Dr. Tina Ehtiati Profile

Dr. Tina Ehtiati

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Publications (10)

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.

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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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).

Proceedings Article | 18 March 2015 Paper

Task-driven imaging in cone-beam computed tomography

G. Gang, J. Stayman, S. Ouadah, T. Ehtiati, J. Siewerdsen

Proceedings Volume 9412, 941220 (2015) https://doi.org/10.1117/12.2082169

KEYWORDS: Modulation, Optical spheres, Computed tomography, Imaging systems, Reconstruction algorithms, Spatial frequencies, Signal attenuation, 3D modeling, Head, Sensors

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Purpose: Conventional workflow in interventional imaging often ignores a wealth of prior information of the patient anatomy and the imaging task. This work introduces a task-driven imaging framework that utilizes such information to prospectively design acquisition and reconstruction techniques for cone-beam CT (CBCT) in a manner that maximizes task-based performance in subsequent imaging procedures. Methods: The framework is employed in jointly optimizing tube current modulation, orbital tilt, and reconstruction parameters in filtered back-projection reconstruction for interventional imaging. Theoretical predictors of noise and resolution relates acquisition and reconstruction parameters to task-based detectability. Given a patient-specific prior image and specification of the imaging task, an optimization algorithm prospectively identifies the combination of imaging parameters that maximizes task-based detectability. Initial investigations were performed for a variety of imaging tasks in an elliptical phantom and an anthropomorphic head phantom. Results: Optimization of tube current modulation and view-dependent reconstruction kernel was shown to have greatest benefits for a directional task (e.g., identification of device or tissue orientation). The task-driven approach yielded techniques in which the dose and sharp kernels were concentrated in views contributing the most to the signal power associated with the imaging task. For example, detectability of a line pair detection task was improved by at least three fold compared to conventional approaches. For radially symmetric tasks, the task-driven strategy yielded results similar to a minimum variance strategy in the absence of kernel modulation. Optimization of the orbital tilt successfully avoided highly attenuating structures that can confound the imaging task by introducing noise correlations masquerading at spatial frequencies of interest. Conclusions: This work demonstrated the potential of a task-driven imaging framework to improve image quality and reduce dose beyond that achievable with conventional imaging approaches.

Proceedings Article | 18 March 2015 Paper

Self-calibration of cone-beam CT geometry using 3D-2D image registration: development and application to tasked-based imaging with a robotic C-arm

S. Ouadah, J. Stayman, G. Gang, A. Uneri, T. Ehtiati, J. Siewerdsen

Proceedings Volume 9415, 94151D (2015) https://doi.org/10.1117/12.2082538

KEYWORDS: Calibration, Imaging systems, 3D image processing, Robotics, Image registration, 3D acquisition, Computed tomography, Ions, Robotic systems, 3D image reconstruction

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