the inter-reader CI95, and there was no statistically significant difference between automatic and manual methods. The SpNorm method appears particularly robust as it operates without segmentation. Conclusions: Such methods could improve the reproducibility and reliability of GSA measurements and are potentially suitable to applications in large datasets—e.g., for outcome assessment in surgical data science.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Purpose: Measurement of global spinal alignment (GSA) is an important aspect of diagnosis and treatment evaluation for spinal deformity but is subject to a high level of inter-reader variability. Approach: Two methods for automatic GSA measurement are proposed to mitigate such variability and reduce the burden of manual measurements. Both approaches use vertebral labels in spine computed tomography (CT) as input: the first (EndSeg) segments vertebral endplates using input labels as seed points; and the second (SpNorm) computes a two-dimensional curvilinear fit to the input labels. Studies were performed to characterize the performance of EndSeg and SpNorm in comparison to manual GSA measurement by five clinicians, including measurements of proximal thoracic kyphosis, main thoracic kyphosis, and lumbar lordosis. Results: For the automatic methods, 93.8% of endplate angle estimates were within the inter-reader 95% confidence interval (CI95). All GSA measurements for the automatic methods were within the inter-reader CI95, and there was no statistically significant difference between automatic and manual methods. The SpNorm method appears particularly robust as it operates without segmentation. Conclusions: Such methods could improve the reproducibility and reliability of GSA measurements and are potentially suitable to applications in large datasets—e.g., for outcome assessment in surgical data science. |
| 6. | Chumin Zhao, Magdalena Herbst, Sebastian Vogt, Ludwig Ritschl, Steffen Kappler, Wojciech Zbijewski, Jeffrey H. Siewerdsen Cone‐beam imaging with tilted rotation axis: Method and performance evaluation Journal Article In: Medical Physics, 47 (8), pp. 3305-3320, 2020. @article{Zhao2020b, title = {Cone‐beam imaging with tilted rotation axis: Method and performance evaluation}, author = {Chumin Zhao and Magdalena Herbst and Sebastian Vogt and Ludwig Ritschl and Steffen Kappler and Wojciech Zbijewski and Jeffrey H. Siewerdsen }, url = {https://doi.org/10.1002/mp.14209}, doi = {10.1002/mp.14209}, year = {2020}, date = {2020-04-24}, journal = {Medical Physics}, volume = {47}, number = {8}, pages = {3305-3320}, abstract = {Purpose The recently introduced robotic x‐ray systems provide the flexibility to acquire cone‐beam computed tomography (CBCT) data using customized, application‐specific source‐detector trajectories. We exploit this capability to mitigate the effects of x‐ray scatter and noise in CBCT imaging of weight‐bearing foot and cervical spine (C‐spine) using scan orbits with a tilted rotation axis. Methods We used an advanced CBCT simulator implementing accurate models of x‐ray scatter, primary attenuation, and noise to investigate the effects of the orbital tilt angle in upright foot and C‐spine imaging. The system model was parameterized using a laboratory version of a three‐dimensional (3D) robotic x‐ray system (Multitom RAX, Siemens Healthineers). We considered a generalized tilted axis scan configuration, where the detector remained parallel to patient's long body axis during the acquisition, but the elevation of source and detector was changing. A modified Feldkamp–Davis–Kress (FDK) algorithm was developed for reconstruction in this configuration, which departs from the FDK assumption of a detector that is perpendicular to the scan plane. The simulated foot scans involved source‐detector distance (SDD) of 1386 mm, orbital tilt angles ranging 10° to 40°, and 400 views at 1 mAs/view and 0.5° increment; the C‐spine scans involved −25° to −45° tilt angles, SDD of 1090 mm, and 202 views at 1.3 mAs and 1° increment The imaging performance was assessed by projection‐domain measurements of the scatter‐to‐primary ratio (SPR) and by reconstruction‐domain measurements of contrast, noise and generalized contrast‐to‐noise ratio (gCNR, accounting for both image noise and background nonuniformity) of the metatarsals (foot imaging) and cervical vertebrae (spine imaging). The effects of scatter correction were also compared for horizontal and tilted scans using an ideal Monte Carlo (MC)‐based scatter correction and a frame‐by‐frame mean scatter correction. Results The proposed modified FDK, involving projection resampling, mitigated streak artifacts caused by the misalignment between the filtering direction and the detector rows. For foot imaging (no grids), an optimized 20° tilted orbit reduced the maximum SPR from ~1.5 in a horizontal scan to <0.5. The gCNR of the second metatarsal was enhanced twofold compared to a horizontal orbit. For the C‐spine (with vertical grids), imaging with a tilted orbit avoided highly attenuating x‐ray paths through the lower cervical vertebrae and shoulders. A −35° tilted orbit yielded improved image quality and visualization of the lower cervical spine: the SPR of lower cervical vertebrae was reduced from ~10 (horizontal orbit) to <6 (tilted orbit), and the gCNR for C5–C7 increased by a factor of 2. Furthermore, tilted orbits showed potential benefits over horizontal orbits by enabling scatter correction with a simple frame‐by‐frame mean correction without substantial increase in noise‐induced artifacts after the correction. Conclusions Tilted scan trajectories, enabled by the emerging robotic x‐ray system technology, were optimized for CBCT imaging of foot and cervical spine using an advanced simulation framework. The results demonstrated the potential advantages of tilted axis orbits in mitigation of scatter artifacts and improving contrast‐to‐noise ratio in CBCT reconstructions. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Purpose The recently introduced robotic x‐ray systems provide the flexibility to acquire cone‐beam computed tomography (CBCT) data using customized, application‐specific source‐detector trajectories. We exploit this capability to mitigate the effects of x‐ray scatter and noise in CBCT imaging of weight‐bearing foot and cervical spine (C‐spine) using scan orbits with a tilted rotation axis. Methods We used an advanced CBCT simulator implementing accurate models of x‐ray scatter, primary attenuation, and noise to investigate the effects of the orbital tilt angle in upright foot and C‐spine imaging. The system model was parameterized using a laboratory version of a three‐dimensional (3D) robotic x‐ray system (Multitom RAX, Siemens Healthineers). We considered a generalized tilted axis scan configuration, where the detector remained parallel to patient's long body axis during the acquisition, but the elevation of source and detector was changing. A modified Feldkamp–Davis–Kress (FDK) algorithm was developed for reconstruction in this configuration, which departs from the FDK assumption of a detector that is perpendicular to the scan plane. The simulated foot scans involved source‐detector distance (SDD) of 1386 mm, orbital tilt angles ranging 10° to 40°, and 400 views at 1 mAs/view and 0.5° increment; the C‐spine scans involved −25° to −45° tilt angles, SDD of 1090 mm, and 202 views at 1.3 mAs and 1° increment The imaging performance was assessed by projection‐domain measurements of the scatter‐to‐primary ratio (SPR) and by reconstruction‐domain measurements of contrast, noise and generalized contrast‐to‐noise ratio (gCNR, accounting for both image noise and background nonuniformity) of the metatarsals (foot imaging) and cervical vertebrae (spine imaging). The effects of scatter correction were also compared for horizontal and tilted scans using an ideal Monte Carlo (MC)‐based scatter correction and a frame‐by‐frame mean scatter correction. Results The proposed modified FDK, involving projection resampling, mitigated streak artifacts caused by the misalignment between the filtering direction and the detector rows. For foot imaging (no grids), an optimized 20° tilted orbit reduced the maximum SPR from ~1.5 in a horizontal scan to <0.5. The gCNR of the second metatarsal was enhanced twofold compared to a horizontal orbit. For the C‐spine (with vertical grids), imaging with a tilted orbit avoided highly attenuating x‐ray paths through the lower cervical vertebrae and shoulders. A −35° tilted orbit yielded improved image quality and visualization of the lower cervical spine: the SPR of lower cervical vertebrae was reduced from ~10 (horizontal orbit) to <6 (tilted orbit), and the gCNR for C5–C7 increased by a factor of 2. Furthermore, tilted orbits showed potential benefits over horizontal orbits by enabling scatter correction with a simple frame‐by‐frame mean correction without substantial increase in noise‐induced artifacts after the correction. Conclusions Tilted scan trajectories, enabled by the emerging robotic x‐ray system technology, were optimized for CBCT imaging of foot and cervical spine using an advanced simulation framework. The results demonstrated the potential advantages of tilted axis orbits in mitigation of scatter artifacts and improving contrast‐to‐noise ratio in CBCT reconstructions. |
| 7. | Jonathan T Kaplan, John W Ramsay, Sarah E Cameron, Kayla D Seymore, Michael Brehler, Gaurav K Thawait, Wojciech B Zbijewski, Tyler N Brown, Jeffrey H Siewerdsen Association between knee anatomic metrics and biomechanics for male soldiers landing with load Journal Article In: The American Journal of Sports Medicine, 48 (6), 2020. @article{Kaplan2020, title = {Association between knee anatomic metrics and biomechanics for male soldiers landing with load }, author = {Jonathan T Kaplan and John W Ramsay and Sarah E Cameron and Kayla D Seymore and Michael Brehler and Gaurav K Thawait and Wojciech B Zbijewski and Tyler N Brown and Jeffrey H Siewerdsen }, url = {https://journals.sagepub.com/doi/10.1177/0363546520911608}, doi = {10.1177/0363546520911608 }, year = {2020}, date = {2020-04-07}, journal = {The American Journal of Sports Medicine}, volume = {48}, number = {6}, abstract = { Background: Anterior cruciate ligament (ACL) injury is a military occupational hazard that may be attributed to an individual's knee biomechanics and joint anatomy. This study sought to determine if greater flexion when landing with load resulted in knee biomechanics thought to decrease ACL injury risk and whether knee biomechanics during landing relate to knee anatomic metrics. Hypothesis: Anatomic metrics regarding the slope and concavity of the tibial plateau will exhibit a significant relation to the increased anterior shear force on the knee and decreased knee flexion posture during landing with body-borne load. Study design: Descriptive laboratory study. Methods: Twenty male military personnel completed a drop landing task with 3 load conditions: light (~6 kg), medium (15% body weight), and heavy (30% body weight). Participants were divided into groups based on knee flexion exhibited when landing with the heavy load (high- and low-Δflexion). Tibial slopes and depth were measured on weightbearing volumetric images of the knee obtained with a prototype cone beam computed tomography system. Knee biomechanics were submitted to a linear mixed model to evaluate the effect of landing group and load, with the anatomic metrics considered covariates. Results: Load increased peak proximal anterior tibial shear force (P = .034), knee flexion angle (P = .024), and moment (P = .001) during landing. Only the high flexion group increased knee flexion (P < .001) during weighted landings with medium and heavy loads. The low flexion group used greater knee abduction angle (P = .030) and peak proximal anterior tibial shear force (P = .034) when landing with load. Anatomic metrics did not differ between groups, but ratio of medial-to-lateral tibial slope and medial tibial depth predicted peak proximal anterior tibial shear force (P = .009) and knee flexion (P = .034) during landing, respectively. Conclusion: Increasing knee flexion is an attainable strategy to mitigate risk of ACL injury, but certain individuals may be predisposed to knee forces and biomechanics that load the ACL during weighted landings. Clinical relevance: The ability to screen individuals for anatomic metrics that predict knee flexion may identify soldiers and athletes who require additional training to mitigate the risk of lower extremity injury. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Background: Anterior cruciate ligament (ACL) injury is a military occupational hazard that may be attributed to an individual's knee biomechanics and joint anatomy. This study sought to determine if greater flexion when landing with load resulted in knee biomechanics thought to decrease ACL injury risk and whether knee biomechanics during landing relate to knee anatomic metrics. Hypothesis: Anatomic metrics regarding the slope and concavity of the tibial plateau will exhibit a significant relation to the increased anterior shear force on the knee and decreased knee flexion posture during landing with body-borne load. Study design: Descriptive laboratory study. Methods: Twenty male military personnel completed a drop landing task with 3 load conditions: light (~6 kg), medium (15% body weight), and heavy (30% body weight). Participants were divided into groups based on knee flexion exhibited when landing with the heavy load (high- and low-Δflexion). Tibial slopes and depth were measured on weightbearing volumetric images of the knee obtained with a prototype cone beam computed tomography system. Knee biomechanics were submitted to a linear mixed model to evaluate the effect of landing group and load, with the anatomic metrics considered covariates. Results: Load increased peak proximal anterior tibial shear force (P = .034), knee flexion angle (P = .024), and moment (P = .001) during landing. Only the high flexion group increased knee flexion (P < .001) during weighted landings with medium and heavy loads. The low flexion group used greater knee abduction angle (P = .030) and peak proximal anterior tibial shear force (P = .034) when landing with load. Anatomic metrics did not differ between groups, but ratio of medial-to-lateral tibial slope and medial tibial depth predicted peak proximal anterior tibial shear force (P = .009) and knee flexion (P = .034) during landing, respectively. Conclusion: Increasing knee flexion is an attainable strategy to mitigate risk of ACL injury, but certain individuals may be predisposed to knee forces and biomechanics that load the ACL during weighted landings. Clinical relevance: The ability to screen individuals for anatomic metrics that predict knee flexion may identify soldiers and athletes who require additional training to mitigate the risk of lower extremity injury. |
| 8. | Runze Han, Ali Uneri, Michael Daniel Ketcha, Rohan Vijayan, Niral Sheth, Pengwei Wu, Prasad Vagdargi, Sebastian Vogt, Gerhard Kleinszig, Greg Michael Osgood, Jeffrey H Siewerdsen Multi-body 3D-2D registration for image-guided reduction of pelvic dislocation in orthopaedic trauma surgery Journal Article In: Physics in Medicine & Biology, 65 (13), 2020. @article{Han2020b, title = {Multi-body 3D-2D registration for image-guided reduction of pelvic dislocation in orthopaedic trauma surgery}, author = {Runze Han and Ali Uneri and Michael Daniel Ketcha and Rohan Vijayan and Niral Sheth and Pengwei Wu and Prasad Vagdargi and Sebastian Vogt and Gerhard Kleinszig and Greg Michael Osgood and Jeffrey H Siewerdsen}, url = {https://iopscience.iop.org/article/10.1088/1361-6560/ab843c}, doi = {10.1088/1361-6560/ab843c }, year = {2020}, date = {2020-03-27}, journal = {Physics in Medicine & Biology}, volume = {65}, number = {13}, abstract = {Purpose. Surgical reduction of pelvic dislocation is a challenging procedure with poor long-term prognosis if natural morphology is not accurately restored. The procedure often requires long fluoroscopic exposure times and trial-and-error to achieve accurate reduction. We report a method to automatically compute the target pose of dislocated bones from preoperative CT and provide 3D guidance of reduction using routine 2D fluoroscopy. Method. A pelvic statistical shape model (SSM) and a statistical pose model (SPM) were formed automatic bone segmentation and estimation of dislocated bone target pose. Intraoperatively, 3D pose of multiple bones were obtained via 3D-2D registration to fluoroscopy images. The method was examined in three studies: a simulation, a phantom, and a clinical case study. Algorithm sensitivity to capture range, radiation dose, and field of view (FOV) size were investigated. Results. The simulation study achieved target pose estimation with translational error of median 2.3 mm (1.4 mm IQR) and rotational error of 2.1° (1.3° IQR). 3D-2D registration yielded 0.3 mm (0.2 mm IQR) in-plane and 0.3 mm (0.2 mm IQR) out-of-plane translational error, with capture range of ±50 mm and ±120 mm, respectively. The phantom study demonstrated 3D-2D target registration error of 2.5 mm (1.5 mm IQR) with robustness over dose range down to 5 μGy/frame (10% of the nominal fluoroscopic dose). The clinical case yielded 3.1 mm (1.0 mm IQR) projection distance error with robust performance for square FOV ranging 340-170 mm². Conclusion. The method demonstrated accurate target reduction pose estimation in simulation, phantom, and clinical feasibility study for a broad range of dislocation patterns, initialization error, dose levels, and FOV size. The system provides a novel means of guidance and assessment of pelvic reduction from routinely acquired images. The method has the potential to reduce radiation dose and guide more accurate joint dislocation reductions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Purpose. Surgical reduction of pelvic dislocation is a challenging procedure with poor long-term prognosis if natural morphology is not accurately restored. The procedure often requires long fluoroscopic exposure times and trial-and-error to achieve accurate reduction. We report a method to automatically compute the target pose of dislocated bones from preoperative CT and provide 3D guidance of reduction using routine 2D fluoroscopy. Method. A pelvic statistical shape model (SSM) and a statistical pose model (SPM) were formed automatic bone segmentation and estimation of dislocated bone target pose. Intraoperatively, 3D pose of multiple bones were obtained via 3D-2D registration to fluoroscopy images. The method was examined in three studies: a simulation, a phantom, and a clinical case study. Algorithm sensitivity to capture range, radiation dose, and field of view (FOV) size were investigated. Results. The simulation study achieved target pose estimation with translational error of median 2.3 mm (1.4 mm IQR) and rotational error of 2.1° (1.3° IQR). 3D-2D registration yielded 0.3 mm (0.2 mm IQR) in-plane and 0.3 mm (0.2 mm IQR) out-of-plane translational error, with capture range of ±50 mm and ±120 mm, respectively. The phantom study demonstrated 3D-2D target registration error of 2.5 mm (1.5 mm IQR) with robustness over dose range down to 5 μGy/frame (10% of the nominal fluoroscopic dose). The clinical case yielded 3.1 mm (1.0 mm IQR) projection distance error with robust performance for square FOV ranging 340-170 mm². Conclusion. The method demonstrated accurate target reduction pose estimation in simulation, phantom, and clinical feasibility study for a broad range of dislocation patterns, initialization error, dose levels, and FOV size. The system provides a novel means of guidance and assessment of pelvic reduction from routinely acquired images. The method has the potential to reduce radiation dose and guide more accurate joint dislocation reductions. |
| 9. | Ali Uneri, Xiaoxuan Zhang, Michael Ketcha, Sophia A. Doerr, Craig K. Jones, Patrick A. Helm, Jeffrey H. Siewerdsen SPIE Medical Imaging, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, Houston, Texas, United States, 11315 , 2020. @proceedings{Uneri2020, title = {Multi-slot intraoperative imaging and 3D-2D registration for evaluation of long surgical constructs in spine surgery}, author = {Ali Uneri and Xiaoxuan Zhang and Michael Ketcha and Sophia A. Doerr and Craig K. Jones and Patrick A. Helm and Jeffrey H. Siewerdsen}, url = {https://doi.org/10.1117/12.2549876}, doi = {10.1117/12.2549876}, year = {2020}, date = {2020-03-16}, volume = {11315}, publisher = {Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling}, address = {Houston, Texas, United States}, organization = {SPIE Medical Imaging,}, abstract = {Purpose. Initial prototype for an intraoperative slot-scan imaging and registration technique is reported for 3D guidance and confirmation of surgical constructs in spine surgery – specifically, involving placement of pedicle screws in multilevel spinal fusion or deformity correction. The unique projection geometry provided by a multi-slot collimator is exploited to produce parallax views of the radiographic scene for accurate 3D registration from a single scan. Methods. The approach takes advantage of the collimator apertures that form disparate fan-beam views to perform 3D- 2D registration of 3D implant models (pedicle screws) and 2D raw detector slot-scan measurements acquired via linear motion of the gantry. Experiments using a prototype O-arm (Medtronic, Littleton MA) were conducted in a cadaver specimen to evaluate the geometric accuracy of multi-slot registration to that of conventional 3D-2D registration from multiple fluoroscopic views. Results. Cadaver studies showed the multi-slot apertures to provide a sufficient degree of parallax to estimate the 3D location of implants. Spinal pedicle screws were registered from a single slot-scan, with mean target registration error of <1.5 mm, comparable to the margins of errors for optical surgical tracking and dual radiograph (AP + Lat) registration. Conclusions. Presented work demonstrates the feasibility of using a multi-slot collimator to perform 3D pose estimation of the patient and surgical implants in intraoperative images. The method is particularly suitable to evaluating the quality of long surgical constructs, as in spinal deformity correction. Ongoing work includes radiation dosimetry in comparison to conventional radiography and streamlined integration with tools for automatic analysis of global spinal alignment.}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Purpose. Initial prototype for an intraoperative slot-scan imaging and registration technique is reported for 3D guidance and confirmation of surgical constructs in spine surgery – specifically, involving placement of pedicle screws in multilevel spinal fusion or deformity correction. The unique projection geometry provided by a multi-slot collimator is exploited to produce parallax views of the radiographic scene for accurate 3D registration from a single scan. Methods. The approach takes advantage of the collimator apertures that form disparate fan-beam views to perform 3D- 2D registration of 3D implant models (pedicle screws) and 2D raw detector slot-scan measurements acquired via linear motion of the gantry. Experiments using a prototype O-arm (Medtronic, Littleton MA) were conducted in a cadaver specimen to evaluate the geometric accuracy of multi-slot registration to that of conventional 3D-2D registration from multiple fluoroscopic views. Results. Cadaver studies showed the multi-slot apertures to provide a sufficient degree of parallax to estimate the 3D location of implants. Spinal pedicle screws were registered from a single slot-scan, with mean target registration error of <1.5 mm, comparable to the margins of errors for optical surgical tracking and dual radiograph (AP + Lat) registration. Conclusions. Presented work demonstrates the feasibility of using a multi-slot collimator to perform 3D pose estimation of the patient and surgical implants in intraoperative images. The method is particularly suitable to evaluating the quality of long surgical constructs, as in spinal deformity correction. Ongoing work includes radiation dosimetry in comparison to conventional radiography and streamlined integration with tools for automatic analysis of global spinal alignment. |
| 10. | Stuart R. Miller, Bipin Singh, Matthew S. J. Marshall, Conner Brown, Niral Sheth, Gengxin Shi, Wojciech Zbijewski, Vivek V. Nagarkar, Jeffrey H. Siewerdsen Pixelated columnar CsI:Tl scintillator for high resolution radiography and cone-beam CT Proceeding Medical Imaging 2020: Physics of Medical Imaging SPIE Medical Imaging, Houston, Texas, United States, 11312 (7), 2020. @proceedings{Miller2020, title = {Pixelated columnar CsI:Tl scintillator for high resolution radiography and cone-beam CT}, author = {Stuart R. Miller and Bipin Singh and Matthew S. J. Marshall and Conner Brown and Niral Sheth and Gengxin Shi and Wojciech Zbijewski and Vivek V. Nagarkar and Jeffrey H. Siewerdsen}, url = {https://doi.org/10.1117/12.2550196}, doi = {10.1117/12.2550196}, year = {2020}, date = {2020-03-16}, volume = {11312}, number = {7}, publisher = {SPIE Medical Imaging}, address = {Houston, Texas, United States}, organization = {Medical Imaging 2020: Physics of Medical Imaging}, abstract = {Microcolumnar CsI:Tl scintillator screens have been the gold standard in X-ray imaging for many years due to their high density, high atomic number, and scintillation efficiency. The structured screens provide an improvement in performance by channeling the light to the detector, improving detection efficiency and spatial resolution. We have taken this concept a step further by laser-machining the CsI:Tl scintillator to provide pixels that match the detector pixels. This allows for still thicker CsI:Tl layers up to 700 μm pixelated with pitch of 100 μm to match CMOS flat panel pixels, thus improving X-ray absorption and resolution. We are investigating the applications of CMOS detectors with pixelated scintillators for imaging of bone microarchitecture on diagnostic Cone Beam CT (CBCT) systems to provide improved quantitative metrics for diagnosis of osteoporosis and osteoarthritis. The scintillator design includes reflective coatings applied to the laser-cut grooves to improve optical isolation between pixels. Such coatings are created by atomic layer deposition (ALD), a unique approach, which permits formation of reflectors over inter-pixel grooves with aspect ratios as high as 140:1. Here we present initial results quantifying performance gains in CMOS detector resolution and their impact on the quality of bone microstructure segmentation. We demonstrate 77% gain in spatial resolution at 2 lp/mm and extension of the limiting resolution from 3 lp/mm to 4.5 lp/mm for the CMOS detector with a pixelated screen compared to a commercial sensor. In a bench-top CBCT study emulating diagnostic systems for orthopedic applications (extremity CBCT), we achieved >0.75 correlations in metrics of trabecular microarchitecture between pixelated CsI:Tl based CBCT and gold-standard micro-CT. The pixelated scintillator is expected to have significant impact for many other applications including mammography and digital radiography, where resolution and dose efficiency (DQE) of the detector are of critical importance.}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Microcolumnar CsI:Tl scintillator screens have been the gold standard in X-ray imaging for many years due to their high density, high atomic number, and scintillation efficiency. The structured screens provide an improvement in performance by channeling the light to the detector, improving detection efficiency and spatial resolution. We have taken this concept a step further by laser-machining the CsI:Tl scintillator to provide pixels that match the detector pixels. This allows for still thicker CsI:Tl layers up to 700 μm pixelated with pitch of 100 μm to match CMOS flat panel pixels, thus improving X-ray absorption and resolution. We are investigating the applications of CMOS detectors with pixelated scintillators for imaging of bone microarchitecture on diagnostic Cone Beam CT (CBCT) systems to provide improved quantitative metrics for diagnosis of osteoporosis and osteoarthritis. The scintillator design includes reflective coatings applied to the laser-cut grooves to improve optical isolation between pixels. Such coatings are created by atomic layer deposition (ALD), a unique approach, which permits formation of reflectors over inter-pixel grooves with aspect ratios as high as 140:1. Here we present initial results quantifying performance gains in CMOS detector resolution and their impact on the quality of bone microstructure segmentation. We demonstrate 77% gain in spatial resolution at 2 lp/mm and extension of the limiting resolution from 3 lp/mm to 4.5 lp/mm for the CMOS detector with a pixelated screen compared to a commercial sensor. In a bench-top CBCT study emulating diagnostic systems for orthopedic applications (extremity CBCT), we achieved >0.75 correlations in metrics of trabecular microarchitecture between pixelated CsI:Tl based CBCT and gold-standard micro-CT. The pixelated scintillator is expected to have significant impact for many other applications including mammography and digital radiography, where resolution and dose efficiency (DQE) of the detector are of critical importance. |
| 11. | Sophia A. Doerr, Ali Uneri, Yixuan Huang, Craig K. Jones, Xiaoxuan Zhang, Michael Ketcha, Patrick A. Helm, Jeffrey H. Siewerdsen Data-driven detection and registration of spine surgery instrumentation in intraoperative images Proceeding SPIE Medical Imaging, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, Houston, Texas, United States, 11315 (8), 2020. @proceedings{Doerr2020b, title = {Data-driven detection and registration of spine surgery instrumentation in intraoperative images}, author = {Sophia A. Doerr and Ali Uneri and Yixuan Huang and Craig K. Jones and Xiaoxuan Zhang and Michael Ketcha and Patrick A. Helm and Jeffrey H. Siewerdsen}, url = {https://doi.org/10.1117/12.2550052}, doi = {10.1117/12.2550052}, year = {2020}, date = {2020-03-16}, volume = {11315}, number = {8}, publisher = {Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling}, address = {Houston, Texas, United States}, organization = {SPIE Medical Imaging,}, abstract = {Purpose. Conventional model-based 3D-2D registration algorithms can be challenged by limited capture range, model validity, and stringent intraoperative runtime requirements. In this work, a deep convolutional neural network was used to provide robust initialization of a registration algorithm (known-component registration, KC-Reg) for 3D localization of spine surgery implants, combining the speed and global support of data-driven approaches with the previously demonstrated accuracy of model-based registration. Methods. The approach uses a Faster R-CNN architecture to detect and localize a broad variety and orientation of spinal pedicle screws in clinical images. Training data were generated using projections from 17 clinical cone-beam CT scans and a library of screw models to simulate implants. Network output was processed to provide screw count and 2D poses. The network was tested on two test datasets of 2,000 images, each depicting real anatomy and realistic spine surgery instrumentation – one dataset involving the same patient data as in the training set (but with different screws, poses, image noise, and affine transformations) and one dataset with five patients unseen in the test data. Assessment of device detection was quantified in terms of accuracy and specificity, and localization accuracy was evaluated in terms of intersection-overunion (IOU) and distance between true and predicted bounding box coordinates. Results. The overall accuracy of pedicle screw detection was ~86.6% (85.3% for the same-patient dataset and 87.8% for the many-patient dataset), suggesting that the screw detection network performed reasonably well irrespective of disparate, complex anatomical backgrounds. The precision of screw detection was ~92.6% (95.0% and 90.2% for the respective same-patient and many-patient datasets). The accuracy of screw localization was within 1.5 mm (median difference of bounding box coordinates), and median IOU exceeded 0.85. For purposes of initializing a 3D-2D registration algorithm, the accuracy was observed to be well within the typical capture range of KC-Reg.1 Conclusions. Initial evaluation of network performance indicates sufficient accuracy to integrate with algorithms for implant registration, guidance, and verification in spine surgery. Such capability is of potential use in surgical navigation, robotic assistance, and data-intensive analysis of implant placement in large retrospective datasets. Future work includes correspondence of multiple views, 3D localization, screw classification, and expansion of the training dataset to a broader variety of anatomical sites, number of screws, and types of implants.}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Purpose. Conventional model-based 3D-2D registration algorithms can be challenged by limited capture range, model validity, and stringent intraoperative runtime requirements. In this work, a deep convolutional neural network was used to provide robust initialization of a registration algorithm (known-component registration, KC-Reg) for 3D localization of spine surgery implants, combining the speed and global support of data-driven approaches with the previously demonstrated accuracy of model-based registration. Methods. The approach uses a Faster R-CNN architecture to detect and localize a broad variety and orientation of spinal pedicle screws in clinical images. Training data were generated using projections from 17 clinical cone-beam CT scans and a library of screw models to simulate implants. Network output was processed to provide screw count and 2D poses. The network was tested on two test datasets of 2,000 images, each depicting real anatomy and realistic spine surgery instrumentation – one dataset involving the same patient data as in the training set (but with different screws, poses, image noise, and affine transformations) and one dataset with five patients unseen in the test data. Assessment of device detection was quantified in terms of accuracy and specificity, and localization accuracy was evaluated in terms of intersection-overunion (IOU) and distance between true and predicted bounding box coordinates. Results. The overall accuracy of pedicle screw detection was ~86.6% (85.3% for the same-patient dataset and 87.8% for the many-patient dataset), suggesting that the screw detection network performed reasonably well irrespective of disparate, complex anatomical backgrounds. The precision of screw detection was ~92.6% (95.0% and 90.2% for the respective same-patient and many-patient datasets). The accuracy of screw localization was within 1.5 mm (median difference of bounding box coordinates), and median IOU exceeded 0.85. For purposes of initializing a 3D-2D registration algorithm, the accuracy was observed to be well within the typical capture range of KC-Reg.1 Conclusions. Initial evaluation of network performance indicates sufficient accuracy to integrate with algorithms for implant registration, guidance, and verification in spine surgery. Such capability is of potential use in surgical navigation, robotic assistance, and data-intensive analysis of implant placement in large retrospective datasets. Future work includes correspondence of multiple views, 3D localization, screw classification, and expansion of the training dataset to a broader variety of anatomical sites, number of screws, and types of implants. |
| 12. | Xiaoxuan Zhang, Ali Uneri, Pengwei Wu, Micheal Ketcha, Sophia Doerr, Craig K. Jones, Patrick A. Helm, Jeffrey H. Siewerdsen SPIE Medical Imaging, 2020 Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, Houston, Texas, United States, 11315 , 2020. @proceedings{Zhang2020, title = {Multi-slot extended view imaging on the O-Arm: image quality and application to intraoperative assessment of spinal morphology}, author = {Xiaoxuan Zhang and Ali Uneri and Pengwei Wu and Micheal Ketcha and Sophia Doerr and Craig K. Jones and Patrick A. Helm and Jeffrey H. Siewerdsen}, url = {Event: SPIE Medical Imaging, 2020, Houston, Texas, United States}, doi = {10.1117/12.2549710}, year = {2020}, date = {2020-03-16}, volume = {11315}, publisher = {Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling}, address = { Houston, Texas, United States}, organization = {SPIE Medical Imaging, 2020}, abstract = { Abstract Purpose. Surgical treatment of spinal deformity often seeks to achieve a given change in spinal curvature for the desired surgical outcome. However, it can be difficult to reliably evaluate changes in spinal curvature in the operating room based on qualitative evaluation or radiographs covering a limited field of view (FOV). We report a prototype beam filtration hardware configuration constructed on the O-arm imaging system and an image reconstruction algorithm for extended view (EV) imaging to enable such clear, long-length visualization of the spine and long surgical constructs. Methods. EV imaging on the O-arm involves a novel multi-slot collimator and longitudinal translation of the gantry. A weighted-backprojection algorithm was developed for EV image reconstruction. Image quality and geometric accuracy was evaluated in simulation and phantom studies to quantitatively characterize the depth resolution and potential sources of geometric distortion. A cadaver study was conducted to verify the quality of visualization in EV images and the potential for measurement of global spinal alignment (GSA) in the operating room. Results. EV imaging provided images spanning up to 65 cm length. Analogous to tomosynthesis, EV image reconstruction provides a modest degree of depth resolution and out-of-plane clutter rejection. The phantom study presenting highcontrast spheres in foam-core exhibited ~11% reduction in signal magnitude at 60 cm from the specified focal plane. The geometric accuracy of EV image reconstructions was high for objects at the focal plane, and distortion outside the focal plane was accurately described by predictions based on the known system geometry and object location. In addition to extending the FOV length by more than a factor of 3, EV images demonstrated strong improvement in visual image quality compared to a plain radiograph, and provided clear visualization of structures necessary for evaluation of GSA– e.g., vertebral endplates at the cervical-thoracic, thoraco-lumbar, and lumbar-sacral junctions. Conclusions. The multi-slot EV imaging technique offers a promising means for intraoperative visualization and assessment of spinal deformity correction through improved visualization over a long FOV and accurate measurement of distance and angles for GSA analysis. Future work involves integration of EV imaging with automated vertebral labeling, GSA analysis, and registration of surgical instrumentation in long surgical constructs. © (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. }, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Abstract Purpose. Surgical treatment of spinal deformity often seeks to achieve a given change in spinal curvature for the desired surgical outcome. However, it can be difficult to reliably evaluate changes in spinal curvature in the operating room based on qualitative evaluation or radiographs covering a limited field of view (FOV). We report a prototype beam filtration hardware configuration constructed on the O-arm imaging system and an image reconstruction algorithm for extended view (EV) imaging to enable such clear, long-length visualization of the spine and long surgical constructs. Methods. EV imaging on the O-arm involves a novel multi-slot collimator and longitudinal translation of the gantry. A weighted-backprojection algorithm was developed for EV image reconstruction. Image quality and geometric accuracy was evaluated in simulation and phantom studies to quantitatively characterize the depth resolution and potential sources of geometric distortion. A cadaver study was conducted to verify the quality of visualization in EV images and the potential for measurement of global spinal alignment (GSA) in the operating room. Results. EV imaging provided images spanning up to 65 cm length. Analogous to tomosynthesis, EV image reconstruction provides a modest degree of depth resolution and out-of-plane clutter rejection. The phantom study presenting highcontrast spheres in foam-core exhibited ~11% reduction in signal magnitude at 60 cm from the specified focal plane. The geometric accuracy of EV image reconstructions was high for objects at the focal plane, and distortion outside the focal plane was accurately described by predictions based on the known system geometry and object location. In addition to extending the FOV length by more than a factor of 3, EV images demonstrated strong improvement in visual image quality compared to a plain radiograph, and provided clear visualization of structures necessary for evaluation of GSA– e.g., vertebral endplates at the cervical-thoracic, thoraco-lumbar, and lumbar-sacral junctions. Conclusions. The multi-slot EV imaging technique offers a promising means for intraoperative visualization and assessment of spinal deformity correction through improved visualization over a long FOV and accurate measurement of distance and angles for GSA analysis. Future work involves integration of EV imaging with automated vertebral labeling, GSA analysis, and registration of surgical instrumentation in long surgical constructs. © (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
| 13. | Runze Han, Ali Uneri, Pengwei Wu, Rohan Vijayan, Prasad Vagdargi, Michael Ketcha, Niral Sheth, Sebastian Vogt, Gerhard Kleinszig; Greg M. Osgood, Jeffrey H. Siewerdsen Multi-body registration for fracture reduction in orthopaedic trauma surgery Proceeding SPIE Medical Imaging, 2020, Houston, Texas, United States, 11315 , 2020. @proceedings{Han2020, title = {Multi-body registration for fracture reduction in orthopaedic trauma surgery}, author = { Runze Han and Ali Uneri and Pengwei Wu and Rohan Vijayan and Prasad Vagdargi and Michael Ketcha and Niral Sheth and Sebastian Vogt and Gerhard Kleinszig; Greg M. Osgood and Jeffrey H. Siewerdsen}, url = { https://doi.org/10.1117/12.2549708}, doi = {10.1117/12.2549708}, year = {2020}, date = {2020-03-16}, journal = {Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; }, volume = {11315}, publisher = {SPIE Medical Imaging, 2020}, address = {Houston, Texas, United States}, abstract = { Abstract Purpose. Fracture reduction is a challenging part of orthopaedic pelvic trauma procedures, resulting in poor long-term prognosis if reduction does not accurately restore natural morphology. Manual preoperative planning is performed to obtain target transformations of target bones – a process that is challenging and time-consuming even to experts within the rapid workflow of emergent care and fluoroscopically guided surgery. We report a method for fracture reduction planning using a novel image-based registration framework. Method. An objective function is designed to simultaneously register multi-body bone fragments that are preoperatively segmented via a graph-cut method to a pelvic statistical shape model (SSM) with inter-body collision constraints. An alternating optimization strategy switches between fragments alignment and SSM adaptation to solve for the fragment transformations for fracture reduction planning. The method was examined in a leave-one-out study performed over a pelvic atlas with 40 members with two-body and three-body fractures simulated in the left innominate bone with displacements ranging 0–20 mm and 0°–15°. Result. Experiments showed the feasibility of the registration method in both two-body and three-body fracture cases. The segmentations achieved Dice coefficient of median 0.94 (0.01 interquartile range [IQR]) and root mean square error (RMSE) of 2.93 mm (0.56 mm IQR). In two-body fracture cases, fracture reduction planning yielded 3.8 mm (1.6 mm IQR) translational and 2.9° (1.8° IQR) rotational error. Conclusion. The method demonstrated accurate fracture reduction planning within 5 mm and shows promise for future generalization to more complicated fracture cases. The algorithm provides a novel means of planning from preoperative CT images that are already acquired in standard workflow. }, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Abstract Purpose. Fracture reduction is a challenging part of orthopaedic pelvic trauma procedures, resulting in poor long-term prognosis if reduction does not accurately restore natural morphology. Manual preoperative planning is performed to obtain target transformations of target bones – a process that is challenging and time-consuming even to experts within the rapid workflow of emergent care and fluoroscopically guided surgery. We report a method for fracture reduction planning using a novel image-based registration framework. Method. An objective function is designed to simultaneously register multi-body bone fragments that are preoperatively segmented via a graph-cut method to a pelvic statistical shape model (SSM) with inter-body collision constraints. An alternating optimization strategy switches between fragments alignment and SSM adaptation to solve for the fragment transformations for fracture reduction planning. The method was examined in a leave-one-out study performed over a pelvic atlas with 40 members with two-body and three-body fractures simulated in the left innominate bone with displacements ranging 0–20 mm and 0°–15°. Result. Experiments showed the feasibility of the registration method in both two-body and three-body fracture cases. The segmentations achieved Dice coefficient of median 0.94 (0.01 interquartile range [IQR]) and root mean square error (RMSE) of 2.93 mm (0.56 mm IQR). In two-body fracture cases, fracture reduction planning yielded 3.8 mm (1.6 mm IQR) translational and 2.9° (1.8° IQR) rotational error. Conclusion. The method demonstrated accurate fracture reduction planning within 5 mm and shows promise for future generalization to more complicated fracture cases. The algorithm provides a novel means of planning from preoperative CT images that are already acquired in standard workflow. |
| 14. | Rohan Vijayan, Runze Han, Pengwei Wu, Niral Sheth, Michael Ketcha, Prasad Vagdargi, Sebastian Vogt, Gerhard Kleinszig, Greg M. Osgood, Ali Uneri, Jeffrey H. Siewerdsen Image-guided robotic k-wire placement for orthopaedic trauma surgery Proceeding Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; , 11315 , 2020. @proceedings{Vijayan2020, title = {Image-guided robotic k-wire placement for orthopaedic trauma surgery}, author = {Rohan Vijayan and Runze Han and Pengwei Wu and Niral Sheth and Michael Ketcha and Prasad Vagdargi and Sebastian Vogt and Gerhard Kleinszig and Greg M. Osgood and Ali Uneri and Jeffrey H. Siewerdsen }, url = {https://doi.org/10.1117/12.2549713}, doi = {10.1117/12.2549713}, year = {2020}, date = {2020-03-16}, volume = {11315}, publisher = {Image-Guided Procedures, Robotic Interventions, and Modeling; }, organization = {Medical Imaging 2020:}, abstract = { Abstract Purpose. We report the initial development of an image-based solution for robotic assistance of pelvic fracture fixation. The approach uses intraoperative radiographs, preoperative CT, and an end effector of known design to align the robot with target trajectories in CT. The method extends previous work to solve the robot-to-patient registration from a single radiographic view (without C-arm rotation) and addresses the workflow challenges associated with integrating robotic assistance in orthopaedic trauma surgery in a form that could be broadly applicable to isocentric or non-isocentric C-arms. Methods. The proposed method uses 3D-2D known-component registration to localize a robot end effector with respect to the patient by: (1) exploiting the extended size and complex features of pelvic anatomy to register the patient; and (2) capturing multiple end effector poses using precise robotic manipulation. These transformations, along with an offline hand-eye calibration of the end effector, are used to calculate target robot poses that align the end effector with planned trajectories in the patient CT. Geometric accuracy of the registrations was independently evaluated for the patient and the robot in phantom studies. Results. The resulting translational difference between the ground truth and patient registrations of a pelvis phantom using a single (AP) view was 1.3 mm, compared to 0.4 mm using dual (AP+Lat) views. Registration of the robot in air (i.e., no background anatomy) with five unique end effector poses achieved mean translational difference ~1.4 mm for K-wire placement in the pelvis, comparable to tracker-based margins of error (commonly ~2 mm). Conclusions. The proposed approach is feasible based on the accuracy of the patient and robot registrations and is a preliminary step in developing an image-guided robotic guidance system that more naturally fits the workflow of fluoroscopically guided orthopaedic trauma surgery. Future work will involve end-to-end development of the proposed guidance system and assessment of the system with delivery of K-wires in cadaver studies. }, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Abstract Purpose. We report the initial development of an image-based solution for robotic assistance of pelvic fracture fixation. The approach uses intraoperative radiographs, preoperative CT, and an end effector of known design to align the robot with target trajectories in CT. The method extends previous work to solve the robot-to-patient registration from a single radiographic view (without C-arm rotation) and addresses the workflow challenges associated with integrating robotic assistance in orthopaedic trauma surgery in a form that could be broadly applicable to isocentric or non-isocentric C-arms. Methods. The proposed method uses 3D-2D known-component registration to localize a robot end effector with respect to the patient by: (1) exploiting the extended size and complex features of pelvic anatomy to register the patient; and (2) capturing multiple end effector poses using precise robotic manipulation. These transformations, along with an offline hand-eye calibration of the end effector, are used to calculate target robot poses that align the end effector with planned trajectories in the patient CT. Geometric accuracy of the registrations was independently evaluated for the patient and the robot in phantom studies. Results. The resulting translational difference between the ground truth and patient registrations of a pelvis phantom using a single (AP) view was 1.3 mm, compared to 0.4 mm using dual (AP+Lat) views. Registration of the robot in air (i.e., no background anatomy) with five unique end effector poses achieved mean translational difference ~1.4 mm for K-wire placement in the pelvis, comparable to tracker-based margins of error (commonly ~2 mm). Conclusions. The proposed approach is feasible based on the accuracy of the patient and robot registrations and is a preliminary step in developing an image-guided robotic guidance system that more naturally fits the workflow of fluoroscopically guided orthopaedic trauma surgery. Future work will involve end-to-end development of the proposed guidance system and assessment of the system with delivery of K-wires in cadaver studies. |
| 15. | Chumin Zhao, Christoph Luckner, Magdalena Herbst, Sebastian Vogt, Ludwig Ritschl, Steffen Kappler, Wojtek Zbijewski, Jeffrey H. Siewerdsen Slot-scan dual-energy measurement of bone mineral density on a robotic x-ray system Proceeding SPIE Medical Imaging, 2020 Medical Imaging 2020: Physics of Medical Imaging, Houston, Texas, United States, 11315 , 2020. @proceedings{Zhao2020, title = {Slot-scan dual-energy measurement of bone mineral density on a robotic x-ray system}, author = {Chumin Zhao and Christoph Luckner and Magdalena Herbst and Sebastian Vogt and Ludwig Ritschl and Steffen Kappler and Wojtek Zbijewski and Jeffrey H. Siewerdsen}, url = {https://doi.org/10.1117/12.2549631}, doi = {10.1117/12.2549631}, year = {2020}, date = {2020-03-16}, volume = {11315}, publisher = {Medical Imaging 2020: Physics of Medical Imaging}, address = {Houston, Texas, United States}, organization = { SPIE Medical Imaging, 2020}, abstract = { Abstract Purpose: We investigate the feasibility of slot-scan dual-energy x-ray absorptiometry (DXA) on robotic x-ray platforms capable of synchronized source and detector translation. This novel approach will enhance the capabilities of such platforms to include quantitative assessment of bone quality using areal bone mineral density (aBMD), normally obtained only with a dedicated DXA scanner. Methods: We performed simulation studies of a robotized x-ray platform that enables fast linear translation of the x-ray source and flat-panel detector (FPD) to execute slot-scan dual-energy (DE) imaging of the entire spine. Two consecutive translations are performed to acquire the low-energy (LE, 80 kVp) and high-energy (HE, 120 kVp) data in <15 sec total time. The slot views are corrected with convolution-based scatter estimation and backprojected to yield tiled long-length LE and HE radiographs. Projection-based DE decomposition is applied to the tiled radiographs to yield (i) aBMD measurements in bone, and (ii) adipose content measurement in bone-free regions. The feasibility of achieving accurate aBMD estimates was assessed using a high-fidelity simulation framework with a digital body phantom and a realistic bone model covering a clinically relevant range of mineral densities. Experiments examined the effects of slot size (1 – 20 cm), scatter correction, and patient size/adipose content (waist circumference: 77 – 95 cm) on the accuracy and reproducibility of aBMD. Results: The proposed combination of backprojection-based tiling of the slot views and DE decomposition yielded bone density maps of the spine that were free of any apparent distortions. The x-ray scatter increased with slot width, leading to aBMD errors ranging from 0.2 g/cm2 for a 5 cm slot to 0.7 g/cm2 for a 20 cm slot when no scatter correction was applied. The convolution-based correction reduced the aBMD error to within 0.02 g/cm2 for slot widths <10 cm. Reproducible aBMD measurements across a range of body sizes (aBMD variability <0.1 g/cm2) were achieved by applying a calibration based on DE adipose thickness estimates from peripheral body sites. Conclusion: The feasibility of accurate and reproducible aBMD measurements on an FPD-based x-ray platform was demonstrated using DE slot scan trajectories, backprojection-domain decomposition, scatter correction, and adipose precorrection. }, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Abstract Purpose: We investigate the feasibility of slot-scan dual-energy x-ray absorptiometry (DXA) on robotic x-ray platforms capable of synchronized source and detector translation. This novel approach will enhance the capabilities of such platforms to include quantitative assessment of bone quality using areal bone mineral density (aBMD), normally obtained only with a dedicated DXA scanner. Methods: We performed simulation studies of a robotized x-ray platform that enables fast linear translation of the x-ray source and flat-panel detector (FPD) to execute slot-scan dual-energy (DE) imaging of the entire spine. Two consecutive translations are performed to acquire the low-energy (LE, 80 kVp) and high-energy (HE, 120 kVp) data in <15 sec total time. The slot views are corrected with convolution-based scatter estimation and backprojected to yield tiled long-length LE and HE radiographs. Projection-based DE decomposition is applied to the tiled radiographs to yield (i) aBMD measurements in bone, and (ii) adipose content measurement in bone-free regions. The feasibility of achieving accurate aBMD estimates was assessed using a high-fidelity simulation framework with a digital body phantom and a realistic bone model covering a clinically relevant range of mineral densities. Experiments examined the effects of slot size (1 – 20 cm), scatter correction, and patient size/adipose content (waist circumference: 77 – 95 cm) on the accuracy and reproducibility of aBMD. Results: The proposed combination of backprojection-based tiling of the slot views and DE decomposition yielded bone density maps of the spine that were free of any apparent distortions. The x-ray scatter increased with slot width, leading to aBMD errors ranging from 0.2 g/cm2 for a 5 cm slot to 0.7 g/cm2 for a 20 cm slot when no scatter correction was applied. The convolution-based correction reduced the aBMD error to within 0.02 g/cm2 for slot widths <10 cm. Reproducible aBMD measurements across a range of body sizes (aBMD variability <0.1 g/cm2) were achieved by applying a calibration based on DE adipose thickness estimates from peripheral body sites. Conclusion: The feasibility of accurate and reproducible aBMD measurements on an FPD-based x-ray platform was demonstrated using DE slot scan trajectories, backprojection-domain decomposition, scatter correction, and adipose precorrection. |
| 16. | Sarah Capostagno, Alejandro Sisniega, J. Webster Stayman, Tina Ehtiati, Clifford R. Weiss, Jeffrey. H. Siewerdsen SPIE Medical Imaging, 2020 Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; , Houston, Texas, United States, 11315 , 2020. @proceedings{Capostagno2020, title = {Image-based deformable motion compensation in cone-beam CT: translation to clinical studies in interventional body radiology}, author = {Sarah Capostagno and Alejandro Sisniega and J. Webster Stayman and Tina Ehtiati and Clifford R. Weiss and Jeffrey. H. Siewerdsen}, url = {https://doi.org/10.1117/12.2549998}, doi = {10.1117/12.2549998}, year = {2020}, date = {2020-03-16}, journal = {Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; }, volume = {11315}, pages = {7}, publisher = {Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; }, address = {Houston, Texas, United States}, organization = { SPIE Medical Imaging, 2020}, abstract = { Abstract Purpose: Complex, involuntary, non-periodic, deformable motion presents a confounding factor to cone-beam CT (CBCT) image quality due to long (>10 s) scan times. We report and demonstrate an image-based deformable motion compensation method for CBCT, including phantom, cadaver, and animal studies as precursors to clinical studies. Methods: The method corrects deformable motion in CBCT scan data by solving for a motion vector field (MVF) that optimizes a sharpness criterion in the 3D image (viz., gradient entropy). MVFs are estimated by interpolating M locally rigid motion trajectories across N temporal nodes and are incorporated in a modified 3D filtered backprojection approach. The method was evaluated in a cervical spine phantom under flexion, and a cadaver undergoing variable magnitude of complex motion while imaged on a mobile C-arm (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Further assessment was performed on a preclinical animal study using a clinical fixed-room C-arm (Artis Zee, Siemens Healthineers, Forchheim, Germany). Results: In phantom studies, the algorithm resolved visibility of cervical vertebrae under situations of strong flexion, reducing the root-mean-square error by 60% when compared to a motion-free reference. Reduced motion artifacts (blurring, streaks, and loss of soft-tissue edges) were evident in abdominal CBCT of a cadaver imaged during small, medium, and large motion-induced deformation. The animal study demonstrated reduction of streaks from complex motion of bowel gas during the scan. Conclusion: Overall, the studies demonstrate the robustness of the algorithm to a broad range of motion amplitudes, frequencies, data sources (i.e., mobile or fixed-room C-arms) and other confounding factors in real (not simulated) experimental data (e.g., truncation and scatter). These preclinical studies successfully demonstrate reduction of motion artifacts in CBCT and support translation of the method to clinical studies in interventional body radiology. }, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Abstract Purpose: Complex, involuntary, non-periodic, deformable motion presents a confounding factor to cone-beam CT (CBCT) image quality due to long (>10 s) scan times. We report and demonstrate an image-based deformable motion compensation method for CBCT, including phantom, cadaver, and animal studies as precursors to clinical studies. Methods: The method corrects deformable motion in CBCT scan data by solving for a motion vector field (MVF) that optimizes a sharpness criterion in the 3D image (viz., gradient entropy). MVFs are estimated by interpolating M locally rigid motion trajectories across N temporal nodes and are incorporated in a modified 3D filtered backprojection approach. The method was evaluated in a cervical spine phantom under flexion, and a cadaver undergoing variable magnitude of complex motion while imaged on a mobile C-arm (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Further assessment was performed on a preclinical animal study using a clinical fixed-room C-arm (Artis Zee, Siemens Healthineers, Forchheim, Germany). Results: In phantom studies, the algorithm resolved visibility of cervical vertebrae under situations of strong flexion, reducing the root-mean-square error by 60% when compared to a motion-free reference. Reduced motion artifacts (blurring, streaks, and loss of soft-tissue edges) were evident in abdominal CBCT of a cadaver imaged during small, medium, and large motion-induced deformation. The animal study demonstrated reduction of streaks from complex motion of bowel gas during the scan. Conclusion: Overall, the studies demonstrate the robustness of the algorithm to a broad range of motion amplitudes, frequencies, data sources (i.e., mobile or fixed-room C-arms) and other confounding factors in real (not simulated) experimental data (e.g., truncation and scatter). These preclinical studies successfully demonstrate reduction of motion artifacts in CBCT and support translation of the method to clinical studies in interventional body radiology. |
| 17. | Alejandro Sisniega, Sarah Capostagno, Wojtek Zbijewski, Joseph W. Stayman, Clifford R. Weiss, Tina Ehtiati, Jeffrey H. Siewerdsen Physics of Medical Imaging, Houston, Texas, United States, 11312 , 2020. @proceedings{Sisniega2020, title = {Estimation of local deformable motion in image-based motion compensation for interventional cone-beam CT}, author = {Alejandro Sisniega and Sarah Capostagno and Wojtek Zbijewski and Joseph W. Stayman and Clifford R. Weiss and Tina Ehtiati and Jeffrey H. Siewerdsen}, url = {https://doi.org/10.1117/12.2549753}, doi = {10.1117/12.2549753}, year = {2020}, date = {2020-03-16}, volume = {11312}, publisher = {Physics of Medical Imaging}, address = {Houston, Texas, United States}, abstract = {Purpose: Cone-beam CT is increasingly used for 3D guidance in interventional radiology (IR), but long image acquisition time results in degradation from complex deformable motion of soft-tissue structures. Deformable motion compensation with multi-region autofocus optimization was shown to improve image quality. However, the high dimensionality and non-convexity of the optimization problem challenge its convergence. This work presents preliminary development and early results obtained from an automatic learning-based decision framework to obtain local estimates of basic properties of the deformable motion field, coupled to a preconditioning strategy to simplify the optimization. Methods: Deformable motion properties are estimated with a deep convolutional neural network (CNN) consisting of a concatenation of custom-designed residual blocks. The preliminary design provided an estimate of the local motion amplitude on an 8x8 grid covering an axial slice of a motion-contaminated CBCT volume. The decision framework is coupled to a preconditioning strategy that effectively favors more likely solutions through motion amplitude-driven spatially-varying regularization of the motion trajectory and spatially varying selection of the search range for the optimization problem. The network was trained on simulated data generated from publicly available CT datasets, including simple motion fields. Results: Predictions of local motion amplitude showed good agreement with the true values, with root mean squared error (RMSE) < 10 mm for the complete range of motion distributions explored (sufficient for the intended purpose of initialization). Combination of amplitude prediction with spatially varying regularization and search range setting resulted in improved motion compensation after 1000 iterations of the preconditioned multi-motion autofocus in an example case with complex deformable motion. Extensive validation in a large dataset of complex, multi-motion patterns is underway. Conclusion: The proposed approach shows promising initial results and the potential for automatic local motion estimation with learning-based methods. Pending ongoing development to extend this initial development, the method could simplify and accelerate complex deformable motion compensation with spatially varying preconditioning of the motion estimation.}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Purpose: Cone-beam CT is increasingly used for 3D guidance in interventional radiology (IR), but long image acquisition time results in degradation from complex deformable motion of soft-tissue structures. Deformable motion compensation with multi-region autofocus optimization was shown to improve image quality. However, the high dimensionality and non-convexity of the optimization problem challenge its convergence. This work presents preliminary development and early results obtained from an automatic learning-based decision framework to obtain local estimates of basic properties of the deformable motion field, coupled to a preconditioning strategy to simplify the optimization. Methods: Deformable motion properties are estimated with a deep convolutional neural network (CNN) consisting of a concatenation of custom-designed residual blocks. The preliminary design provided an estimate of the local motion amplitude on an 8x8 grid covering an axial slice of a motion-contaminated CBCT volume. The decision framework is coupled to a preconditioning strategy that effectively favors more likely solutions through motion amplitude-driven spatially-varying regularization of the motion trajectory and spatially varying selection of the search range for the optimization problem. The network was trained on simulated data generated from publicly available CT datasets, including simple motion fields. Results: Predictions of local motion amplitude showed good agreement with the true values, with root mean squared error (RMSE) < 10 mm for the complete range of motion distributions explored (sufficient for the intended purpose of initialization). Combination of amplitude prediction with spatially varying regularization and search range setting resulted in improved motion compensation after 1000 iterations of the preconditioned multi-motion autofocus in an example case with complex deformable motion. Extensive validation in a large dataset of complex, multi-motion patterns is underway. Conclusion: The proposed approach shows promising initial results and the potential for automatic local motion estimation with learning-based methods. Pending ongoing development to extend this initial development, the method could simplify and accelerate complex deformable motion compensation with spatially varying preconditioning of the motion estimation. |
| 18. | Pengwei Wu, Niral Sheth, Alejandro Sisniega, Ali Uneri, Runze Han, Rohan Vijayan, Prasad Vagdargi, B. Kreher, Holger Kunze, Gerhard Kleinszig, Sebastian. Vogt, Sheng-fu Lo, Nicolas Theodore, Jeffrey H. Siewerdsen Method for metal artifact avoidance in C-Arm cone-beam CT Proceeding Physics of Medical Imaging, Houston, Texas, United States, 11312 , 2020. @proceedings{Wu2020b, title = {Method for metal artifact avoidance in C-Arm cone-beam CT}, author = {Pengwei Wu and Niral Sheth and Alejandro Sisniega and Ali Uneri and Runze Han and Rohan Vijayan and Prasad Vagdargi and B. Kreher and Holger Kunze and Gerhard Kleinszig and Sebastian. Vogt and Sheng-fu Lo and Nicolas Theodore and Jeffrey H. Siewerdsen}, url = {https://doi.org/10.1117/12.2549840}, doi = {10.1117/12.2549840}, year = {2020}, date = {2020-03-16}, volume = {11312}, publisher = {Physics of Medical Imaging}, address = {Houston, Texas, United States}, abstract = { Purpose: Metal artifacts remain a challenge for CBCT systems in diagnostic imaging and image-guided surgery, obscuring visualization of metal instruments and surrounding anatomy. We present a method to predict C-arm CBCT orbits that will avoid metal artifacts by acquiring projection data that is least affected by polyenergetic bias. Methods: The metal artifact avoidance (MAA) method operates with a minimum of prior information, is compatible with simple mobile C-arms that are increasingly prevalent in routine use, and is consistent with either 3D filtered backprojection (FBP), more advanced (polyenergetic) model-based image reconstruction (MBIR), and/or metal artifact reduction (MAR) post-processing methods. MAA consists of the following steps: (i) coarse localization of metal objects in the field of view (FOV) via two or more low-dose scout views, coarse backprojection, and segmentation (e.g., with a U-Net); (ii) a simple model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices (gantry rotation and tilt angles) accessible by the imaging system; and (iii) definition of a source-detector orbit that minimizes the view-to-view inconsistency in spectral shift. The method was evaluated in anthropomorphic phantom study emulating pedicle screw placement in spine surgery. Results: Phantom studies confirmed that the MAA method could accurately predict tilt angles that minimize metal artifacts. The proposed U-Net segmentation method was able to localize complex distributions of metal instrumentation (over 70% Dice coefficient) with 6 low-dose scout projections acquired during routine pre-scan collision check. CBCT images acquired at MAA-prescribed tilt angles demonstrated ~50% reduction in “blooming” artifacts (measured as FWHM of the screw shaft). Geometric calibration for tilted orbits at prescribed angular increments with interpolation for intermediate values demonstrated accuracy comparable to non-tilted circular trajectories in terms of the modulation transfer function. Conclusion: The preliminary results demonstrate the ability to predict C-arm orbits that provide projection data with minimal spectral bias from metal instrumentation. Such orbits exhibit strongly reduced metal artifacts, and the projection data are compatible with additional post-processing (metal artifact reduction, MAR) methods to further reduce artifacts and/or reduce noise. Ongoing studies aim to improve the robustness of metal object localization from scout views and investigate additional benefits of non-circular C-arm trajectories. }, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Purpose: Metal artifacts remain a challenge for CBCT systems in diagnostic imaging and image-guided surgery, obscuring visualization of metal instruments and surrounding anatomy. We present a method to predict C-arm CBCT orbits that will avoid metal artifacts by acquiring projection data that is least affected by polyenergetic bias. Methods: The metal artifact avoidance (MAA) method operates with a minimum of prior information, is compatible with simple mobile C-arms that are increasingly prevalent in routine use, and is consistent with either 3D filtered backprojection (FBP), more advanced (polyenergetic) model-based image reconstruction (MBIR), and/or metal artifact reduction (MAR) post-processing methods. MAA consists of the following steps: (i) coarse localization of metal objects in the field of view (FOV) via two or more low-dose scout views, coarse backprojection, and segmentation (e.g., with a U-Net); (ii) a simple model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices (gantry rotation and tilt angles) accessible by the imaging system; and (iii) definition of a source-detector orbit that minimizes the view-to-view inconsistency in spectral shift. The method was evaluated in anthropomorphic phantom study emulating pedicle screw placement in spine surgery. Results: Phantom studies confirmed that the MAA method could accurately predict tilt angles that minimize metal artifacts. The proposed U-Net segmentation method was able to localize complex distributions of metal instrumentation (over 70% Dice coefficient) with 6 low-dose scout projections acquired during routine pre-scan collision check. CBCT images acquired at MAA-prescribed tilt angles demonstrated ~50% reduction in “blooming” artifacts (measured as FWHM of the screw shaft). Geometric calibration for tilted orbits at prescribed angular increments with interpolation for intermediate values demonstrated accuracy comparable to non-tilted circular trajectories in terms of the modulation transfer function. Conclusion: The preliminary results demonstrate the ability to predict C-arm orbits that provide projection data with minimal spectral bias from metal instrumentation. Such orbits exhibit strongly reduced metal artifacts, and the projection data are compatible with additional post-processing (metal artifact reduction, MAR) methods to further reduce artifacts and/or reduce noise. Ongoing studies aim to improve the robustness of metal object localization from scout views and investigate additional benefits of non-circular C-arm trajectories. |
| 19. | Grace J. Gang, J. Webster Stayman, Jeffrey H. Siewerdsen Non-circular CT orbit design for elimination of metal artifacts Proceeding Physics of Medical Imaging , Houston, Texas, United States, 11312 , 2020. @proceedings{Gang2020, title = {Non-circular CT orbit design for elimination of metal artifacts}, author = {Grace J. Gang and J. Webster Stayman and Jeffrey H. Siewerdsen}, url = {https://doi.org/10.1117/12.2550203}, doi = {10.1117/12.2550203}, year = {2020}, date = {2020-03-16}, volume = {11312}, publisher = {Physics of Medical Imaging }, address = {Houston, Texas, United States}, abstract = {Metal artifacts are a well-known problem in computed tomography - particularly in interventional imaging where surgical tools and hardware are often found in the field-of-view. An increasing number of interventional imaging systems are capable of non-circular orbits providing one potential avenue to avoid metal artifacts entirely by careful design of the orbital trajectory. In this work, we propose a general design methodology to find complete data solution by applying Tuy’s condition for data completeness. That is, because metal implants effectively cause missing data in projections, we propose to find orbital designs that will not have missing data based on arbitrary placement of metal within the imaging field-of-view. We present the design process for these missing-data-free orbits and evaluate the orbital designs in simulation experiments. The resulting orbits are highly robust to metal objects and show greatly improved visualization of features that are ordinarily obscured.}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Metal artifacts are a well-known problem in computed tomography - particularly in interventional imaging where surgical tools and hardware are often found in the field-of-view. An increasing number of interventional imaging systems are capable of non-circular orbits providing one potential avenue to avoid metal artifacts entirely by careful design of the orbital trajectory. In this work, we propose a general design methodology to find complete data solution by applying Tuy’s condition for data completeness. That is, because metal implants effectively cause missing data in projections, we propose to find orbital designs that will not have missing data based on arbitrary placement of metal within the imaging field-of-view. We present the design process for these missing-data-free orbits and evaluate the orbital designs in simulation experiments. The resulting orbits are highly robust to metal objects and show greatly improved visualization of features that are ordinarily obscured. |
| 20. | Pengwei Wu, Alejandro Sisniega, J. Webster Stayman, Wojtek Zbijewski, David Foos, Xiaohui Wang, Nishanth Khanna, Nafi Aygun, Robert E. Stevens, Jeffrey H. Siewerdsen Cone‐beam CT for imaging of the head/brain: Development and assessment of scanner prototype and reconstruction algorithms Journal Article In: Medical Physics, 47 (6), pp. 2392-2407, 2020. @article{Wu2020c, title = {Cone‐beam CT for imaging of the head/brain: Development and assessment of scanner prototype and reconstruction algorithms}, author = {Pengwei Wu and Alejandro Sisniega and J. Webster Stayman and Wojtek Zbijewski and David Foos and Xiaohui Wang and Nishanth Khanna and Nafi Aygun and Robert E. Stevens and Jeffrey H. Siewerdsen }, url = {https://doi.org/10.1002/mp.14124}, doi = {10.1002/mp.14124}, year = {2020}, date = {2020-03-07}, journal = {Medical Physics}, volume = {47}, number = {6}, pages = {2392-2407}, abstract = {Purpose Our aim was to develop a high‐quality, mobile cone‐beam computed tomography (CBCT) scanner for point‐of‐care detection and monitoring of low‐contrast, soft‐tissue abnormalities in the head/brain, such as acute intracranial hemorrhage (ICH). This work presents an integrated framework of hardware and algorithmic advances for improving soft‐tissue contrast resolution and evaluation of its technical performance with human subjects. Methods Four configurations of a CBCT scanner prototype were designed and implemented to investigate key aspects of hardware (including system geometry, antiscatter grid, bowtie filter) and technique protocols. An integrated software pipeline (c.f., a serial cascade of algorithms) was developed for artifact correction (image lag, glare, beam hardening and x‐ray scatter), motion compensation, and three‐dimensional image (3D) reconstruction [penalized weighted least squares (PWLS), with a hardware‐specific statistical noise model]. The PWLS method was extended in this work to accommodate multiple, independently moving regions with different resolution (to address both motion compensation and image truncation). Imaging performance was evaluated quantitatively and qualitatively with 41 human subjects in the neurosciences critical care unit (NCCU) at our institution. Results The progression of four scanner configurations exhibited systematic improvement in the quality of raw data by variations in system geometry (source‐detector distance), antiscatter grid, and bowtie filter. Quantitative assessment of CBCT images in 41 subjects demonstrated: ~70% reduction in image nonuniformity with artifact correction methods (lag, glare, beam hardening, and scatter); ~40% reduction in motion‐induced streak artifacts via the multi‐motion compensation method; and ~15% improvement in soft‐tissue contrast‐to‐noise ratio (CNR) for PWLS compared to filtered backprojection (FBP) at matched resolution. Each of these components was important to improve contrast resolution for point‐of‐care cranial imaging. Conclusions This work presents the first application of a high‐quality, point‐of‐care CBCT system for imaging of the head/ brain in a neurological critical care setting. Hardware configuration iterations and an integrated software pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to achieve image quality that could be valuable for point‐of‐care detection and monitoring of a variety of intracranial abnormalities, including ICH and hydrocephalus. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Purpose Our aim was to develop a high‐quality, mobile cone‐beam computed tomography (CBCT) scanner for point‐of‐care detection and monitoring of low‐contrast, soft‐tissue abnormalities in the head/brain, such as acute intracranial hemorrhage (ICH). This work presents an integrated framework of hardware and algorithmic advances for improving soft‐tissue contrast resolution and evaluation of its technical performance with human subjects. Methods Four configurations of a CBCT scanner prototype were designed and implemented to investigate key aspects of hardware (including system geometry, antiscatter grid, bowtie filter) and technique protocols. An integrated software pipeline (c.f., a serial cascade of algorithms) was developed for artifact correction (image lag, glare, beam hardening and x‐ray scatter), motion compensation, and three‐dimensional image (3D) reconstruction [penalized weighted least squares (PWLS), with a hardware‐specific statistical noise model]. The PWLS method was extended in this work to accommodate multiple, independently moving regions with different resolution (to address both motion compensation and image truncation). Imaging performance was evaluated quantitatively and qualitatively with 41 human subjects in the neurosciences critical care unit (NCCU) at our institution. Results The progression of four scanner configurations exhibited systematic improvement in the quality of raw data by variations in system geometry (source‐detector distance), antiscatter grid, and bowtie filter. Quantitative assessment of CBCT images in 41 subjects demonstrated: ~70% reduction in image nonuniformity with artifact correction methods (lag, glare, beam hardening, and scatter); ~40% reduction in motion‐induced streak artifacts via the multi‐motion compensation method; and ~15% improvement in soft‐tissue contrast‐to‐noise ratio (CNR) for PWLS compared to filtered backprojection (FBP) at matched resolution. Each of these components was important to improve contrast resolution for point‐of‐care cranial imaging. Conclusions This work presents the first application of a high‐quality, point‐of‐care CBCT system for imaging of the head/ brain in a neurological critical care setting. Hardware configuration iterations and an integrated software pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to achieve image quality that could be valuable for point‐of‐care detection and monitoring of a variety of intracranial abnormalities, including ICH and hydrocephalus. |