SPECT Imaging Reconstruction Method Based on Deep Convolutional Neural Network

TL;DR

This paper introduces CNNR, a novel deep convolutional neural network-based method for SPECT image reconstruction, demonstrating improved image quality over traditional algorithms through extensive testing on software and hardware phantoms.

Contribution

The paper presents a new CNN-based reconstruction method for SPECT imaging, leveraging deep learning to enhance image quality compared to existing techniques.

Findings

CNNR outperforms FBP, MLEM, and OSEM in image quality.

Deep learning-based reconstruction improves SPECT imaging accuracy.

Extensive testing confirms CNNR's effectiveness on various phantoms.

Abstract

In this paper, we explore a novel method for tomographic image reconstruction in the field of SPECT imaging. Deep Learning methodologies and more specifically deep convolutional neural networks (CNN) are employed in the new reconstruction method, which is referred to as "CNN Reconstruction - CNNR". For training of the CNNR Projection data from software phantoms were used. For evaluation of the efficacy of the CNNR method, both software and hardware phantoms were used. The resulting tomographic images are compared to those produced by filtered back projection (FBP) [1], the "Maximum Likelihood Expectation Maximization" (MLEM) [1] and ordered subset expectation maximization (OSEM) [2].