Dr. Barry A. Siegel Profile

Dr. Barry A. Siegel

at Mallinckrodt Institute of Radiology

SPIE Involvement:

Author

Publications (6)

Proceedings Article | 29 March 2024 Presentation + Paper

Can patient-specific acquisition protocol improve performance on defect detection task in myocardial perfusion SPECT?

Nu Ri Choi, Md Ashequr Rahman, Zitong Yu, Barry Siegel, Abhinav Jha

Proceedings Volume 12929, 129290O (2024) https://doi.org/10.1117/12.3006924

KEYWORDS: Anatomy, Defect detection, Medical image reconstruction, Information operations, Single photon emission computed tomography, Breast, Computed tomography, Perfusion imaging, Image quality, Design

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Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical characteristics, such as large breasts, images acquired using conventional protocols are often sub-optimal in quality, leading to degraded diagnostic accuracy. Solutions to improve image quality for these patients outside of increased dose or total acquisition time remain challenging. Thus, there is an important need for new methodologies that can help improve the quality of the acquired images for such patients, in terms of the ability to detect myocardial perfusion defects. One approach to improving this performance is adapting the image acquisition protocol specific to each patient. Studies have shown that in MPS, different projection angles usually contain varying amounts of information for the detection task. However, current clinical protocols spend the same time at each projection angle. In this work, we evaluated whether an acquisition protocol that is optimized for each patient could improve performance on the task of defect detection on reconstructed images for patients with outlier anatomical characteristics. For this study, we first designed and implemented a personalized patient-specific protocol-optimization strategy, which we term precision SPECT (PRESPECT). This strategy integrates the theory of ideal observers with the constraints of tomographic reconstruction to optimize the acquisition time for each projection view, such that performance on the task of detecting myocardial perfusion defects is maximized. We performed a clinically realistic simulation study on patients with outlier anatomies on the task of detecting perfusion defects on various realizations of low-dose scans by an anthropomorphic channelized Hotelling observer. Our results show that using PRESPECT led to improved performance on the defect detection task for the considered patients. These results provide evidence that personalization of MPS acquisition protocol has the potential to improve defect detection performance on reconstructed images by anthropomorphic observers for patients with outlier anatomical characteristics. Thus, our findings motivate further research to design optimal patient-specific acquisition and reconstruction protocols for MPS, as well as developing similar approaches for other medical imaging modalities.

Proceedings Article | 7 April 2023 Presentation + Paper

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT

Zitong Yu, Md Ashequr Rahman, Craig Abbey, Barry Siegel, Abhinav Jha

Proceedings Volume 12463, 124631E (2023) https://doi.org/10.1117/12.2654500

KEYWORDS: Single photon emission computed tomography, Signal attenuation, Computed tomography, Image segmentation, Medical image reconstruction, Attenuation, Defect detection

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Proceedings Article | 3 April 2023 Poster + Paper

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT

Ashequr Rahman, Zitong Yu, Barry Siegel, Abhinav Jha

Proceedings Volume 12467, 1246719 (2023) https://doi.org/10.1117/12.2655629

KEYWORDS: Denoising, Single photon emission computed tomography, Defect detection, Image processing, Image filtering, Visual system, Artificial intelligence, Deep learning

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