Johns Hopkins University
Sophia A. Doerr, Ali Uneri, Yixuan Huang, Craig K. Jones, Xiaoxuan Zhang, Michael Ketcha, Patrick A. Helm, Jeffrey H. Siewerdsen
SPIE Medical Imaging, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, Houston, Texas, United States, 11315 (8), 2020.
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.
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.
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.
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Runze Han, Ali Uneri, Pengwei Wu, Rohan Vijayan, Prasad Vagdargi, Michael Ketcha, Niral Sheth, Sebastian Vogt, Gerhard Kleinszig; Greg M. Osgood, Jeffrey H. Siewerdsen
SPIE Medical Imaging, 2020, Houston, Texas, United States, 11315 , 2020.
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.
Rohan Vijayan, Runze Han, Pengwei Wu, Niral Sheth, Michael Ketcha, Prasad Vagdargi, Sebastian Vogt, Gerhard Kleinszig, Greg M. Osgood, Ali Uneri, Jeffrey H. Siewerdsen
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; , 11315 , 2020.
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.
Chumin Zhao, Christoph Luckner, Magdalena Herbst, Sebastian Vogt, Ludwig Ritschl, Steffen Kappler, Wojtek Zbijewski, Jeffrey H. Siewerdsen
SPIE Medical Imaging, 2020 Medical Imaging 2020: Physics of Medical Imaging, Houston, Texas, United States, 11315 , 2020.
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.
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.
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.
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.
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.
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
Physics of Medical Imaging, Houston, Texas, United States, 11312 , 2020.
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.
Grace J. Gang, J. Webster Stayman, Jeffrey H. Siewerdsen
Physics of Medical Imaging , Houston, Texas, United States, 11312 , 2020.
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.
Pengwei Wu, Alejandro Sisniega, J. Webster Stayman, Wojtek Zbijewski, David Foos, Xiaohui Wang, Nishanth Khanna, Nafi Aygun, Robert E. Stevens, Jeffrey H. Siewerdsen
In: Medical Physics, 47 (6), pp. 2392-2407, 2020.
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.
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.
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.
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.
Prasad Vagdargi, Ali Uneri, Niral Sheth, Alejandro Sisniega, Tharindu De Silva, Greg M. Osgood, Jeffrey H. Siewerdsen
SPIE Medical Imaging, 2020 Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling;, Houston, Texas, United States, 11315 , 2020.
Pelvic trauma surgical procedures rely heavily on guidance with 2D fluoroscopy views for navigation in complex bone corridors. This “fluoro-hunting” paradigm results in extended radiation exposure and possible suboptimal guidewire placement from limited visualization of the fractures site with overlapped anatomy in 2D fluoroscopy. A novel computer visionbased navigation system for freehand guidewire insertion is proposed. The navigation framework is compatible with the rapid workflow in trauma surgery and bridges the gap between intraoperative fluoroscopy and preoperative CT images. The system uses a drill-mounted camera to detect and track poses of simple multimodality (optical/radiographic) markers for registration of the drill axis to fluoroscopy and, in turn, to CT. Surgical navigation is achieved with real-time display of the drill axis position on fluoroscopy views and, optionally, in 3D on the preoperative CT. The camera was corrected for lens distortion effects and calibrated for 3D pose estimation. Custom marker jigs were constructed to calibrate the drill axis and tooltip with respect to the camera frame. A testing platform for evaluation of the navigation system was developed, including a robotic arm for precise, repeatable, placement of the drill. Experiments were conducted for hand-eye calibration between the drill-mounted camera and the robot using the Park and Martin solver. Experiments using checkerboard calibration demonstrated subpixel accuracy [−0.01 ± 0.23 px] for camera distortion correction. The drill axis was calibrated using a cylindrical model and demonstrated sub-mm accuracy [0.14 ± 0.70 mm] and sub-degree angular deviation.
Amalie Shi, Shalini Subramanian, Qian Cao, Shadpour Demehri, Wotek Zbijewski, Jeffrey H. Siewerdsen
SPIE MEDICAL IMAGING, Houston, Texas, United States, 11317 , 2020.
Purpose: To evaluate the performance of a novel ultra-high resolution multi-detector CT scanner (Canon Aquilion Precision UHR CT), capable of visualizing ~150 μm details, in quantitative assessment of bone microarchitecture. Compared to conventional CT, the spatial resolution of UHR CT begins to approach the size of the trabeculae. This might enable measurements of microstructural correlates of osteoporosis, osteoarthritis, and other bone disease. Methods: The UHR CT system features a 160-row x-ray detector with 250x250 μm pixels (measured at isocenter) and a custom-designed x-ray source with a 0.4x0.5 mm focal spot. Visualization of high contrast details down to ~150 μm has been achieved on this device, which is now commercially available for clinical use. To evaluate the performance of UHR CT in quantification of bone microstructure, we imaged a variety of human bone samples (including ulna, hamate, radius, and vertebrae) embedded in a ~16 cm diameter plastic cylinder and in an anthropomorphic thorax phantom (QRM-Thorax, QRM Gmbh). Helical UHR CT acquisitions (120 kVp tube voltage) were acquired at scan exposures of 375 mAs - 5 mAs. For comparison, the samples were also imaged using a Normal Resolution (NR) mode available on the scanner, involving 500 μm slice thickness, exposure of 50 mAs, and a focal spot of 0.6x1.3 mm. We obtained micro-CT (μCT) of the bone samples at ~28 μm voxel size as a gold-standard reference. Geometric measurements of bone microstructure were performed in 17 regions-of-interests (ROIs) distributed throughout the bones of the phantoms; image registration was used to place the ROIs at corresponding locations in the UHR CT and NR CT. Trabecular thickness Tb.Th, spacing Tb.Sp, and Bone Volume fraction BvTv were obtained. The UHR and NR imaging protocols were compared terms of correlations to μCT and error of trabecular measurements. The effect of dose on trabecular morphometry was also studied for the UHR CT. Furthermore, we evaluated the sensitivity of texture features of trabecular bone (recently proposed as an alternative to geometric indices of microstructure) to imaging protocol. Image texture evaluation was performed using ~150 regions of interest (ROIs) across all bone samples. Three-dimensional Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) features were extracted for each ROI. We analyzed correlation and concordance correlation coefficient (CCC) of the mean ROI values of texture features obtained using the UHR and NR modes. Results: UHR CT reconstructions of bone samples clearly demonstrated improved visualization of the trabeculae compared to NR CT. UHR CT achieved substantially better correlations for all three metrics of bone microstructure, in particular for BvTv (correlation coefficient of 0.91 for UHR CT compared to 0.84 for NR CT) and TbSp (correlation of 0.74 for UHR CT and 0.047 for NR CT). The error obtained with UHR CT was generally smaller than that of NR CT. For TbSp, the mean deviation from CT (averaged across all bone samples) was only ~0.07 for UHR CT, compared to 0.25 for NR CT. Analysis of reproducibility of texture features of trabecular bone between UHR CT and NR CT revealed fair correlations (<0.7) for the majority of GLCM features, but relatively poor CCC (e.g. 0.02 for Energy and 0.04 for Entropy). The magnitude of texture metrics is particularly affected by the enhanced spatial resolution of UHR CT. Conclusion: The recently introduced UHR CT achieves improved correlation and reduced error in measurements of trabecular bone microstructure compared to conventional resolution CT. Future development of diagnostic strategies based on textural biomarkers derived from UHR CT will need to account for potential sensitivity of texture features to image resolution.
Niral Sheth, Tharindu De Silva, Ali Uneri, Michael Ketcha, Runze Han, Rohan Vijayan, Greg M Osgood, Jeffrey H. Siewerdsen
In: Medical Physics, 47 (3), pp. 958-974, 2020.
Purpose: To characterize the radiation dose and three-dimensional (3D) imaging performance of a recently developed mobile, isocentric C-arm equipped with a flat-panel detector (FPD) for intraoperative cone-beam computed tomography (CBCT) (Cios Spin 3D, Siemens Healthineers) and to identify potential improvements in 3D imaging protocols for pertinent imaging tasks.
Methods: The C-arm features a 30 × 30 cm2 FPD and isocentric gantry with computer-controlled motorization of rotation (0-195°), angulation (±220°), and height (0-45 cm). Geometric calibration was assessed in terms of 9 degrees of freedom of the x-ray source and detector in CBCT scans, and the reproducibility of geometric calibration was evaluated. Standard and custom scan protocols were evaluated, with variation in the number of projections (100-400) and mAs per view (0.05-1.65 mAs). Image reconstruction was based on 3D filtered backprojection using "smooth," "normal," and "sharp" reconstruction filters as well as a custom, two-dimensional 2D isotropic filter. Imaging performance was evaluated in terms of uniformity, gray value correspondence with Hounsfield units (HU), contrast, noise (noise-power spectrum, NPS), spatial resolution (modulation transfer function, MTF), and noise-equivalent quanta (NEQ). Performance tradeoffs among protocols were visualized in anthropomorphic phantoms for various anatomical sites and imaging tasks.
Results: Geometric calibration showed a high degree of reproducibility despite ~19 mm gantry flex over a nominal semicircular orbit. The dose for a CBCT scan varied from ~0.8-4.7 mGy for head protocols to ~6-38 mGy for body protocols. The MTF was consistent with sub-mm spatial resolution, with f10 (frequency at which MTF = 10%) equal to 0.64 mm-1 , 1.0 mm-1 , and 1.5 mm-1 for smooth, standard, and sharp filters respectively. Implementation of a custom 2D isotropic filter improved CNR ~ 50-60% for both head and body protocols and provided more isotropic resolution and noise characteristics. The NPS and NEQ quantified the 3D noise performance and provided a guide to protocol selection, confirmed in images of anthropomorphic phantoms. Alternative scan protocols were identified according to body site and task - for example, lower-dose body protocols (<3 mGy) sufficient for visualization of bone structures.
Conclusion: The studies provided objective assessment of the dose and 3D imaging performance of a new C-arm, offering an important basis for clinical deployment and a benchmark for quality assurance. Modifications to standard 3D imaging protocols were identified that may improve performance or reduce radiation dose for pertinent imaging tasks.
Stephen Liu, Qian Cao, Greg Osgood, J. Webster Stayman, Wojtek Zbijewski, Jeffrey H. Siewerdsen
SPIE MEDICAL IMAGING, Houston, Texas, United States, 11317 , 2020.
Purpose: We investigate an application of multisource extremity Cone-Beam CT (CBCT) with capability of weight-bearing tomographic imaging to obtain quantitative measurements of load-induced deformation of metal internal fixation hardware (e.g. tibial plate). Such measurements are desirable to improve the detection of delayed fusion or non-union of fractures, potentially facilitating earlier return to weight-bearing activities. Methods: To measure the deformation, we perform a deformable 3D-2D registration of a prior model of the implant to its CBCT projections under load-bearing. This Known-Component Registration (KC-Reg) framework avoids potential errors that emerge when the deformation is estimated directly from 3D reconstructions with metal artifacts. The 3D-2D registration involves a free-form deformable (FFD) point cloud model of the implant and a 3D cubic B-spline representation of the deformation. Gradient correlation is used as the optimization metric for the registration. The proposed approach was tested in experimental studies on the extremity CBCT system. A custom jig was designed to apply controlled axial loads to a fracture model, emulating weight-bearing imaging scenarios. Performance evaluation involved a Sawbone tibia phantom with an ~4 mm fracture gap. The model was fixed with a locking plate and imaged under five loading conditions. To investigate performance in the presence of confounding background gradients, additional experiments were performed with a pre-deformed femoral plate placed in a water bath with Ca bone mineral density inserts. Errors were measured using eight reference BBs for the tibial plate, and surface point distances for the femoral plate, where a prior model of deformed implant was available for comparison. Results: Both in the loaded tibial plate case and for the femoral plate with confounding background gradients, the proposed KC-Reg framework estimated implant deformations with errors of <0.2 mm for the majority of the investigated deformation magnitudes (error range 0.14 - 0.25 mm). The accuracy was comparable between 3D-2D registrations performed from 12 x-ray views and registrations obtained from as few as 3 views. This was likely enabled by the unique three-source x-ray unit on the extremity CBCT scanner, which implements two off-central-plane focal spots that provided oblique views of the field-of-view to aid implant pose estimation. Conclusion: Accurate measurements of fracture hardware deformations under physiological weight-bearing are feasible using an extremity CBCT scanner and FFD 3D-2D registration. The resulting deformed implant models can be incorporated into tomographic reconstructions to reduce metal artifacts and improve quantification of the mineral content of fracture callus in CBCT volumes.
Tharindu S. De Silva, Satyanarayana S. Vedula, Alexander Perdomo-Pantoja, Rohan C. Vijayan, Sophia A. Doerr, Ali Uneri, Runze Han, Michael D. Ketcha, Richard L. Skolasky, Timothy Witham, Nicholas Theodore, Jeffrey H. Siewerdsen
In: Journal of Medical Imaging , 7 (3), pp. 15, 2020.
Purpose: Data-intensive modeling could provide insight on the broad variability in outcomes in spine surgery. Previous studies were limited to analysis of demographic and clinical characteristics. We report an analytic framework called “SpineCloud” that incorporates quantitative features extracted from perioperative images to predict spine surgery outcome.
Approach: A retrospective study was conducted in which patient demographics, imaging, and outcome data were collected. Image features were automatically computed from perioperative CT. Postoperative 3- and 12-month functional and pain outcomes were analyzed in terms of improvement relative to the preoperative state. A boosted decision tree classifier was trained to predict outcome using demographic and image features as predictor variables. Predictions were computed based on SpineCloud and conventional demographic models, and features associated with poor outcome were identified from weighting terms evident in the boosted tree.
Results: Neither approach was predictive of 3- or 12-month outcomes based on preoperative data alone in the current, preliminary study. However, SpineCloud predictions incorporating image features obtained during and immediately following surgery (i.e., intraoperative and immediate postoperative images) exhibited significant improvement in area under the receiver operating characteristic (AUC): AUC = 0.72 (CI95 = 0.59 to 0.83) at 3 months and AUC = 0.69 (CI95 = 0.55 to 0.82) at 12 months.
Conclusions: Predictive modeling of lumbar spine surgery outcomes was improved by incorporation of image-based features compared to analysis based on conventional demographic data. The SpineCloud framework could improve understanding of factors underlying outcome variability and warrants further investigation and validation in a larger patient cohort.
Godwin O. Abiola, Niral M. Sheth, Wojciech Zbijewski, Matthew W. Jacobson, Christopher Bailey, John Filtes, Gerhard Kleinszig, Sebastian K. Vogt, Stefan Söllradl, Jens Bialkowski, William S. Anderson, Clifford R. Weiss, Jeffrey H. Siewerdsen
In: JOURNAL OF MEDICAL IMAGING, 7 (1), pp. 15 PAGES, 2020.
We assessed interventional radiologists’ task-based image quality preferences for two- and three-dimensional images obtained with a complementary metal–oxide semiconductor (CMOS) flat-panel detector versus a hydrogenated amorphous silicon (a-Si:H) flat-panel detector. CMOS and a-Si:H detectors were implemented on identical mobile C-arms to acquire radiographic, fluoroscopic, and cone-beam computed tomography (CBCT) images of cadavers undergoing simulated interventional procedures using low- and high-dose settings. Images from both systems were displayed side by side on calibrated, diagnostic-quality displays, and three interventional radiologists evaluated task performance relevant to each image and ranked their preferences based on visibility of pertinent anatomy and interventional devices. Overall, CMOS images were preferred in fluoroscopy (p = 0.002) and CBCT (p = 0.004), at low-dose settings (p = 0.001), and for tasks associated with high levels of spatial resolution [e.g., fine anatomical details (p = 0.006) and assessment of interventional devices (p = 0.015)]. No significant difference was found for fluoroscopic imaging tasks emphasizing temporal resolution (p = 0.072), for radiography tasks (p = 0.825), when using high-dose settings (p = 0.360), or tasks involving general anatomy (p = 0.174). The image quality preferences are consistent with reported technical advantages of CMOS regarding finer pixel size and reduced electronic noise.
Sebastian Schafer, Jeffrey H. Siewerdsen
In: Zhou, S. Kevin; Rueckert, Daniel; Fichtinger, Gabor (Ed.): Chapter 26, pp. 625-671, Academic Press, Handbook of Medical Image Computing and Computer Assisted Intervention, 2020, ISBN: 978-0-12-816176-0.
The role of X-ray imaging in interventional procedures expanded considerably over the first two decades of the 21st century. New imaging capabilities have been driven by clinical needs in a broad scope of emerging therapeutic techniques, including minimally invasive surgical approaches, an expanding spectrum of therapies delivered in interventional radiology, and the need for increasing levels of geometric accuracy in disease targeting and normal tissue avoidance. Such capabilities have been enabled by advances in X-ray tube and detector technology, advanced image processing and 3D reconstruction algorithms, and a diversity of mobile and fixed-room imaging platforms. This chapter provides a brief overview of the state-of-the-art and future directions in each of these areas, including hardware and algorithm components of the 2D and 3D imaging chain (e.g., flat-panel X-ray detectors, FPDs, and optimization-based image reconstruction, OBIR), a survey of interventional imaging platforms (e.g., C-arms, U-arms, and O-arms), and a review of clinical applications in interventional radiology, cardiology, and surgery.
Gina L. Adrales, William S. Anderson, John P. Carey, Pete X. Creighton, Sandra R. DiBrito, Deepa Galaiya, Michael R. Marohn, Todd R. McNutt, Greg M. Osgood, Nicholas. Theodore, Clifford R. Weiss, Akila N. Viswanathan, Jeffery H. Siewerdsen.
In: International Journal of Computer Assisted Radiology and Surgery, 15 (1), pp. 1-14, 2020.
A strong foundation in the fundamental principles of medical intervention combined with genuine exposure to real clinical systems and procedures will improve engineering students’ capability for informed innovation on clinical problems. To help build such a foundation, a new course (dubbed Surgineering) was developed to convey fundamental principles of surgery, interventional radiology (IR), and radiation therapy, with an emphasis on experiential learning, hands-on with real clinical systems, exposure to clinicians, and visits to real operating theaters. The concept, structure, and outcomes of the course of the first run of the first semester of the course are described.
The course included six segments spanning fundamental concepts and cutting-edge approaches in a spectrum of surgical specialties, body and neurological IR, and radiation therapy. Each class involved a minimum of didactic content and an emphasis on hands-on experience with instrumentation, equipment, surgical approaches, anatomical models, dissection, and visits to clinical theaters. Outcomes on the quality of the course and areas for continuing improvement were assessed by student surveys (5-point Likert scores and word-cloud representations of free response) as well as feedback from clinical collaborators.
Surveys assessed four key areas of feedback on the course and were analyzed quantitatively and in word-cloud representations of: (1) best aspects (hands-on experience with surgeons); (2) worst aspects (quizzes and reading materials); (3) areas for improvement (projects, quizzes, and background on anatomy); and (4) what prospective students should know (a lot background reading for every class). Five-point Likert scores from survey respondents (16/19 students) indicated: overall quality of the course 4.63 ± 0.72 (median 5.00); instructor teaching effectiveness 4.06 ± 1.06 (median 4.00); intellectual challenge 4.19 ± 0.40 (median 4.00); and workload somewhat heavier (62.5%) compared to other courses. Novel elements of the course included the opportunity to engage with clinical faculty and participate in realistic laboratory exercises, work with clinical instruments and equipment, and visit real operating theaters. An additional measure of the success of the course was evidenced by surveys and a strong escalation in enrollment in the following year.
The Surgineering course presents an important addition to upper-level engineering curricula and a valuable opportunity for engineering students to gain hands-on experience and interaction with clinical experts. Close partnership with clinical faculty was essential to the schedule and logistics of the course as well as to the continuity of concepts delivered over the semester. The knowledge and experience gained provides stronger foundation for identification of un-met clinical needs and ideation of new engineering approaches in medicine. The course also provides a valuable prerequisite to higher-level coursework in systems engineering, human factors, and data science applied to medicine.
Niral M. Sheth, Tharindu De Silva, Ali Uneri, Michael Ketcha, Runze Han, Rohan Vijayan, Greg M. Osgood, Jeffery H. Siewerdsen
In: imaging performance. Med. Phys, 2019.
To characterize the radiation dose and three‐dimensional (3D) imaging performance of a recently developed mobile, isocentric C‐arm equipped with a flat‐panel detector (FPD) for intraoperative cone‐beam computed tomography (CBCT) (Cios Spin 3D, Siemens Healthineers) and to identify potential improvements in 3D imaging protocols for pertinent imaging tasks.
The C‐arm features a 30 × 30 cm2 FPD and isocentric gantry with computer‐controlled motorization of rotation (0–195°), angulation (±220°), and height (0–45 cm). Geometric calibration was assessed in terms of 9 degrees of freedom of the x‐ray source and detector in CBCT scans, and the reproducibility of geometric calibration was evaluated. Standard and custom scan protocols were evaluated, with variation in the number of projections (100–400) and mAs per view (0.05–1.65 mAs). Image reconstruction was based on 3D filtered backprojection using “smooth,” “normal,” and “sharp” reconstruction filters as well as a custom, two‐dimensional 2D isotropic filter. Imaging performance was evaluated in terms of uniformity, gray value correspondence with Hounsfield units (HU), contrast, noise (noise‐power spectrum, NPS), spatial resolution (modulation transfer function, MTF), and noise‐equivalent quanta (NEQ). Performance tradeoffs among protocols were visualized in anthropomorphic phantoms for various anatomical sites and imaging tasks.
Geometric calibration showed a high degree of reproducibility despite ~19 mm gantry flex over a nominal semicircular orbit. The dose for a CBCT scan varied from ~0.8–4.7 mGy for head protocols to ~6–38 mGy for body protocols. The MTF was consistent with sub‐mm spatial resolution, with f10 (frequency at which MTF = 10%) equal to 0.64 mm−1, 1.0 mm−1, and 1.5 mm−1 for smooth, standard, and sharp filters respectively. Implementation of a custom 2D isotropic filter improved CNR ~ 50–60% for both head and body protocols and provided more isotropic resolution and noise characteristics. The NPS and NEQ quantified the 3D noise performance and provided a guide to protocol selection, confirmed in images of anthropomorphic phantoms. Alternative scan protocols were identified according to body site and task — for example, lower‐dose body protocols (<3 mGy) sufficient for visualization of bone structures.
The studies provided objective assessment of the dose and 3D imaging performance of a new C‐arm, offering an important basis for clinical deployment and a benchmark for quality assurance. Modifications to standard 3D imaging protocols were identified that may improve performance or reduce radiation dose for pertinent imaging tasks.
Michael D. Ketch, Tharindu S. De Silva, Runze Han, Ali Uneri, Sebastian Vogt, Gerhard Kleinszig, Jeffrey H. Siewerdsen
In: J. of Medical Imaging, 6 (4), pp. 10, 2019.
Convolutional neural networks (CNNs) offer a promising means to achieve fast deformable image registration with accuracy comparable to conventional, physics-based methods. A persistent question with CNN methods, however, is whether they will be able to generalize to data outside of the training set. We investigated this question of mismatch between train and test data with respect to first- and second-order image statistics (e.g., spatial resolution, image noise, and power spectrum). A UNet-based architecture was built and trained on simulated CT images for various conditions of image noise (dose), spatial resolution, and deformation magnitude. Target registration error was measured as a function of the difference in statistical properties between the test and training data. Generally, registration error is minimized when the training data exactly match the statistics of the test data; however, networks trained with data exhibiting a diversity in statistical characteristics generalized well across the range of statistical conditions considered. Furthermore, networks trained on simulated image content with first- and second-order statistics selected to match that of real anatomical data were shown to provide reasonable registration performance on real anatomical content, offering potential new means for data augmentation. Characterizing the behavior of a CNN in the presence of statistical mismatch is an important step in understanding how these networks behave when deployed on new, unobserved data. Such characterization can inform decisions on whether retraining is necessary and can guide the data collection and/or augmentation process for training.