Mr. Jordan M. Slagowski Profile

Jordan M. Slagowski

Doctoral Candidate at Univ of Wisconsin-Madison

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Publications (10)

SPIE Journal Paper | 18 April 2017

Dynamic electronic collimation method for 3-D catheter tracking on a scanning-beam digital x-ray system

David A. Dunkerley, Jordan Slagowski, Tobias Funk, Michael Speidel

JMI, Vol. 4, Issue 02, 023501, (April 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.2.023501

KEYWORDS: X-rays, Collimation, Monte Carlo methods, 3D image processing, X-ray imaging, Sensors, Detection and tracking algorithms, Fluoroscopy, X-ray detectors, Imaging systems

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SPIE Journal Paper | 20 March 2017 Open Access

Single-view geometric calibration for C-arm inverse geometry CT

Jordan Slagowski, David Dunkerley, Charles Hatt, Michael Speidel

JMI, Vol. 4, Issue 01, 013506, (March 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.1.013506

KEYWORDS: Calibration, Sensors, Detector arrays, Composites, Data acquisition, Modulation transfer functions, Error analysis, X-rays, Computed tomography, Image sensors

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Proceedings Article | 9 March 2017 Paper

4D DSA reconstruction using tomosynthesis projections

Michael Speidel, Marc Buehler, Jordan Slagowski, Charles Mistretta, Charles Strother

Proceedings Volume 10132, 101322C (2017) https://doi.org/10.1117/12.2255197

KEYWORDS: Angiography, X-ray computed tomography, Signal attenuation, 3D scanning, X-rays, Image restoration, Arteries, Veins, Temporal resolution, 3D acquisition, X-ray detectors, Reconstruction algorithms

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Proceedings Article | 9 March 2017 Presentation + Paper

Automated 3D coronary sinus catheter detection using a scanning-beam digital x-ray system

Michael Speidel, David A. Dunkerley, Jordan Slagowski, Lindsay Bodart

Proceedings Volume 10132, 101321N (2017) https://doi.org/10.1117/12.2254443

KEYWORDS: X-rays, Fluoroscopy, 3D displays, Detection and tracking algorithms, X-ray imaging, Electrodes, Chest, Motion detection, X-ray sources, 3D modeling, Motion models, Motion measurement, 3D acquisition, 3D image processing

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Proceedings Article | 9 March 2017 Paper

Localization of cardiac volume and patient features in inverse geometry x-ray fluoroscopy

Michael Speidel, Jordan Slagowski, David A. Dunkerley, Martin Wagner, Tobias Funk, Amish Raval

Proceedings Volume 10132, 101325T (2017) https://doi.org/10.1117/12.2254400

KEYWORDS: Angiography, Fluoroscopy, Arteries, Reconstruction algorithms, Lung, X-ray imaging, Cardiology, Heart, X-ray detectors

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Proceedings Article | 3 March 2017 Presentation + Paper

Feature-based respiratory motion tracking in native fluoroscopic sequences for dynamic roadmaps during minimally invasive procedures in the thorax and abdomen

Martin Wagner, Paul Laeseke, Tilman Schubert, Jordan Slagowski, Michael Speidel, Charles Mistretta

Proceedings Volume 10135, 101351H (2017) https://doi.org/10.1117/12.2254148

KEYWORDS: Feature extraction, Fluoroscopy, Image registration, Abdomen, Image contrast enhancement, Image enhancement, Motion models, Edge detection, Error analysis, Image segmentation, Motion estimation, Liver, Detection and tracking algorithms, Process modeling

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Fluoroscopic image guidance for minimally invasive procedures in the thorax and abdomen suffers from respiratory and cardiac motion, which can cause severe subtraction artifacts and inaccurate image guidance. This work proposes novel techniques for respiratory motion tracking in native fluoroscopic images as well as a model based estimation of vessel deformation. This would allow compensation for respiratory motion during the procedure and therefore simplify the workflow for minimally invasive procedures such as liver embolization. The method first establishes dynamic motion models for both the contrast-enhanced vasculature and curvilinear background features based on a native (non-contrast) and a contrast-enhanced image sequence acquired prior to device manipulation, under free breathing conditions. The model of vascular motion is generated by applying the diffeomorphic demons algorithm to an automatic segmentation of the subtraction sequence. The model of curvilinear background features is based on feature tracking in the native sequence. The two models establish the relationship between the respiratory state, which is inferred from curvilinear background features, and the vascular morphology during that same respiratory state. During subsequent fluoroscopy, curvilinear feature detection is applied to determine the appropriate vessel mask to display. The result is a dynamic motioncompensated vessel mask superimposed on the fluoroscopic image. Quantitative evaluation of the proposed methods was performed using a digital 4D CT-phantom (XCAT), which provides realistic human anatomy including sophisticated respiratory and cardiac motion models. Four groups of datasets were generated, where different parameters (cycle length, maximum diaphragm motion and maximum chest expansion) were modified within each image sequence. Each group contains 4 datasets consisting of the initial native and contrast enhanced sequences as well as a sequence, where the respiratory motion is tracked. The respiratory motion tracking error was between 1.00 % and 1.09 %. The estimated dynamic vessel masks yielded a Sørensen-Dice coefficient between 0.94 and 0.96. Finally, the accuracy of the vessel contours was measured in terms of the 99th percentile of the error, which ranged between 0.64 and 0.96 mm. The presented results show that the approach is feasible for respiratory motion tracking and compensation and could therefore considerably improve the workflow of minimally invasive procedures in the thorax and abdomen

Proceedings Article | 22 March 2016 Paper

A geometric calibration method for inverse geometry computed tomography using P-matrices

Jordan Slagowski, David A. Dunkerley, Charles Hatt, Michael Speidel

Proceedings Volume 9783, 978337 (2016) https://doi.org/10.1117/12.2216565

KEYWORDS: Calibration, Detector arrays, Computed tomography, X-rays, CT reconstruction, Tomography, Imaging systems, X-ray technology, Composites, Fluoroscopy, Error analysis, Data acquisition, X-ray computed tomography

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Proceedings Article | 18 March 2015 Paper

Feasibility of CT-based 3D anatomic mapping with a scanning-beam digital x-ray (SBDX) system

Jordan Slagowski, Michael Tomkowiak, David Dunkerley, Michael Speidel

Proceedings Volume 9412, 941209 (2015) https://doi.org/10.1117/12.2082052

KEYWORDS: Image segmentation, Reconstruction algorithms, X-rays, 3D modeling, Sensors, X-ray computed tomography, 3D acquisition, Optical spheres, CT reconstruction, Imaging systems

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Proceedings Article | 18 March 2015 Paper

Detector, collimator and real-time reconstructor for a new scanning-beam digital x-ray (SBDX) prototype

Michael Speidel, Michael Tomkowiak, Amish Raval, David A. Dunkerley, Jordan Slagowski, Paul Kahn, Jamie Ku, Tobias Funk

Proceedings Volume 9412, 94121W (2015) https://doi.org/10.1117/12.2081716

KEYWORDS: Sensors, X-rays, Collimators, X-ray detectors, Prototyping, X-ray imaging, Composites, Image sensors, Angiography, X-ray technology

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Proceedings Article | 18 March 2015 Paper

Monte Carlo simulation of inverse geometry x-ray fluoroscopy using a modified MC-GPU framework

Michael Tomkowiak, Michael Speidel, David A. Dunkerley, Jordan Slagowski, Tobias Funk, Bradley McCabe

Proceedings Volume 9412, 94120S (2015) https://doi.org/10.1117/12.2081684

KEYWORDS: X-rays, Monte Carlo methods, Sensors, X-ray detectors, Prototyping, Computer simulations, Optical spheres, Optical simulations, X-ray imaging, Collimators

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Showing 5 of 10 publications

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