@article{Huang2021,
title = {3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework},
author = {Yixuan Huang and Ali Uneri and Craig K Jones and Xiaoxuan Zhang and Michael Daniel Ketcha and Nafi Aygun and Patrick A Helm and Jeffrey H Siewerdsen},
url = {https://doi.org/10.1088/1361-6560/ac07c7},
doi = {10.1088/1361-6560/ac07c7},
year = {2021},
date = {2021-06-03},
journal = { Institute of Physics and Engineering in Medicine},
abstract = {Purpose: Accurate localization and labeling of vertebrae in computed tomography (CT) is an important step toward more quantitative, automated diagnostic analysis and surgical planning. In this paper, we present a framework (called Ortho2D) for vertebral labeling in CT in a manner that is accurate and memory-efficient. Methods: Ortho2D uses two independent Faster R-CNN networks to detect and classify vertebrae in orthogonal (sagittal and coronal) CT slices. The 2D detections are clustered in 3D to localize vertebrae centroids in the volumetric CT and classify the region (cervical, thoracic, lumbar, or sacral) and vertebral level. A post-process sorting method incorporates the confidence in network output to refine classifications and reduce outliers. Ortho2D was evaluated on a publicly available dataset containing 302 normal and pathological spine CT images with and without surgical instrumentation. Labeling accuracy and memory requirements were assessed in comparison to other recently reported methods. The memory efficiency of Ortho2D permitted extension to high-resolution CT to investigate the potential for further boosts to labeling performance. Results: Ortho2D achieved overall vertebrae detection accuracy of 97.1%, region identification accuracy of 94.3%, and individual vertebral level identification accuracy of 91.0%. The framework achieved 95.8% and 83.6% level identification accuracy in images without and with surgical instrumentation, respectively. Ortho2D met or exceeded the performance of previously reported 2D and 3D labeling methods and reduced memory consumption by a factor of ~50 (at 1 mm voxel size) compared to a 3D U-Net, allowing extension to higher resolution datasets than normally afforded. The accuracy of level identification increased from 80.1% (for standard / low resolution CT) to 95.1% (for high-resolution CT). Conclusions: The Ortho2D method achieved vertebrae labeling performance that is comparable to other recently reported methods with significant reduction in memory consumption, permitting further performance boosts via application to high-resolution CT.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Zhang2021,
title = {Long-length tomosynthesis and 3D-2D registration for intraoperative assessment of spine instrumentation},
author = {Xiaoxuan Zhang and Ali Uneri and Pengwei Wu and Michael Daniel Ketcha and Craig Jones and Yixuan Huang and Sheng-fu L Lo and Patrick A Helm and Jeffrey H Siewerdsen},
url = {https://iopscience.iop.org/article/10.1088/1361-6560/abde96/meta},
doi = {10.1088/1361-6560/abde96},
year = {2021},
date = {2021-01-21},
journal = {Institute of Physics and Engineering in Medicine},
abstract = {Purpose: A system for long-length intraoperative imaging is reported based on longitudinal motion of an O-arm gantry featuring a multi-slot collimator. We assess the utility of long-length tomosynthesis and the geometric accuracy of 3D image registration for surgical guidance and evaluation of long spinal constructs. Methods: A multi-slot collimator with tilted apertures was integrated into an O-arm system for long-length imaging. The multi-slot projective geometry leads to slight view disparity in both long-length projection images (referred to as "line scans") and tomosynthesis "slot reconstructions" produced using a weighted-backprojection method. The radiation dose for long-length imaging was measured, and the utility of long-length, intraoperative tomosynthesis was evaluated in phantom and cadaver studies. Leveraging the depth resolution provided by parallax views, an algorithm for 3D-2D registration of the patient and surgical devices was adapted for registration with line scans and slot reconstructions. Registration performance using single-plane or dual-plane long-length images was evaluated and compared to registration accuracy achieved using standard biplane radiographs. Results: Longitudinal coverage of ~50–64 cm was achieved with a single long-length slot scan, providing a field-of-view up to (40 × 64) cm2, depending on patient positioning. The dose-area product (reference point air kerma × x-ray field area) for a slot scan ranged from ~702–1757 mGy ⋅ cm2, equivalent to ~2.5 s of fluoroscopy and comparable to other long-length imaging systems. Long-length scanning produced high-resolution tomosynthesis reconstructions, covering ~12–16 vertebral levels. 3D image registration using dual-plane slot reconstructions achieved median target registration error (TRE) of 1.2 mm and 0.6° in cadaver studies, outperforming registration to line scans (TRE = 2.8 mm and 2.2°) and biplane radiographs (TRE = 2.5 mm and 1.1°). 3D registration using single-plane slot reconstructions leveraged the ~7–14° angular separation between slots to achieve median TRE ~2 mm and < 2° from a single scan. Conclusion: The multi-slot configuration provided intraoperative visualization of long spine segments, facilitating target localization, assessment of global spinal alignment, and evaluation of long surgical constructs. 3D-2D registration to long-length tomosynthesis reconstructions yielded a promising means of guidance and verification with accuracy exceeding that of 3D-2D registration to conventional radiographs. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Sisniega2021,
title = {Accelerated 3D image reconstruction with a morphological pyramid and noise-power convergence criterion},
author = {Alejandro Sisniega and Joseph Webster Stayman and Sarah Capostagno and Clifford Raabe Weiss and Tina Ehtiati and Jeffrey H Siewerdsen},
url = {https://doi.org/10.1088/1361-6560/abde97},
doi = {10.1088/1361-6560/abde97},
year = {2021},
date = {2021-01-21},
journal = { Institute of Physics and Engineering in Medicine},
abstract = {Model-based iterative reconstruction (MBIR) for cone-beam CT (CBCT) offers better noise-resolution tradeoff and image quality than analytical methods for acquisition protocols with low x-ray dose or limited data, but with increased computational burden that poses a drawback to routine application in clinical scenarios. This work develops a comprehensive framework for acceleration of MBIR in the form of penalized weighted least squares (PWLS) optimized with ordered subsets separable quadratic surrogates. The optimization was scheduled on a set of stages forming a morphological pyramid varying in voxel size. Transition between stages was controlled with a convergence criterion based on the deviation between the mid-band noise power spectrum (NPS) measured on a homogeneous region of the evolving reconstruction and that expected for the converged image, computed with an analytical model that used projection data and the reconstruction parameters. An stochastic backprojector (SBP) was developed by introducing a random perturbation to the sampling position of each voxel for each ray in the reconstruction within a voxel-based backprojector, breaking the deterministic pattern of sampling artifacts when combined with an unmatched Siddon forward projector. This fast, forward and backprojector pair were included into a multi-resolution reconstruction strategy to provide support for objects partially outside of the field of view. Acceleration from ordered subsets was combined with momentum accumulation stabilized with an adaptive technique that automatically resets the accumulated momentum when it diverges noticeably from the current iteration update. The framework was evaluated with CBCT data of a realistic abdomen phantom acquired on an imaging x-ray bench and with clinical CBCT data from an angiography robotic C-arm (Artis Zeego, Siemens Healthineers, Forchheim, Germany) acquired during a liver embolization procedure. Image fidelity was assessed with the structural similarity index (SSIM) computed with a converged reconstruction. The accelerated framework provided accurate reconstructions in 60 s (SSIM = 0.97) and as little as 27 s (SSIM = 0.94) for soft-tissue evaluation. The use of simple forward and backprojectors resulted in 9.3x acceleration. Accumulation of momentum provided extra ~3.5x acceleration with stable convergence for 6 to 30 subsets. The NPS-driven morphological pyramid resulted in initial faster convergence, achieving similar SSIM with 1.5x lower runtime than the single-stage optimization. Acceleration of MBIR to provide reconstruction time compatible with clinical applications is feasible via architectures that integrate algorithmic acceleration with approaches to provide stable convergence, and optimization schedules that maximize convergence speed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Han2021,
title = {Fracture reduction planning and guidance in orthopaedic trauma surgery via multi-body image registration},
author = {Runze Han and Ali Uneria and Rohan Vijayana Pengwei Wu and Prasad Vagdargi and Niral Sheth and Sebastian Vogt and Gerhard Kleinszig and Greg Michael Osgood and Jeffrey H. Siewerdsen},
url = {https://doi.org/10.1016/j.media.2020.101917},
doi = {10.1016/j.media.2020.101917},
isbn = {1361-8415},
year = {2020},
date = {2020-11-30},
journal = {Medical Image Analysis},
volume = {68},
number = {101917},
abstract = {Purposes

Surgical reduction of pelvic fracture is a challenging procedure, and accurate restoration of natural morphology is essential to obtaining positive functional outcome. The procedure often requires extensive preoperative planning, long fluoroscopic exposure time, and trial-and-error to achieve accurate reduction. We report a multi-body registration framework for reduction planning using preoperative CT and intraoperative guidance using routine 2D fluoroscopy that could help address such challenges.
Method

The framework starts with semi-automatic segmentation of fractured bone fragments in preoperative CT using continuous max-flow. For reduction planning, a multi-to-one registration is performed to register bone fragments to an adaptive template that adjusts to patient-specific bone shapes and poses. The framework further registers bone fragments to intraoperative fluoroscopy to provide 2D fluoroscopy guidance and/or 3D navigation relative to the reduction plan. The framework was investigated in three studies: (1) a simulation study of 40 CT images simulating three fracture categories (unilateral two-body, unilateral three-body, and bilateral two-body); (2) a proof-of-concept cadaver study to mimic clinical scenario; and (3) a retrospective clinical study investigating feasibility in three cases of increasing severity and accuracy requirement.
Results

Segmentation of simulated pelvic fracture demonstrated Dice coefficient of

. Reduction planning using the adaptive template achieved 2-3 mm and 2-3° error for the three fracture categories, significantly better than planning based on mirroring of contralateral anatomy. 3D-2D registration yielded ~2 mm and 0.5° accuracy, providing accurate guidance with respect to the preoperative reduction plan. The cadaver study and retrospective clinical study demonstrated comparable accuracy: ~0.90 Dice coefficient in segmentation, ~3 mm accuracy in reduction planning, and ~2 mm accuracy in 3D-2D registration.
Conclusion

The registration framework demonstrated planning and guidance accuracy within clinical requirements in both simulation and clinical feasibility studies for a broad range of fracture-dislocation patterns. Using routinely acquired preoperative CT and intraoperative fluoroscopy, the framework could improve the accuracy of pelvic fracture reduction, reduce radiation dose, and could integrate well with common clinical workflow without the need for additional navigation systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}