Dr. Clifford R. Weiss Profile

Dr. Clifford R. Weiss

at Johns Hopkins Univ

SPIE Involvement:

Author

Publications (17)

Proceedings Article | 7 April 2023 Presentation + Paper

Multi-stage Adaptive Spline Autofocus (MASA) with a learned metric for deformable motion compensation in interventional cone-beam CT

H. Huang, J. Siewerdsen, A. Lu, Y. Hu, W. Zbijewski, M. Unberath, C. Weiss, A. Sisniega

Proceedings Volume 12463, 1246314 (2023) https://doi.org/10.1117/12.2654361

KEYWORDS: Deformation, Cone beam computed tomography, Motion estimation, Machine learning

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Cone-beam CT (CBCT) is widespread in abdominal interventional imaging, but its long acquisition time makes it susceptible to patient motion. Image-based autofocus has shown success in CBCT deformable motion compensation, via deep autofocus metrics and multi-region optimization, but it is challenged by the large parameter dimensionality required to capture intricate motion trajectories. This work leverages the differentiable nature of deep autofocus metrics to build a novel optimization strategy, Multi-Stage Adaptive Spine Autofocus (MASA), for compensation of complex deformable motion in abdominal CBCT. MASA poses the autofocus problem as a multi-stage adaptive sampling strategy of the motion trajectory, sampled with Hermite spline basis with variable amplitude and knot temporal positioning. The adaptive method permits simultaneous optimization of the sampling phase, local temporal sampling density, and time-dependent amplitude of the motion trajectory. The optimization is performed in a multi-stage schedule with increasing number of knots that progressively accommodates complex trajectories in late stages, preconditioned by coarser components from early stages, and with minimal increase in dimensionality. MASA was evaluated in controlled simulation experiments with two types of motion trajectories: i) combinations of slow drifts with sudden jerk (sigmoid) motion; and ii) combinations of periodic motion sources of varying frequency into multi-frequency trajectories. Further validation was obtained in clinical data from liver CBCT featuring motion of contrast-enhanced vessels, and soft-tissue structures. The adaptive sampling strategy provided successful motion compensation in sigmoid trajectories, compared to fixed sampling strategies (mean SSIM increase of 0.026 compared to 0.011). Inspection of the estimated motion showed the capability of MASA to automatically allocate larger sampling density to parts of the scan timeline featuring sudden motion, effectively accommodating complex motion without increasing the problem dimension. Experiments on multifrequency trajectories with 3-stage MASA (5, 10, and 15 knots) yielded a twofold SSIM increase compared to single-stage autofocus with 15 knots (0.076 vs 0.040, respectively). Application of MASA to clinical datasets resulted in simultaneous improvement on the delineation of both contrast-enhanced vessels and soft-tissue structures in the liver. A new autofocus framework, MASA, was developed including a novel multi-stage technique for adaptive temporal sampling of the motion trajectory in combination with fully differentiable deep autofocus metrics. This novel adaptive sampling approach is a crucial step for application of deformable motion compensation to complex temporal motion trajectories.

Proceedings Article | 3 April 2023 Presentation + Paper

Deformable motion compensation for intraprocedural vascular cone-beam CT with sequential projection domain targeting and vessel-enhancing autofocus

Alexander Lu, Heyuan Huang, Yicheng Hu, Wojtek Zbijewski, Mathias Unberath, Jeffrey Siewerdsen, Clifford Weiss, Alejandro Sisniega

Proceedings Volume 12466, 124660P (2023) https://doi.org/10.1117/12.2652137

KEYWORDS: Cone beam computed tomography, Anatomy, Deformation, Transformers, 3D acquisition, Motion models, Image segmentation, Image restoration, Radiology

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Cone-Beam CT (CBCT) is used in Interventional Radiology (IR) for identification of complex vascular anatomy, difficult to visualize in 2D fluoroscopy. However, long acquisition time makes CBCT susceptible to soft-tissue deformable motion that degrades visibility of fine vessels. We propose a targeted framework to compensate for deformable intra-scan motion via learned full-sequence models for identification of vascular anatomy coupled to an autofocus function specifically tailored to vascular imaging. The vessel-targeted autofocus acts in two stages: (i) identification of vascular and catheter targets in the projection domain; and (ii) autofocus optimization for a 4D vector field through an objective function that quantifies vascular visibility. Target identification is based on a deep learning model that operates on the complete sequence of projections, via a transformer encoder-decoder architecture that uses spatial-temporal self-attention modules to infer long-range feature correlations, enabling identification of vascular anatomy with highly variable conspicuity. The vascular autofocus function is derived through eigenvalues of the local image Hessian, which quantify the local image structure for identification of bright tubular structures. Motion compensation was achieved via spatial transformer operators that impart time dependent deformations to N_PAR = 90 partial angle reconstructions, allowing for efficient minimization via gradient backpropagation. The framework was trained and evaluated in synthetic abdominal CBCTs obtained from liver MDCT volumes and including realistic models of contrast-enhanced vascularity with 15 to 30 end branches, 1 – 3.5 mm vessel diameter, and 1400 HU contrast. The targeted autofocus resulted in qualitative and quantitative improvement in vascular visibility in both simulated and clinical intra-procedural CBCT. The transformer-based target identification module resulted in superior detection of target vascularity and a lower number of false positives, compared to a baseline U-Net model acting on individual projection views, reflected as a 1.97x improvement in intersection-over-union values. Motion compensation in simulated data yielded improved conspicuity of vascular anatomy, and reduced streak artifacts and blurring around vessels, as well as recovery of shape distortion. These improvements amounted to an average 147% improvement in cross correlation computed against the motion-free ground truth, relative to the un-compensated reconstruction. Targeted autofocus yielded improved visibility of vascular anatomy in abdominal CBCT, providing better potential for intra-procedural tracking of fine vascular anatomy in 3D images. The proposed method poses an efficient solution to motion compensation in task-specific imaging, with future application to a wider range of imaging scenarios.

Proceedings Article | 18 October 2022 Open Access

Practical workflow for arbitrary non-circular orbits for CT with clinical robotic C-arms

Yiqun Ma, Grace Gang, Tess Reynolds, Tina Ehitiati, Junyuan Li, Owen Dillon, Tom Russ, Wenying Wang, Clifford Weiss, Nicholas Theodore, Kelvin Hong, Ricky O’Brien, Jeffrey Siewerdsen, J. Webster Stayman

Proceedings Volume 12304, 123042H (2022) https://doi.org/10.1117/12.2647158

KEYWORDS: Calibration, Robotics, Image quality, Spine, Imaging systems, Reconstruction algorithms, Data acquisition, CT reconstruction, X-ray computed tomography

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Proceedings Article | 18 October 2022 Open Access

Context-aware, reference-free local motion metric for CBCT deformable motion compensation

Heyuan Huang, Jeffrey Siewerdsen, Wojciech Zbijewski, Clifford Weiss, Mathias Unberath, Alejandro Sisniega

Proceedings Volume 12304, 1230412 (2022) https://doi.org/10.1117/12.2646857

KEYWORDS: Motion estimation, Image quality, Information visualization, Motion models, Convolutional neural networks, Computed tomography, Abdomen

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Deformable motion is one of the main challenges to image quality in interventional cone beam CT (CBCT). Autofocus methods have been successfully applied for deformable motion compensation in CBCT, using multi-region joint optimization approaches that leverage the moderately smooth spatial variation motion of the deformable motion field with a local neighborhood. However, conventional autofocus metrics enforce images featuring sharp image-appearance, but do not guarantee the preservation of anatomical structures. Our previous work (DL-VIF) showed that deep convolutional neural networks (CNNs) can reproduce metrics of structural similarity (visual information fidelity - VIF), removing the need for a matched motion-free reference, and providing quantification of motion degradation and structural integrity. Application of DL-VIF within local neighborhoods is challenged by the large variability of local image content across a CBCT volume and requires global context information for successful evaluation of motion effects. In this work, we propose a novel deep autofocus metric, based on a context-aware, multi-resolution, deep CNN design. In addition to the inclusion of contextual information, the resulting metric generates a voxel-wise distribution of reference-free VIF values. The new metric, denoted CADL-VIF, was trained on simulated CBCT abdomen scans with deformable motion at random locations and with amplitude up to 30 mm. The CADL-VIF achieved good correlation with the ground truth VIF map across all test cases with R2 = 0.843 and slope = 0.941. When integrated into a multi-ROI deformable motion compensation method, CADL-VIF consistently reduced motion artifacts, yielding an average increase in SSIM of 0.129 in regions with severe motion and 0.113 in regions with mild motion. This work demonstrated the capability of CADL-VIF to recognize anatomical structures and penalize unrealistic images, which is a key step in developing reliable autofocus for complex deformable motion compensation in CBCT.

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Proceedings Article | 18 October 2022 Open Access

Simulation of random deformable motion in soft-tissue cone-beam CT with learned models

Y. Hu, H. Huang, J. Siewerdsen, W. Zbijewski, M. Unberath, C. Weiss, A. Sisniega

Proceedings Volume 12304, 1230413 (2022) https://doi.org/10.1117/12.2646720

KEYWORDS: Motion models, Data modeling, Spine, Convolution, Computer simulations, Computer programming, Abdomen, Transformers, Tissues

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Cone-beam CT (CBCT) is widely used for guidance in interventional radiology but it is susceptible to motion artifacts. Motion in interventional CBCT features a complex combination of diverse sources including quasi-periodic, consistent motion patterns such as respiratory motion, and aperiodic, quasi-random, motion such as peristalsis. Recent developments in image-based motion compensation methods include approaches that combine autofocus techniques with deep learning models for extraction of image features pertinent to CBCT motion. Training of such deep autofocus models requires the generation of large amounts of realistic, motion-corrupted CBCT. Previous works on motion simulation were mostly focused on quasi-periodic motion patterns, and reliable simulation of complex combined motion with quasi-random components remains an unaddressed challenge. This work presents a framework aimed at synthesis of realistic motion trajectories for simulation of deformable motion in soft-tissue CBCT. The approach leveraged the capability of conditional generative adversarial network (GAN) models to learn the complex underlying motion present in unlabeled, motion-corrupted, CBCT volumes. The approach is designed for training with unpaired clinical CBCT in an unsupervised fashion. This work presents a first feasibility study, in which the model was trained with simulated data featuring known motion, providing a controlled scenario for validation of the proposed approach prior to extension to clinical data. Our proof-of-concept study illustrated the potential of the model to generate realistic, variable simulation of CBCT deformable motion fields, consistent with three trends underlying the designed training data: i) the synthetic motion induced only diffeomorphic deformations – with Jacobian Determinant larger than zero; ii) the synthetic motion showed median displacement of 0.5 mm in regions predominantly static in the training (e.g., the posterior aspect of the patient laying supine), compared to a median displacement of 3.8 mm in regions more prone to motion in the training; and iii) the synthetic motion exhibited predominant directionality consistent with the training set, resulting in larger motion in the superior-inferior direction (median and maximum amplitude of 4.58 mm and 20 mm, ⪆2x larger than the two remaining direction). Together, the proposed framework shows the feasibility for realistic motion simulation and synthesis of variable CBCT data.

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Proceedings Article | 4 April 2022 Presentation + Paper

Targeted deformable motion compensation for vascular interventional cone-beam CT imaging

Alejandro Sisniega, Alexander Lu, Heyuan Huang, Wojciech Zbijewski, Mathias Unberath, Jeffrey Siewerdsen, Clifford Weiss

Proceedings Volume 12031, 120311H (2022) https://doi.org/10.1117/12.2613232

KEYWORDS: Liver, Computed tomography, Motion estimation, Image quality, Visibility, Motion models, 3D acquisition, 3D image reconstruction, CT reconstruction

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Purpose: Cone-beam CT has become commonplace for 3D guidance in interventional radiology (IR), especially for vascular procedures in which identification of small vascular structures is crucial. However, its long image acquisition time poses a limit to image quality due to soft-tissue deformable motion that hampers visibility of small vessels. Autofocus motion compensation has shown promising potential for soft-tissue deformable motion compensation, but it lacks specific target to the imaging task. This work presents an approach for deformable motion compensation targeted at imaging of vascular structures. Methods: The proposed method consists on a two-stage framework for: i) identification of contrast-enhanced blood vessels in 2D projection data and delineation of an approximate region covering the vascular target in the volume space, and, ii) a novel autofocus approach including a metric designed to promote the presence of vascular structures acting solely in the region of interest. The vesselness of the image is quantified via evaluation of the properties of the 3D image Hessian, yielding a vesselness filter that gives larger values to voxels candidate to be part of a tubular structure. A cost metric is designed to promote large vesselness values and spatial sparsity, as expected in regions of fine vascularity. A targeted autofocus method was designed by combining the presented metric with a conventional autofocus term acting outside of the region of interest. The resulting method was evaluated on simulated data including synthetic vascularity merged with real anatomical features obtained from MDCT data. Further evaluation was obtained in two clinical datasets obtained during TACE procedures with a robotic C-arm (Artis Zeego, Siemens Healthineers). Results: The targeted vascular autofocus effectively restored the shape and contrast of the contrast-enhanced vascularity in the simulation cases, resulting in improved visibility and reduced artifacts. Segmentations performed with a single threshold value on the target vascular regions yielded a net increase of up to 42% in DICE coefficient computed against the static reference. Motion compensation in clinical datasets resulted in improved visibility of vascular structures, observed in maximum intensity projections of the contrast-enhanced liver vessel tree. Conclusion: Targeted motion compensation for vascular imaging showed promising performance for increased identification of small vascular structures in presence of motion. The development of autofocus metrics and methods tailored to vascular imaging opens the way for reliable compensation of deformable motion while preserving the integrity of anatomical structures in the image.

Proceedings Article | 4 April 2022 Presentation + Paper

Non-circular CBCT orbit design and realization on a clinical robotic C-arm for metal artifact reduction

Yiqun Ma, Grace Gang, Tina Ehtiati, Tess Reynolds, Tom Russ, Wenying Wang, Clifford Weiss, Nicholas Theodore, Kelvin Hong, Jeffrey Siewerdsen, J. Webster Stayman

Proceedings Volume 12034, 120340A (2022) https://doi.org/10.1117/12.2612448

KEYWORDS: Metals, Robotics, Reconstruction algorithms, Calibration, Data acquisition, Spine, CT reconstruction, X-ray medical imaging, 3D acquisition, X-ray computed tomography

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Proceedings Article | 23 February 2021 Presentation + Paper

Deformable image-based motion compensation for interventional cone-beam CT with a learned autofocus metric

A. Sisniega, H. Huang, W. Zbijewski, J. W. Stayman, C. Weiss, T. Ehtiati, J. Siewerdsen

Proceedings Volume 11595, 115950W (2021) https://doi.org/10.1117/12.2582140

KEYWORDS: Computed tomography, Image quality, Visualization, Visibility, Stereoscopy, Motion estimation, Long wavelength infrared, Infrared imaging, Image-guided intervention, Image visualization

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Purpose: Cone-beam CT is a common approach for 3D guidance in interventional radiology (IR), but its long image acquisition time poses a limit to image quality due to complex deformable motion of soft-tissue structures. Multi-region autofocus optimization showed successful compensation of deformable motion with gains in image quality. However, the use of sub-optimal conventional autofocus metrics, accompanied by the high-dimensionality and non-convexity of the optimization problem result in challenged convergence. This work presents a learning-based approach to the design of autofocus metrics tailored to the specific problem of motion compensation in soft-tissue CBCT. Methods: A deep convolutional neural network (CNN) was used to automatically extract image features quantifying the local motion amplitude and principal direction in volumetric regions of interest (128 x 128x 128 voxels) of a motion contaminated volume. The estimated motion amplitude is then used as the core component of the cost function of the deformable autofocus method, complemented by a regularization term favoring similar motion for regions close in space. The network consists of a siamese arrangement of three branches acting on the three central orthogonal planes of the volumetric ROI. The network was trained with simulated data generated from publicly available CT datasets, including deformable motion fields from which volumetric ROIs with locally rigid motion were extracted. The performance of motion amplitude estimation and of the final CNN-based deformable autofocus were assessed on synthetic CBCT data generated similarly to the training dataset and featuring deformable motion fields with 1 to 5 components with random direction and random amplitude ranging from 0 mm to 50 mm. Results: Predicted local motion amplitude showed good agreement with the true values, showing a liner relationship (R2 = 0.96, slope 0.95), and slight underestimation of the motion amplitude. Absolute errors in total motion amplitude and individual components remained < 2 mm throughout the explored range. Relative errors were higher for low amplitude motion, pointing to need of a larger training cohort, more focused on low motion amplitude scenarios. Motion compensation with the learning-based metric showed successful compensation of motion artifacts in single ROI environments, with 40% reduction in median RMSE using the motion static image as reference. Deformable motion compensation resulted in better visualization of soft-tissue structures, and overall sharper image details, with slight residual motion artifacts for regions combining moderate motion amplitude with complex anatomical structures. Conclusion: Deformable motion compensation with automatically learned autofocus metrics was proved feasible, opening the way to the design of metrics with potential for more reliable performance and easier optimization than conventional autofocus metrics.

Proceedings Article | 16 March 2020 Presentation + Paper

Image-based deformable motion compensation in cone-beam CT: translation to clinical studies in interventional body radiology

S. Capostagno, A. Sisniega, J. Stayman, T. Ehtiati, C. Weiss, J. Siewerdsen

Proceedings Volume 11315, 113150B (2020) https://doi.org/10.1117/12.2549998

KEYWORDS: Motion estimation, Computed tomography, Radiology, Spine, Abdomen, 3D image processing, Tissues, Image quality, Tumors

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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.

Proceedings Article | 16 March 2020 Presentation + Paper

Estimation of local deformable motion in image-based motion compensation for interventional cone-beam CT

A. Sisniega, S. Capostagno, W. Zbijewski, J. Stayman, C. Weiss, T. Ehtiati, J. Siewerdsen

Proceedings Volume 11312, 113121M (2020) https://doi.org/10.1117/12.2549753

KEYWORDS: Motion estimation, Computed tomography, Motion models, Image-guided intervention, Image quality, Abdomen, Databases, Data archive systems

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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.

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, 015501, (January 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.1.015501

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

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Proceedings Article | 28 May 2019 Paper

Convergence criterion for MBIR based on the local noise-power spectrum: Theory and implementation in a framework for accelerated 3D image reconstruction with a morphological pyramid

A. Sisniega, J. W. Stayman, S. Capostagno, C. Weiss, T. Ehtiati, J. Siewerdsen

Proceedings Volume 11072, 1107209 (2019) https://doi.org/10.1117/12.2534896

KEYWORDS: Image quality, Data modeling, Image analysis, 3D image reconstruction, Error analysis, Reconstruction algorithms, Projection systems

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SPIE Journal Paper | 9 May 2019

Task-driven source–detector trajectories in cone-beam computed tomography: II. Application to neuroradiology

Sarah Capostagno, Joseph Stayman, Matthew Jacobson, Tina Ehtiati, Clifford Weiss, Jeffrey Siewerdsen

JMI, Vol. 6, Issue 02, 025004, (May 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.025004

KEYWORDS: Robotics, Imaging systems, Aneurysms, Image quality, Visualization, Optical spheres, Computed tomography, 3D image processing, Calibration, Sensors

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Proceedings Article | 1 March 2019 Presentation + Paper

Image-based deformable motion compensation for interventional cone-beam CT

A. Sisniega, S. Capostagno, W. Zbijewski, C. Weiss, T. Ehtiati, J. Siewerdsen

Proceedings Volume 10948, 109481O (2019) https://doi.org/10.1117/12.2513446

KEYWORDS: Liver, Motion estimation, Image quality, Computed tomography, Abdomen, Image-guided intervention, 3D image processing, Motion models

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SPIE Journal Paper | 27 March 2018

Photoacoustic-based catheter tracking: simulation, phantom, and in vivo studies

Alexis Cheng, Younsu Kim, Yuttana Itsarachaiyot, Haichong Zhang, Clifford Weiss, Russell Taylor, Emad Boctor

JMI, Vol. 5, Issue 02, 021223, (March 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.2.021223

KEYWORDS: Photoacoustic spectroscopy, Ferroelectric materials, Sensors, In vivo imaging, Acoustics, Chemical elements, Distance measurement, Image segmentation, Fluoroscopy, Imaging systems

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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

Proceedings Volume 10573, 105730M (2018) https://doi.org/10.1117/12.2293780

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

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Proceedings Article | 9 March 2017 Presentation + Paper

Task-driven orbit design and implementation on a robotic C-arm system for cone-beam CT

S. Ouadah, M. Jacobson, J. Stayman, T. Ehtiati, C. Weiss, J. Siewerdsen

Proceedings Volume 10132, 101320H (2017) https://doi.org/10.1117/12.2255646

KEYWORDS: Imaging systems, Robotic systems, Computed tomography, 3D metrology, Image-guided intervention, Systems modeling, Spatial resolution, Medicine, Visualization, Reconstruction algorithms, Head, Modulation transfer functions

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Purpose: This work applies task-driven optimization to the design of non-circular orbits that maximize imaging performance for a particular imaging task. First implementation of task-driven imaging on a clinical robotic C-arm system is demonstrated, and a framework for orbit calculation is described and evaluated.

Methods: We implemented a task-driven imaging framework to optimize orbit parameters that maximize detectability index d'. This framework utilizes a specified Fourier domain task function and an analytical model for system spatial resolution and noise. Two experiments were conducted to test the framework. First, a simple task was considered consisting of frequencies lying entirely on the f_z-axis (e.g., discrimination of structures oriented parallel to the central axial plane), and a “circle + arc” orbit was incorporated into the framework as a means to improve sampling of these frequencies, and thereby increase task-based detectability. The orbit was implemented on a robotic C-arm (Artis Zeego, Siemens Healthcare). A second task considered visualization of a cochlear implant simulated within a head phantom, with spatial frequency response emphasizing high-frequency content in the (f_y, f_z) plane of the cochlea. An optimal orbit was computed using the task-driven framework, and the resulting image was compared to that for a circular orbit.

Results: For the f_z-axis task, the circle + arc orbit was shown to increase d' by a factor of 1.20, with an improvement of 0.71 mm in a 3D edge-spread measurement for edges located far from the central plane and a decrease in streak artifacts compared to a circular orbit. For the cochlear implant task, the resulting orbit favored complementary views of high tilt angles in a 360° orbit, and d' was increased by a factor of 1.83.

Conclusions: This work shows that a prospective definition of imaging task can be used to optimize source-detector orbit and improve imaging performance. The method was implemented for execution of non-circular, task-driven orbits on a clinical robotic C-arm system. The framework is sufficiently general to include both acquisition parameters (e.g., orbit, kV, and mA selection) and reconstruction parameters (e.g., a spatially varying regularizer).

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