Edge-preserving, CNN-based, denoising in low dose SPECT myocardial perfusion imaging

Mehdi Toumi; Yongyi Yang; P. Hendrik Pretorius; Michael A. King; Jovan G. Brankov

doi:10.1117/12.3006904

2 April 2024 Edge-preserving, CNN-based, denoising in low dose SPECT myocardial perfusion imaging

Mehdi Toumi, Yongyi Yang, P. Hendrik Pretorius, Michael A. King, Jovan G. Brankov

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129262Y (2024) https://doi.org/10.1117/12.3006904
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

The purpose of this research is to address the critical challenge of improving the detectability of small perfusion defects in deep learning (DL) denoising for low dose Myocardial Perfusion Imaging (MPI) with Single-Photon Emission Computed Tomography (SPECT). By developing a 3D convolutional auto-encoder (CAE) incorporated with an edge-preservation mechanism, the study aims to mitigate potential blurring effects associated with DL-based denoising methods. The CAE is optimized to enhance noise reduction on low-dose SPECT-MPI scans while seeking to maintain the integrity of image-edge features which are vital for preserving subtle myocardial perfusion defects after denoising.

Citation Download Citation

Mehdi Toumi, Yongyi Yang, P. Hendrik Pretorius, Michael A. King, and Jovan G. Brankov "Edge-preserving, CNN-based, denoising in low dose SPECT myocardial perfusion imaging", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129262Y (2 April 2024); https://doi.org/10.1117/12.3006904

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