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

Ashequr Rahman; Zitong Yu; Barry A. Siegel; Abhinav K. Jha

doi:10.1117/12.2655629

3 April 2023 A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT

Ashequr Rahman, Zitong Yu, Barry A. Siegel, Abhinav K. Jha

Proceedings Volume 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment; 1246719 (2023) https://doi.org/10.1117/12.2655629
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Deep-learning (DL)-based methods have shown significant promise in denoising myocardial perfusion SPECT images acquired at low dose. For clinical application of these methods, evaluation on clinical tasks is crucial. Typically, these methods are designed to minimize some fidelity-based criterion between the predicted denoised image and some reference normal-dose image. However, while promising, studies have shown that these methods may have limited impact on the performance of clinical tasks in SPECT. To address this issue, we use concepts from the literature on model observers and our understanding of the human visual system to propose a DL-based denoising approach designed to preserve observer-related information for detection tasks. The proposed method was objectively evaluated on the task of detecting perfusion defect in myocardial perfusion SPECT images using a retrospective study with anonymized clinical data. Our results demonstrate that the proposed method yields improved performance on this detection task compared to using low-dose images. The results show that by preserving task-specific information, DL may provide a mechanism to improve observer performance in low-dose myocardial perfusion SPECT.

Citation Download Citation

Ashequr Rahman, Zitong Yu, Barry A. Siegel, and Abhinav K. Jha "A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT", Proc. SPIE 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, 1246719 (3 April 2023); https://doi.org/10.1117/12.2655629

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available