SPECT Imaging Reconstruction Method Based on Deep Convolutional Neural Network
This work addresses image reconstruction for SPECT imaging, potentially improving medical diagnostics, but appears incremental as it applies an existing deep learning approach to a specific domain without claiming broad breakthroughs.
The paper tackles the problem of tomographic image reconstruction in SPECT imaging by proposing a novel deep convolutional neural network method called CNNR, which is trained on software phantom data and evaluated against traditional methods like FBP, MLEM, and OSEM using both software and hardware phantoms, though no concrete numerical results are provided in the 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].