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

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.