Dr. Pengwei Wu Profile

Dr. Pengwei Wu

Graduate Student at Johns Hopkins Univ

SPIE Involvement:

Author

Publications (21)

SPIE Journal Paper | 7 June 2023

Cone-beam CT sampling incompleteness: analytical and empirical studies of emerging systems and source–detector orbits

Pengwei Wu, Aina Tersol, Rolf Clackdoyle, John Boone, Jeffrey Siewerdsen

JMI, Vol. 10, Issue 03, 033503, (June 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.3.033503

KEYWORDS: Cone beam computed tomography, X-ray sources, Simulations, Optical spheres, Voxels, 3D image reconstruction, Modulation, Image quality, X-ray computed tomography, Imaging systems

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PurposeMotivated by emerging cone-beam computed tomography (CBCT) systems and scan orbits, we aim to quantitatively assess the completeness of data for 3D image reconstruction—in turn, related to “cone-beam artifacts.” Fundamental principles of cone-beam sampling incompleteness are considered with respect to an analytical figure-of-merit [FOM, denoted tan ( ψmin ) ] and related to an empirical FOM (denoted zmod) for measurement of cone-beam artifact magnitude in a test phantom.ApproachA previously proposed analytical FOM [tan ( ψmin ) , defined as the minimum angle between a point in the 3D image reconstruction and the x-ray source over the scan orbit] was analyzed for a variety of CBCT geometries. A physical test phantom was configured with parallel disk pairs (perpendicular to the z-axis) at various locations throughout the field of view, quantifying cone-beam artifact magnitude in terms of zmod (the relative signal modulation between the disks). Two CBCT systems were considered: an interventional C-arm (Cios Spin 3D; Siemens Healthineers, Forcheim Germany) and a musculoskeletal extremity scanner; Onsight3D, Carestream Health, Rochester, United States)]. Simulations and physical experiments were conducted for various source–detector orbits: (a) a conventional 360 deg circular orbit, (b) tilted and untilted semi-circular (196 deg) orbits, (c) multi-source (three x-ray sources distributed along the z axis) semi-circular orbits, and (d) a non-circular (sine-on-sphere, SoS) orbit. The incompleteness of sampling [tan ( ψmin ) ] and magnitude of cone-beam artifacts (zmod) were evaluated for each system and orbit.ResultsThe results show visually and quantitatively the effect of system geometry and scan orbit on cone-beam sampling effects, demonstrating the relationship between analytical tan ( ψmin ) and empirical zmod. Advanced source–detector orbits (e.g., three-source and SoS orbits) exhibited superior sampling completeness as quantified by both the analytical and the empirical FOMs. The test phantom and zmod metric were sensitive to variations in CBCT system geometry and scan orbit and provided a surrogate measure of underlying sampling completeness.ConclusionFor a given system geometry and source–detector orbit, cone-beam sampling completeness can be quantified analytically (in terms arising from Tuy’s condition) and/or empirically (using a test phantom for quantification of cone-beam artifacts). Such analysis provides theoretical and practical insight on sampling effects and the completeness of data for emerging CBCT systems and scan trajectories.

Proceedings Article | 3 April 2023 Presentation + Paper

Real-time 3D neuroendoscopic guidance using SLAM: first clinical studies

P. Vagdargi, A. Uneri, C. Jones, X. Zhang, P. Wu, R. Han, A. Sisniega, J. Lee, P. Helm, M. Luciano, W. Anderson, G. Hager, J. Siewerdsen

Proceedings Volume 12466, 1246604 (2023) https://doi.org/10.1117/12.2654595

KEYWORDS: Calibration, Distortion, Deformation, Video, Cameras, Point clouds, Endoscopy, Visualization, Neurosurgery, Vision based navigation, Navigation systems

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Neurosurgical techniques often require accurate targeting of deep-brain structures even in the presence of deformation due intervention and egress of Cerebrospinal Fluid (CSF) during surgical access. Prior work reported Simultaneous Localization and Mapping (SLAM) methods for endoscopic guidance using 3D reconstruction. In this work, methods for correcting the geometric distortion of a neuroendoscope are reported in a form that have been translated intraoperative use in first clinical studies. Furthermore, SLAM methods are evaluated in first clinical studies for real-time 3D endoscopic navigation with near real-time registration in the presence of deep-brain tissue deformation. A custom calibration jig with swivel mounts was designed and manufactured for neuroendoscope calibration in the operating room. The process is potentially suitable to intraoperative use while maintaining sterility of the endoscope, although the current calibration system was used in the Operating Room (OR) immediately following the case for offline analysis. A six by seven checkerboard pattern was used to obtain corner locations for calibration, and the method was evaluated in terms of Reprojection Error (RPE). Neuroendoscopic video was acquired under an IRB-approved clinical study, demonstrating rich vascular features and other structures on the interior walls of the lateral ventricles for 3D point-cloud reconstruction. Geometric accuracy was evaluated in terms of Projected Error (PE) on a ground truth surface defined from MR or cone-beam CT (CBCT) images. Intraoperative neuroendoscope calibration was achieved with sub-pixel [0.61 ± 0.20 px] error. The calibration yielded a focal length of 816.42 px and 822.71 px in X and Y directions respectively, along with radial distortion coefficients of -0.432 (first order term [𝑘1]) and 0.158 (second order term [𝑘2]). The 3D reconstruction was performed successfully with a PE of 0.23 ± 0.15 mm compared to the ground truth surface. The system for neuroendoscopic guidance based on SLAM 3D point-cloud reconstruction provided a promising platform for the development of 3D neuroendoscopy. The studies reported in this work presented an important means of neuroendoscope calibration in the OR and provided preliminary evidence for accurate 3D video reconstruction in first clinical studies. Future work aims to further extend the clinical evaluation and improve reconstruction accuracy using ventricular shape priors.

Proceedings Article | 18 October 2022 Open Access

DL-Recon: combining 3D deep learning image synthesis and model uncertainty with physics-based image reconstruction

Xiaoxuan Zhang, Pengwei Wu, Wojciech Zbijewski, Alejandro Sisniega, Runze Han, Craig Jones, Prasad Vagdargi, Ali Uneri, Patrick Helm, William Anderson, Jeffrey Siewerdsen

Proceedings Volume 12304, 1230408 (2022) https://doi.org/10.1117/12.2646383

KEYWORDS: 3D modeling, Brain, Computed tomography, 3D image reconstruction, 3D image processing, Gallium nitride, Image quality, Neuroimaging, Image restoration, Image analysis

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High-precision image-guided neurosurgery – especially in the presence of brain shift – would benefit from intraoperative image quality beyond the conventional contrast-resolution limits of cone-beam CT (CBCT) for visualization of the brain parenchyma, ventricles, and intracranial hemorrhage. Deep neural networks for 3D image reconstruction offer a promising basis for noise and artifact reduction, but generalizability can be challenged in scenarios involving features previously unseen in training data. We propose a 3D deep learning reconstruction framework (termed “DL-Recon”) that integrates learning-based image synthesis with physics-based reconstruction to leverage strengths of each. A 3D conditional GAN was developed to generate synthesized CT from CBCT images. Uncertainty in the synthesis image was estimated in a spatially varying, voxel-wise manner via Monte-Carlo dropout and was shown to correlate with abnormalities or pathology not present in training data. The DL-Recon approach improves the fidelity of the resulting image by combining the synthesized image (“DL-Synthesis”) with physics-based reconstruction (filtered back-projection (FBP) or other approaches) in a manner weighted by uncertainty – i.e., drawing more from the physics-based method in regions where model uncertainty is high. The performance of image synthesis, uncertainty estimation, and DL-Recon was investigated for the first time in real CBCT images of the brain. Variable input to the synthesis network was tested – including uncorrected FBP and precorrection with a simple (constant) scatter estimate – hypothesizing the latter to improve synthesis performance. The resulting uncertainty estimation was evaluated for the first time in real anatomical features not included in training (abnormalities and brain shift). The performance of DL-Recon was evaluated in terms of image uniformity, noise, and soft-tissue contrast-to-noise ratio in comparison to DL-Synthesis and FBP with a comprehensive artifact correction framework. DL-Recon was found to leverage the strengths of the learning-based and physics-based reconstruction approaches, providing a high degree of image uniformity similar to DL-Synthesis while accurately preserving soft-tissue contrast as in artifact-corrected FBP.

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Proceedings Article | 4 April 2022 Paper

Sampling effects for emerging cone-beam CT systems and scan trajectories: from Tuy’s condition to system design and routine image quality tests

A. Tersol, P. Wu, R. Clackdoyle, J. Boone, J. Siewerdsen

Proceedings Volume 12031, 120313D (2022) https://doi.org/10.1117/12.2613139

KEYWORDS: Modulation, Image quality, Computed tomography, 3D image reconstruction, Calibration, Imaging systems, X-ray sources, Visualization, Image restoration, X-ray computed tomography

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Proceedings Article | 4 April 2022 Paper

Deformable registration of MRI to intraoperative cone-beam CT of the brain using a joint synthesis and registration network

R. Han, C. Jones, P. Wu, P. Vagdargi, X. Zhang, A. Uneri, J. Lee, M. Luciano, W. Anderson, P. Helm, J. Siewerdsen

Proceedings Volume 12034, 1203407 (2022) https://doi.org/10.1117/12.2611783

KEYWORDS: Image registration, Magnetic resonance imaging, Brain

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Proceedings Article | 4 April 2022 Presentation + Paper

Robot-assisted neuroendoscopy for real-time 3D guidance of transventricular approach to deep-brain targets

P. Vagdargi, A. Uneri, C. Jones, P. Wu, R. Han, M. Luciano, W. Anderson, P. Helm, G. Hager, J. Siewerdsen

Proceedings Volume 12034, 120340E (2022) https://doi.org/10.1117/12.2613231

KEYWORDS: Cameras, 3D acquisition, Endoscopy, Video, Imaging systems, Visualization, 3D modeling, Navigation systems, Image filtering, Augmented reality, Computer vision technology

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Purpose: Recent neurosurgical techniques require accurate targeting of deep-brain structures even in the presence of deformation due to egress of cerebrospinal fluid (CSF) during surgical access. Prior work reported Structure-from-Motion (SfM) based methods for endoscopic guidance using 3D reconstruction. We are developing feature detection and description methods for a real-time 3D endoscopic navigation system using simultaneous localization and mapping (SLAM) to for accurate and near real-time registration. Methods: Feature detectors and descriptors were evaluated in SLAM reconstruction in anthropomorphic phantom studies emulating neuroendoscopy. The experimental system utilized a mobile UR3e robot (Universal Robots, Denmark) and ventriculoscope (Karl Storz, Tuttlingen, Germany) affixed to the end effector as a repeatable ventriculoscopy platform. Experiments were conducted to quantify optimal feature detection parameters in scale-space. Neuroendoscopic images acquired in traversal of the lateral and third ventricles provided a rich feature space of vessels and other structures on ventricular walls supporting feature detection and 3D point-cloud reconstruction. Performance was evaluated in terms of the mean number of features detected per frame and the algorithm runtime. Results: Parameter search in scale-space for feature detection demonstrated the dependence on the mean number of features per image and the points of diminishing return in parameter selection (e.g., the number of octaves and scale levels) and tradeoffs in runtime. Nominal parameters were identified as 3 octaves and 9 scale levels, with a mean number of features detected as 492 and 806 respectively. Conclusions: The system for neuroendoscopic guidance based on SLAM 3D point-cloud reconstruction provided a promising platform for the development of robot-assisted endoscopic neurosurgery. The studies reported in this work provided an essential basis for rigorous selection of parameters for feature detection. Future work aims to further develop the SLAM framework, assess the geometric accuracy of reconstruction, and translate methods to clinical studies.

SPIE Journal Paper | 9 June 2021

Development of a fluoroscopically guided robotic assistant for instrument placement in pelvic trauma surgery

Rohan Vijayan, Runze Han, Pengwei Wu, Niral Sheth, Michael Ketcha, Prasad Vagdargi, Sebastian Vogt, Gerhard Kleinszig, Greg Osgood, Jeffrey Siewerdsen, Ali Uneri

JMI, Vol. 8, Issue 03, 035001, (June 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.3.035001

KEYWORDS: Image registration, Robotics, Calibration, Bone, Surgery, Imaging systems, Computed tomography, Solid modeling, Instrument modeling, Robotic systems

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SPIE Journal Paper | 13 March 2021 Open Access

Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomography

Michael Ketcha, Michael Marrama, Andre Souza, Ali Uneri, Pengwei Wu, Xiaoxuan Zhang, Patrick Helm, Jeffrey Siewerdsen

JMI, Vol. 8, Issue 05, 052103, (March 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.5.052103

KEYWORDS: Metals, Image segmentation, Neural networks, Signal attenuation, Data modeling, Head, 3D image processing, Computed tomography, Network architectures, 3D image reconstruction

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Proceedings Article | 25 February 2021 Presentation + Paper

Fluoroscopic guidance of a surgical robot: pre-clinical evaluation in pelvic guidewire placement

R. Vijayan, R. Han, P. Wu, N. Sheth, P. Vagdargi, S. Vogt, G. Kleinszig, G. Osgood, J. Siewerdsen, A. Uneri

Proceedings Volume 11598, 115981G (2021) https://doi.org/10.1117/12.2582188

KEYWORDS: Radiography, Computer aided design, Solid modeling, Instrument modeling, Surgery, Robotics, Robotic systems, Prototyping, Process modeling, Imaging devices

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Proceedings Article | 23 February 2021 Presentation + Paper

Robot-assisted ventriculoscopic 3D reconstruction for guidance of deep-brain stimulation surgery

P. Vagdargi, A. Uneri, C. Jones, P. Wu, R. Han, M. Luciano, W. Anderson, G. Hager, J. Siewerdsen

Proceedings Volume 11598, 1159809 (2021) https://doi.org/10.1117/12.2582173

KEYWORDS: 3D modeling, Surgery, Calibration, Robotic systems, Imaging systems, Clouds, Cameras, Video, Image restoration, Image registration

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Proceedings Article | 23 February 2021 Presentation + Paper

Deformable MR-CT image registration using an unsupervised synthesis and registration network for neuro-endoscopic surgery

R. Han, C. Jones, M. Ketcha, P. Wu, P. Vagdargi, A. Uneri, J. Lee, M. Luciano, W. Anderson, J. Siewerdsen

Proceedings Volume 11598, 1159819 (2021) https://doi.org/10.1117/12.2581567

KEYWORDS: Image registration, Endoscopy, Computed tomography, Magnetic resonance imaging, Brain, 3D image processing, Surgery, Neural networks, Computer simulations

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Purpose: Deep-brain stimulation via neuro-endoscopic surgery is a challenging procedure that requires accurate targeting of deep-brain structures that can undergo deformations (up to 10 mm). Conventional deformable registration methods have the potential to resolve such geometric error between preoperative MR and intraoperative CT but at the expense of long computation time. New advances in deep learning methods offer benefits to inter-modality image registration accuracy and runtime using novel similarity metrics and network architectures. Method: An unsupervised deformable registration network is reported that first generates a synthetic CT from MR using CycleGAN and then registers the synthetic CT to the intraoperative CT using an inverse-consistent registration network. Diffeomorphism of the registration is maintained using deformation exponentiation “squaring and scaling” layers. The method was trained and tested on a dataset of CT and T1-weighted MR images with randomly simulated deformations that mimic deep-brain deformation during surgery. The method was compared to a baseline method using inter-modality deep learning registration, VoxelMorph. Results: The methods were tested on 10 pairs of CT/MR images from 5 subjects. The proposed method achieved a Dice score of 0.84±0.04 for the lateral ventricles, 0.72±0.09 for the 3^rd ventricle, and 0.63±0.10 for the 4^th ventricle, with target registration error (TRE) of 0.95±0.54 mm. The proposed method showed statistically significant improvement in both Dice score and TRE in comparison to inter-modality VoxelMorph, while maintaining a fast runtime of less than 3 seconds for a typical MR-CT pair of volume images. Conclusion: The proposed unsupervised image synthesis and registration network demonstrates the capability for accurate volumetric deformable MR-CT registration with near real-time performance. The method will be further developed for application in intraoperative CT (or cone-beam CT) guided neurosurgery.

Proceedings Article | 23 February 2021 Presentation + Paper

Data-driven deformable 3D-2D registration for guiding neuroelectrode placement in deep brain stimulation

A. Uneri, P. Wu, C. Jones, M. Ketcha, P. Vagdargi, R. Han, P. Helm, M. Luciano, W. Anderson, J. Siewerdsen

Proceedings Volume 11598, 115981B (2021) https://doi.org/10.1117/12.2582160

KEYWORDS: Brain stimulation, 3D modeling, Image registration, Model-based design, X-rays, Radiography, Performance modeling, Neural networks, Metals, Instrument modeling

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Proceedings Article | 22 February 2021 Presentation + Paper

Cone-beam CT for neurosurgical guidance: high-fidelity artifacts correction for soft-tissue contrast resolution

P. Wu, A. Sisniega, A. Uneri, R. Han, M. Ketcha, C. Jones, P. Vagdargi, X. Zhang, M. Luciano, W. Anderson, J. Siewerdsen

Proceedings Volume 11595, 115950X (2021) https://doi.org/10.1117/12.2581686

KEYWORDS: Metals, Visualization, Quantum electronics, Image resolution, Image quality, Magnetic resonance imaging, Head, Convolutional neural networks, Bone

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Purpose: Intraoperative cone-beam CT (CBCT) plays an important role in neurosurgical guidance but is conventionally limited to high-contrast bone visualization. This work reports a high-fidelity artifacts correction pipeline to advance image quality beyond conventional limits and achieve soft-tissue contrast resolution even in the presence of multiple metal objects – specifically, a stereotactic head frame. Methods: A new metal artifact reduction (MAR) method was developed based on a convolutional neural network (CNN) that simultaneously estimates metal-induced bias and metal path length in the projection domain. To improve generalizability of the network, a physics-based method was developed to generate highly accurate simulated, metalcontaminated projection training data. The MAR method was integrated with previously proposed artifacts correction methods (lag, glare, scatter, and beam-hardening) to form a high-fidelity artifacts correction pipeline. The proposed methods were tested using an intraoperative CBCT system (O-arm, Medtronic) emulating a realistic setup in stereotactic neurosurgery, including nominal (20 cm) and extended (40 cm) field of view (FOV) protocols. Results: The physics-based data generation method provided accurate simulation of metal in projection data, including scatter, polyenergetic, quantum noise, and electronic noise effects. The artifacts correction pipeline was able to accommodate both 20 cm and 40 cm FOV protocols and demonstrated ~80% improvement in image uniformity and ~20% increase in contrast-to-noise ratio (CNR). Fully corrected images in the smaller FOV mode exhibited ~32% increase in CNR compared to the 40 cm FOV mode, showing the method’s ability to handle truncated metal objects outside the FOV. Conclusion: The image quality of intraoperative CBCT was greatly improved with the proposed artifacts correction pipeline, with clear improvement in soft-tissue contrast resolution (e.g., cerebral ventricles) even in the presence of a complex metal stereotactic frame. Such capability gives clearer visualization of structures of interest for intracranial neurosurgery, and it provides an important basis for future work aiming to deformably register preoperative MRI to intraoperative CBCT. Ongoing work includes clinical studies now underway.

Proceedings Article | 16 March 2020 Presentation + Paper

Multi-body registration for fracture reduction in orthopaedic trauma surgery

R. Han, A. Uneri, P. Wu, R. Vijayan, P. Vagdargi, M. Ketcha, N. Sheth, S. Vogt, G. Kleinszig, G. Osgood, J. Siewerdsen

Proceedings Volume 11315, 113150F (2020) https://doi.org/10.1117/12.2549708

KEYWORDS: Image segmentation, Image registration, Surgery, Statistical modeling, 3D modeling, X-ray computed tomography, Computer aided diagnosis and therapy, Optimization (mathematics), Orthopedic imaging

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Proceedings Article | 16 March 2020 Paper

Multi-slot extended view imaging on the O-Arm: image quality and application to intraoperative assessment of spinal morphology

X. Zhang, A. Uneri, P. Wu, M. Ketcha, S. Doerr, C. Jones, P. Helm, J. Siewerdsen

Proceedings Volume 11315, 113152B (2020) https://doi.org/10.1117/12.2549710

KEYWORDS: Optical spheres, Image quality, Visualization, Imaging systems, Spine, Image resolution, Collimators, Distortion, Sensors, Image visualization

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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.

Proceedings Article | 16 March 2020 Presentation + Paper

Image-guided robotic k-wire placement for orthopaedic trauma surgery

R. Vijayan, R. Han, P. Wu, N. Sheth, M. Ketcha, P. Vagdargi, S. Vogt, G. Kleinszig, G. Osgood, J. Siewerdsen, A. Uneri

Proceedings Volume 11315, 113151A (2020) https://doi.org/10.1117/12.2549713

KEYWORDS: Radiography, Robotics, Surgery, 3D modeling, Image-guided intervention, Image registration, Computed tomography

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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.

Proceedings Article | 16 March 2020 Presentation + Paper

Method for metal artifact avoidance in C-arm cone-beam CT

P. Wu, N. Sheth, A. Sisniega, A. Uneri, R. Han, R. Vijayan, P. Vagdargi, B. Kreher, H. Kunze, G. Kleinszig, S. Vogt, S.-F. Lo, N. Theodore, J. Siewerdsen

Proceedings Volume 11312, 1131226 (2020) https://doi.org/10.1117/12.2549840

KEYWORDS: Metals, Calibration, Image segmentation, Signal attenuation, Chest, Spine, Imaging systems, Surgery, Modulation transfer functions, Visualization

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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.

Proceedings Article | 28 May 2019 Paper

Clinical study of soft-tissue contrast resolution in cone-beam CT of the head using multi-resolution PWLS with multi-motion correction and an electronic noise model

P. Wu, A. Sisniega, J. Stayman, W. Zbijewski, D. Foos, X. Wang, N. Aygun, R. Stevens, J. Siewerdsen

Proceedings Volume 11072, 110720B (2019) https://doi.org/10.1117/12.2534887

KEYWORDS: Head, Computed tomography, Imaging systems, Prototyping, Visualization, Sensors, Motion models, Image quality, Injuries, Brain

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Purpose: Improving soft-tissue contrast resolution beyond the capability of current cone-beam CT (CBCT) systems is essential to a growing range of image guidance and diagnostic imaging scenarios. We present a framework for CBCT model-based image reconstruction (MBIR) combining artifact corrections with multi-resolution reconstruction and multiregion motion compensation and apply the method for the first time in a clinical study of CBCT for high-quality imaging of head injury. Methods: A CBCT prototype was developed for mobile point-of-care imaging in the neuro-critical care unit (NCCU). Projection data were processed via poly-energetic gain correction and an artifacts correction pipeline treating scatter, beam hardening, and motion compensation. The scatter correction was modified to use a penalized weighted least-squares (PWLS) image in the Monte-Carlo (MC) object model for better uniformity in truncated data. The PWLS method included: (1) multi-resolution reconstruction to mitigate lateral truncation from the head-holder; (2) multi-motion compensation allowing separate motion of the head and head-holder; and (3) modified statistical weights to account for electronics noise and fluence modulation by the bowtie filter. Imaging performance was evaluated in simulation and in the first clinical study (N = 54 patients) conducted with the system. Results: Using a PWLS object model in the final iteration of the MC scatter estimate improved image uniformity by 40.4% for truncated datasets. The multi-resolution, multi-motion PWLS method greatly reduced streak artifacts and nonuniformity both in simulation (RMSE reduced by 65.5%) and in the clinical study (visual image quality assessed by a neuroradiologist). Up to 15% reduction in variance was achieved using statistical weights modified according to a model for electronic noise from the detector. Each component was important for improved contrast resolution in the patient data. Conclusion: An integrated pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to a level supporting visualization of low-contrast brain lesions and warranting future studies of diagnostic performance in the NCCU.

Proceedings Article | 1 March 2019 Presentation + Paper

Cone-beam CT statistical reconstruction with a model for fluence modulation and electronic readout noise

P. Wu, A. Sisniega, J. W. Stayman, W. Zbijewski, D. Foos, X. Wang, N. Aygun, R. Stevens, J. Siewerdsen

Proceedings Volume 10948, 1094814 (2019) https://doi.org/10.1117/12.2513417

KEYWORDS: Modulation, Imaging systems, Head, Sensors, Modulators, Scanners, Image filtering, Statistical modeling, Signal attenuation, Prototyping

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Purpose: Cone-beam CT (CBCT) systems with a flat-panel detector (FPD) have advanced in a variety of specialty diagnostic imaging scenarios, with fluence modulation and multiple-gain detectors playing important roles in extending dynamic range and improving image quality. We present a penalized weighted least-squares (PWLS) reconstruction approach with a noise model that includes the effects of fluence modulation and electronic readout noise, and we show preliminary results that tests the concept with a CBCT head scanner prototype. Methods: Statistical weights in PWLS were modified using a realistic noise model for the FPD that considers factors such as system blur and spatially varying electronic noise in multiple-gain readout detectors (PWLS^e). A spatially varying gain term was then introduced in the calculation of statistical weights to account for the change in quantum noise due to fluence modulation (e.g. bowtie filter) (PWLS∗). The methods were tested in phantom experiments involving an elliptical phantom specially designed to stress dual-gain readout, and a water phantom and an anthropomorphic head phantom to quantify improvements in noise-resolution characteristics for the new PWLS methods (PWLS𝑒 and PWLS∗, and combined PWLS^∗e). The proposed methods were further tested using a high-quality, low-dose CBCT head scanner prototype in a clinical study involving patients with head injury. Results: Preliminary results show that the PWLS^e method demonstrated superior noise-resolution tradeoffs compared to conventional PWLS, with variance reduced by ~15-25% at matched resolution of 0.65 mm edge-spread-function (ESF) width. Clinical studies confirmed these findings, with variance reduced by ~15% in peripheral regions of the head without loss in spatial resolution, improving visual image quality in detection of peridural hemorrhage. A bowtie filter and polyenergetic gain correction improved image uniformity, and early results demonstrated that the proposed PWLS∗ method showed a ~40% reduction in variance compared to conventional PWLS when used with a bowtie filter. Conclusion: A more accurate noise model incorporated in PWLS statistical weights to account for fluence modulation and electronic readout noise reduces image noise and improves soft-tissue imaging performance in CBCT for clinical applications requiring a high degree of contrast resolution.

Proceedings Article | 9 March 2018 Presentation + Paper

Evaluation of the reconstruction-of-difference (RoD) algorithm for cone-beam CT neuro-angiography

P. Wu, J. Stayman, M. Mow, W. Zbijewski, A. Sisniega, N. Aygun, R. Stevens, D. Foos, X. Wang, J. Siewerdsen

Proceedings Volume 10573, 105731R (2018) https://doi.org/10.1117/12.2293709

KEYWORDS: Reconstruction algorithms, Head, Prototyping, Computed tomography, Angiography, Iodine, Scanners, Brain, Targeting Task Performance metric, Data modeling

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Purpose: Timely detection of neurovascular pathology such as ischemic stroke is essential to effective treatment, and systems for cone-beam CT (CBCT) could provide CT angiography (CTA) assessment in a timely manner close to the point of care. CBCT systems suffer from slow rotation speed and readout speed, which leads to inconsistent or sparse dataset. This work describes a new reconstruction method using a reconstruction of difference (RoD) approach that is robust against such factors.

Methods: Important aspects of CBCT angiography were investigated, weighting tradeoffs among the magnitude of iodine enhancement (peak contrast), the degree of data consistency, and the degree of data sparsity. Simulation studies were performed across a range of CBCT half-scan acquisition speed ranging ~3 – 17 s. Experiments were conducted using a CBCT prototype and an anthropomorphic neurovascular phantom incorporating a vessel with contrast injection with a time-attenuation (TAC) injection giving low data consistency but high peak contrast. Images were reconstructed using filtered back-projection (FBP), penalized likelihood (PL), and the RoD algorithm. Data were evaluated in terms of root mean square error (RMSE) in image enhancement as well as overall image noise and artifact.

Results: Feasibility was demonstrated for 3D angiographic assessment in CBCT images acquired across a range of data consistency and sparsity. Compared to FBP, the RoD method reduced the RMSE in reconstructed images by 50.0% in simulation studies (fixed peak contrast; variable data consistency and sparsity). The improvement in RMSE compared to PL reconstruction was 28.8%. The phantom experiments investigated conditions of low data consistency, RoD provided a 15.6% reduction in RMSE compared to FBP and a 16.3% reduction compared to PL, showing the feasibility of RoD method for slow-rotating CBCT-A system.

Conclusions: Simulations and phantom experiments show the feasibility and improved performance of the RoD approach compared to FBP and PL reconstruction, enabling 3D neuro-angiography on a slowly rotating CBCT system (e.g., 17.1s for a half-scan). The algorithm is relatively robust against data sparsity and is sensitive in detecting low levels of contrast enhancement from the baseline (mask) scan. Tradeoffs among peak contrast, data consistency, and data sparsity are demonstrated clearly in each experiment and help to guide the development of optimal contrast injection protocols for future preclinical and clinical studies.

Proceedings Article | 9 March 2018 Paper

Image quality, scatter, and dose in compact CBCT systems with flat and curved detectors

A. Sisniega, W. Zbijewski, P. Wu, J. W. Stayman, V. Koliatsos, N. Aygun, R. Stevens, X. Wang, D. Foos, J. Siewerdsen

Proceedings Volume 10573, 105734E (2018) https://doi.org/10.1117/12.2293872

KEYWORDS: X-rays, Imaging systems, Image quality, X-ray detectors, Monte Carlo methods, Signal attenuation, Head, X-ray imaging, Systems modeling

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Purpose: A number of cone-beam CT (CBCT) applications demand increasingly compact system designs for smaller footprint and improved portability. Such compact geometries can be achieved via reduction of air gap and integration of novel, curved detectors; however, the increased x-ray scatter in presents a major challenge to soft-tissue image quality in such compact arrangements. This work investigates pre-patient modulation (bowtie filters) and antiscatter grids to mitigate such effects for compact geometries with curved detectors. Methods: The effects of bowtie filters on dose and x-ray scatter were investigated in a compact geometry (180 mm air gap), for three detector curvatures: Flat, Focused at source, and Compact focused at isocenter. Experiments used bowtie filters of varying curvature combined with antiscatter grids (GR: 8:1, 80 lpmm). Scatter was estimated via GPU-accelerated Monte Carlo simulation in an anthropomorphic head phantom. Primary fluence was estimated with a polychromatic Siddon projector. Realistic Poisson noise (total dose: 20 mGy) was added to the total signal. Scatter magnitude and distribution were evaluated in projection data, and CT image quality was assessed in PWLS reconstructions. Results were validated in physical experiments on an x-ray test-bench for CBCT. Results: Moderate bowties combined with grids reduced average scatter magnitude and SPR, reduced cupping from 90 to 5 HU, and yielded net benefit to CNR despite attenuation of primary fluence. Dose to sensitive organs (eye lens) was reduced by 27%. More aggressive bowties showed further potential for dose reduction (35%) but increased peripheral SPR and increased non-uniformity and artifacts at the periphery of the image. Curved detector geometry exhibited slightly improved uniformity but a slight reduction in CNR compared to conventional flat detector geometry. Conclusion: Highly portable, soft-tissue imaging, CBCT systems with very compact geometry and curved detectors appear feasible, despite elevated x-ray scatter, through combination of moderate pre-patient collimation and antiscatter grids.

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