Mr. Niral M. Sheth Profile

Niral M. Sheth

at Johns Hopkins Univ

SPIE Involvement:

Author

Publications (10)

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

Proc. SPIE. 11312, Medical Imaging 2020: Physics of Medical Imaging

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

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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 | 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

Proc. SPIE. 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling

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

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Proceedings Article | 16 March 2020 Presentation + Paper

Calibration and registration of a freehand video-guided surgical drill for orthopaedic trauma

P. Vagdargi, A. Uneri, N. Sheth, A. Sisniega, T. De Silva, G. Osgood, J. Siewerdsen

Proc. SPIE. 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Visualization, Surgery, Imaging systems, Cameras, Calibration, Video, Distortion, Image registration, Computer vision technology, Navigation systems, Rigid registration, Fluoroscopy, Orthopedic imaging

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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

Proc. SPIE. 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling

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

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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

Pixelated columnar CsI:Tl scintillator for high resolution radiography and cone-beam CT

Stuart Miller, Bipin Singh, Matthew S. Marshall, Conner Brown, Niral Sheth, Gengxin Shi, Jeffrey Siewerdsen, Wojciech Zbijewski, Vivek Nagarkar

Proc. SPIE. 11312, Medical Imaging 2020: Physics of Medical Imaging

KEYWORDS: CMOS sensors, Sensors, Scintillators, Radiography, Image resolution, Bone, Atomic layer deposition, Spatial resolution

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Microcolumnar CsI:Tl scintillator screens have been the gold standard in X-ray imaging for many years due to their high density, high atomic number, and scintillation efficiency. The structured screens provide an improvement in performance by channeling the light to the detector, improving detection efficiency and spatial resolution. We have taken this concept a step further by laser-machining the CsI:Tl scintillator to provide pixels that match the detector pixels. This allows for still thicker CsI:Tl layers up to 700 μm pixelated with pitch of 100 μm to match CMOS flat panel pixels, thus improving X-ray absorption and resolution. We are investigating the applications of CMOS detectors with pixelated scintillators for imaging of bone microarchitecture on diagnostic Cone Beam CT (CBCT) systems to provide improved quantitative metrics for diagnosis of osteoporosis and osteoarthritis.

The scintillator design includes reflective coatings applied to the laser-cut grooves to improve optical isolation between pixels. Such coatings are created by atomic layer deposition (ALD), a unique approach, which permits formation of reflectors over inter-pixel grooves with aspect ratios as high as 140:1. Here we present initial results quantifying performance gains in CMOS detector resolution and their impact on the quality of bone microstructure segmentation. We demonstrate 77% gain in spatial resolution at 2 lp/mm and extension of the limiting resolution from 3 lp/mm to 4.5 lp/mm for the CMOS detector with a pixelated screen compared to a commercial sensor. In a bench-top CBCT study emulating diagnostic systems for orthopedic applications (extremity CBCT), we achieved >0.75 correlations in metrics of trabecular microarchitecture between pixelated CsI:Tl based CBCT and gold-standard micro-CT. The pixelated scintillator is expected to have significant impact for many other applications including mammography and digital radiography, where resolution and dose efficiency (DQE) of the detector are of critical importance.

SPIE Journal Paper | 24 January 2020

Evaluation of image quality and task performance for a mobile C-arm with a complementary metal–oxide semiconductor detector

Godwin Abiola, Niral Sheth, Wojciech Zbijewski, Matthew Jacobson, Christopher Bailey, John Filtes, Gerhard Kleinszig, Sebastian Vogt, Stefan Söllradl, Jens Bialkowski, William Anderson, Jeffrey Siewerdsen, Clifford Weiss

JMI Vol. 7 Issue 01

KEYWORDS: Sensors, Image quality, CMOS sensors, Tissues, Fluoroscopy, Radiography, Bone, Semiconductors, Spatial resolution, Temporal resolution

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Proceedings Article | 13 March 2018 Presentation + Paper

Automatic definition of surgical trajectories and acceptance window in pelvic trauma surgery using deformable registration

R. Han, B. Ramsay, T. De Silva, J. Goerres, A. Uneri, M. Ketcha, M. Jacobson, N. Sheth, S. Vogt, G. Kleinszig, G. Osgood, J. Siewerdsen

Proc. SPIE. 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Principal component analysis, Surgery, Image segmentation, Error analysis, Image registration, Bone, Computed tomography, Fluoroscopy, Statistical modeling, Binary data

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Proceedings Article | 12 March 2018 Presentation + Paper

Real-time image-based 3D-2D registration for ultrasound-guided spinal interventions

T. De Silva, A. Uneri, X. Zhang, M. Ketcha, R. Han, M. Jacobson, N. Sheth, S. Vogt, G. Kleinszig, A. Belzberg, D. Sciubba, J. Siewerdsen

Proc. SPIE. 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Image registration, Spine

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Introduction: Ultrasound (US) is a promising low-cost, real-time, portable imaging modality suitable for guidance in spine pain procedures. However, suboptimal image quality and US artifacts confound visualization of deep bony anatomy and have limited its widespread use. Real-time fusion of US images with pre-procedure MRI could provide valuable assistance to guide needle targeting in 3D. To achieve this goal, we propose a fast, entirely image-based 3D-2D rigid registration framework that operates without external hardware tracking and can estimate US probe pose relative to patient position in real-time.

Method: Registration of 2D US (slice) images is performed via the initialization obtained from a fast dictionary search that determines probe pose within a predefined set of pose configurations. 2D slices are extracted from a static 3D US (volume) image to construct a feature dictionary representing different probe poses. Haar features are computed in a fourlevel pyramid that transforms 2D image intensities to a 1D feature vector, which are in turn matched to the 2D target image. 3D-2D registration was performed with the Haar-based initialization with normalized cross-correlation as the metric and Powell’s method as the optimizer. Reduction to 1D feature vectors presents the potential for major gains in speed compared to registration of the 3D and 2D images directly. The method was validated in experiments conducted in a lumbar spine phantom and a cadaver specimen with known translations imparted by a computerized motion stage.

Results: The Haar feature matching method demonstrated initialization accuracy (mean ± std) = (1.9 ± 1.4) mm and (2.1 ± 1.2) mm in phantom and cadaver studies, respectively. The overall registration accuracy was (2.0 ± 1.3) mm and (1.7 ± 0.9) mm, and the initialization was a necessary and important step in the registration process.

Conclusions: The proposed image-based registration method demonstrated promising results for compensating motion of the US probe. This image-based solution could be an important step toward an entirely image-based, real-time registration method of 2D US to 3D US and pre-procedure MRI, eliminating hardware-based tracking systems in a manner more suitable to clinical workflow.

Proceedings Article | 12 March 2018 Paper

Clustered iterative sub-atlas registration for improved deformable registration using statistical shape models

B. Ramsay, T. De Silva, R. Han, M. Ketcha, A. Uneri, J. Goerres, Niral Sheth, Matt Jacobson, S. Vogt, G. Kleinszig, G. Osgood, J. Siewerdsen

Proc. SPIE. 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

KEYWORDS: Principal component analysis, Statistical analysis, Detection and tracking algorithms, Surgery, Image segmentation, Error analysis, Image registration, Shape analysis, Computed tomography, Statistical modeling

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Purpose: Statistical atlases provide a valuable basis for registration and guidance in orthopaedic surgery – for example, automatic anatomical segmentation and planning via atlas-to-patient registration. We report the construction of a statistical shape model for the pelvis containing annotations of common surgical trajectories and investigate a novel method for deformable registration that takes advantage of sub-types that may exist within the atlas and uses them in active shape model registration according to sub-atlas similarity of principal components between atlas members and the target (patient) pelvis.

Methods: CT images from 41 subjects (21 males, 20 females) were derived from the Cancer Imaging Archive (TCIA) and segmented using manual/semi-automatic methods. A statistical shape model was constructed and incorporated in an active shape model (ASM) registration framework for atlas-to-patient registration. Further, we introduce a registration method that exploits clusters in the underlying distribution to iteratively perform registrations after selecting a patient relevant cluster (sub-atlas) that represents similar shape characteristics to the image being registered. Experiments were performed to evaluate surface-to-surface and atlas-to patient registration algorithms using this clustered iterative model. Initial investigation of improved registration based on using similar shapes, was first explored through the use of gender as a categorical way of selecting a possible sub-atlas for registration.

Results: The RMSE surface-to-surface registration error (mean ± std) was reduced from (2.1 ± 0.2) mm when registering according to the entire atlas (N=40 members) to (1.8 ± 0.1) mm when registering within clusters based on similarity of principal components (N=20 members), showing improved accuracy (p<0.001) with fewer atlas members – an efficiency gained by virtue of the proposed approach. The atlas showed clear clusters in the first two principal components corresponding to gender, and the proposed method demonstrated improved accuracy when using ASM registration as well as when applied to a coherent-point drift (CPD) non-rigid deformable registration.

Conclusions: The proposed framework improved atlas-to-patient registration accuracy and increased the efficiency of statistical shape models (i.e., equivalent registration using fewer atlas members) by guiding member selection according to similarity in principal components.

Proceedings Article | 9 March 2018 Presentation + Paper

Imaging performance of CMOS and a-Si:H flat-panel detectors for C-arm fluoroscopy and cone-beam CT

N. Sheth, M. Jacobson, W. Zbijewski, G. Kleinszig, S. Vogt, S. Soellradl, J. Bialkowski, W. Anderson, C. Weiss, G. Osgood, J. Siewerdsen

Proc. SPIE. 10573, Medical Imaging 2018: Physics of Medical Imaging

KEYWORDS: CMOS sensors, Visualization, Spatial frequencies, Sensors, Stereoscopy, Image quality, Modulation transfer functions, Spatial resolution, Fluoroscopy, 3D image processing

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