Dr. Matthew W. Jacobson Profile

Dr. Matthew W. Jacobson

Faculty Research Associate at Johns Hopkins Univ

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Author

Publications (14)

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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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 | 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

Proceedings Volume 10576, 105761N (2018) https://doi.org/10.1117/12.2292927

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

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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

Proceedings Volume 10576, 105760C (2018) https://doi.org/10.1117/12.2293857

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

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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 | 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

Proceedings Volume 10576, 105760E (2018) https://doi.org/10.1117/12.2293710

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 | 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 | 2 March 2018 Presentation + Paper

A statistical model for image registration performance: effect of tissue deformation

M. Ketcha, T. De Silva, R. Han, A. Uneri, M. Jacobson, S. Vogt, G. Kleinszig, J. Siewerdsen

Proceedings Volume 10574, 105740W (2018) https://doi.org/10.1117/12.2293638

KEYWORDS: Image registration, Image quality, Statistical analysis, Modulation transfer functions, Error analysis, Image processing

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SPIE Journal Paper | 13 February 2018

Virtual fluoroscopy for intraoperative C-arm positioning and radiation dose reduction

Tharindu De Silva, Joshua Punnoose, Ali Uneri, Mahadevappa Mahesh, Joseph Goerres, Matthew Jacobson, Michael Ketcha, Amir Manbachi, Sebastian Vogt, Gerhard Kleinszig, Akhil Jay Khanna, Jean-Paul Wolinsky, Jeffrey Siewerdsen, Greg Osgood

JMI, Vol. 5, Issue 01, 015005, (February 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.015005

KEYWORDS: Fluoroscopy, Radiography, Image registration, Surgery, Computer programming, Computed tomography, Radiology, Visualization, Skin, X-ray computed tomography

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Positioning of an intraoperative C-arm to achieve clear visualization of a particular anatomical feature often involves repeated fluoroscopic views, which cost time and radiation exposure to both the patient and surgical staff. A system for virtual fluoroscopy (called FluoroSim) that could dramatically reduce time- and dose-spent “fluoro-hunting” by leveraging preoperative computed tomography (CT), encoded readout of C-arm gantry position, and automatic 3D–2D image registration has been developed. The method is consistent with existing surgical workflow and does not require additional tracking equipment. Real-time virtual fluoroscopy was achieved via mechanical encoding of the C-arm motion, C-arm geometric calibration, and patient registration using a single radiograph. The accuracy, time, and radiation dose associated with C-arm positioning were measured for FluoroSim in comparison with conventional methods. Five radiology technologists were tasked with acquiring six standard pelvic views pertinent to sacro-illiac, anterior–inferior iliac spine, and superior-ramus screw placement in an anthropomorphic pelvis phantom using conventional and FluoroSim approaches. The positioning accuracy, exposure time, number of exposures, and total time for each trial were recorded, and radiation dose was characterized in terms of entrance skin dose and in-room scatter. The geometric accuracy of FluoroSim was measured to be 1.6±1.1 mm. There was no significant difference (p>0.05) observed in the accuracy or total elapsed time for C-arm positioning. However, the total fluoroscopy time required to achieve the desired view decreased by 4.1 s (4.7±3.6 s for conventional, compared with 0.5±0.0 s for FluoroSim, p<0.05), and the total number of exposures reduced by 4.0 (6.4±4.8 for conventional, compared with 2.0±0.0 for FluoroSim, p<0.05). These reductions amounted to a 50% to 78% decrease in patient entrance skin dose and a 55% to 70% reduction in in-room scatter. FluoroSim was found to reduce the radiation exposure required in C-arm positioning without diminishing positioning time or accuracy, providing a potentially valuable tool to assist technologists and surgeons.

Proceedings Article | 9 March 2017 Presentation + Paper

Geometric calibration using line fiducials for cone-beam CT with general, non-circular source-detector trajectories

M. Jacobson, M. Ketcha, A. Uneri, J. Goerres, T. De Silva, S. Reaungamornrat, S. Vogt, G. Kleinszig, J. Siewerdsen

Proceedings Volume 10132, 101320I (2017) https://doi.org/10.1117/12.2255724

KEYWORDS: Calibration, Image quality, Computed tomography, 3D modeling, Spatial resolution, Head, Sensors, Bone, Spherical lenses

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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 | 3 March 2017 Presentation + Paper

Fundamental limits of image registration performance: effects of image noise and resolution in CT-guided interventions

M. Ketcha, T. de Silva, R. Han, A. Uneri, J. Goerres, M. Jacobson, S. Vogt, G. Kleinszig, J. Siewerdsen

Proceedings Volume 10135, 1013508 (2017) https://doi.org/10.1117/12.2256025

KEYWORDS: Image registration, Image quality, Image resolution, Imaging systems, Image-guided intervention, Computed tomography, Modulation transfer functions, Error analysis, Brain-machine interfaces, Image visualization

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Purpose: In image-guided procedures, image acquisition is often performed primarily for the task of geometrically registering information from another image dataset, rather than detection / visualization of a particular feature. While the ability to detect a particular feature in an image has been studied extensively with respect to image quality characteristics (noise, resolution) and is an ongoing, active area of research, comparatively little has been accomplished to relate such image quality characteristics to registration performance.

Methods: To establish such a framework, we derived Cramer-Rao lower bounds (CRLB) for registration accuracy, revealing the underlying dependencies on image variance and gradient strength. The CRLB was analyzed as a function of image quality factors (in particular, dose) for various similarity metrics and compared to registration accuracy using CT images of an anthropomorphic head phantom at various simulated dose levels. Performance was evaluated in terms of root mean square error (RMSE) of the registration parameters.

Results: Analysis of the CRLB shows two primary dependencies: 1) noise variance (related to dose); and 2) sum of squared image gradients (related to spatial resolution and image content). Comparison of the measured RMSE to the CRLB showed that the best registration method, RMSE achieved the CRLB to within an efficiency factor of 0.21, and optimal estimators followed the predicted inverse proportionality between registration performance and radiation dose.

Conclusions: Analysis of the CRLB for image registration is an important step toward understanding and evaluating an intraoperative imaging system with respect to a registration task. While the CRLB is optimistic in absolute performance, it reveals a basis for relating the performance of registration estimators as a function of noise content and may be used to guide acquisition parameter selection (e.g., dose) for purposes of intraoperative registration.

Proceedings Article | 3 March 2017 Paper

C-arm positioning using virtual fluoroscopy for image-guided surgery

T. de Silva, J. Punnoose, A. Uneri, J. Goerres, M. Jacobson, M. Ketcha, A. Manbachi, S. Vogt, G. Kleinszig, A. Khanna, J.-P. Wolinsky, G. Osgood, J. Siewerdsen

Proceedings Volume 10135, 101352K (2017) https://doi.org/10.1117/12.2256028

KEYWORDS: Fluoroscopy, Image registration, Image-guided intervention, Radiography, Calibration, Computer programming, Surgery, Radiology, Computed tomography, Computer simulations

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Introduction: Fluoroscopically guided procedures often involve repeated acquisitions for C-arm positioning at the cost of radiation exposure and time in the operating room. A virtual fluoroscopy system is reported with the potential of reducing dose and time spent in C-arm positioning, utilizing three key advances: robust 3D-2D registration to a preoperative CT; real-time forward projection on GPU; and a motorized mobile C-arm with encoder feedback on C-arm orientation.

Method: Geometric calibration of the C-arm was performed offline in two rotational directions (orbit α, orbit β). Patient registration was performed using image-based 3D-2D registration with an initially acquired radiograph of the patient. This approach for patient registration eliminated the requirement for external tracking devices inside the operating room, allowing virtual fluoroscopy using commonly available systems in fluoroscopically guided procedures within standard surgical workflow. Geometric accuracy was evaluated in terms of projection distance error (PDE) in anatomical fiducials. A pilot study was conducted to evaluate the utility of virtual fluoroscopy to aid C-arm positioning in image guided surgery, assessing potential improvements in time, dose, and agreement between the virtual and desired view.

Results: The overall geometric accuracy of DRRs in comparison to the actual radiographs at various C-arm positions was PDE (mean ± std) = 1.6 ± 1.1 mm. The conventional approach required on average 8.0 ± 4.5 radiographs spent “fluoro hunting” to obtain the desired view. Positioning accuracy improved from 2.6^o ± 2.3^o (in α) and 4.1^o ± 5.1^o (in β) in the conventional approach to 1.5^o ± 1.3^o and 1.8^o ± 1.7^o, respectively, with the virtual fluoroscopy approach.

Conclusion: Virtual fluoroscopy could improve accuracy of C-arm positioning and save time and radiation dose in the operating room. Such a system could be valuable to training of fluoroscopy technicians as well as intraoperative use in fluoroscopically guided procedures.

Proceedings Article | 3 March 2017 Presentation + Paper

Deformable 3D-2D registration for guiding K-wire placement in pelvic trauma surgery

J. Goerres, M. Jacobson, A. Uneri, T. de Silva, M. Ketcha, S. Reaungamornrat, S. Vogt, G. Kleinszig, J.-P. Wolinsky, G. Osgood, J. Siewerdsen

Proceedings Volume 10135, 101350A (2017) https://doi.org/10.1117/12.2255952

KEYWORDS: X-rays, Calibration, Image registration, Surgery, Imaging systems, Navigation systems, Fluoroscopy, Bone, X-ray computed tomography, Radiography, Digital x-ray imaging

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Proceedings Article | 29 April 2005 Paper

Fast interpolation operations in non-rigid image registration

Matthew Jacobson, Jeffrey Fessler

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.592243

KEYWORDS: Image registration, Matrices, Dielectrophoresis, Lung, 3D modeling, Convolution, Medical imaging, Detection and tracking algorithms, Imaging arrays, Surgery

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