Deep Convolutional Neural Network for Low Projection SPECT Imaging Reconstruction

2021/08/09 by Charalambos Chrysostomou, Chrysostomou, Charalambos, Loizos Koutsantonis +5
Computer Science · Engineering · Medicine · #Advanced X-ray and CT Imaging #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Medical Imaging Techniques and Applications #Radiomics and Machine Learning in Medical Imaging #cs.AI

paper · pdf · doi:10.48550/arxiv.2108.03897

arxiv created 2021/08/09 · openalex publication_date 2021/08/09 · arxiv updated 2021/08/10 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

In this paper, we present a novel method for tomographic image reconstruction in SPECT imaging with a low number of projections. Deep convolutional neural networks (CNN) are employed in the new reconstruction method. Projection data from software phantoms were used to train the CNN network. For evaluation of the efficacy of the proposed method, software phantoms and hardware phantoms based on the FOV SPECT system were used. The resulting tomographic images are compared to those produced by the "Maximum Likelihood Expectation Maximisation" (MLEM).

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