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An AI Approach for Reducing Residual Motion and Noise from Myocardial Perfusion for Assessment of Coronary Artery Disease

Published onJun 08, 2021
An AI Approach for Reducing Residual Motion and Noise from Myocardial Perfusion for Assessment of Coronary Artery Disease
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Abstract

Over the last two decades, image denoising has achieved significant performance improvements. However, the underlying idea of losing meaningful image information during filtering does not suit medical images well, where every voxel is essential. For highly radio-sensitive organs, such as hearts, modalities such as CT must use low-dose radiation. As a result, low-quality CT images are produced, making the diagnosis difficult. In myocardial perfusion imaging, cardiac imaging's residual motion also severely affects the diagnosis, requiring an accurate deformable registration algorithm. We propose a deep learning model to concurrently reduce noise and residual activities for low-dose myocardial CT perfusion without compromising the image information.

Article ID: 2021G05

Month: May

Year: 2021

Address: Online

Venue: Graduate Student Symposium- Canadian Conference on Artificial Intelligence

Publisher: Canadian Artificial Intelligence Association

URL:https://caiac.pubpub.org/pub/hg2up97e/

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