Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer

Convolutional neural networks (CNNs) have been extremely successful in various medical imaging tasks. However, because the size of the convolutional kernel used in a CNN is much smaller than the image size, CNN has a strong spatial inductive bias and lacks a global understanding of the input images. Vision Transformer, a recently emerged network structure in computer vision, can potentially overcome the limitations of CNNs for image-reconstruction tasks. In this work, we proposed a slice-by-slice Transformer network (SSTrans-3D) to reconstruct cardiac single-photon emission computed tomography (SPECT) images from 3-D few-angle data. To be specific, the network reconstructs the whole 3-D volume using a slice-by-slice scheme. By doing so, SSTrans-3D alleviates the memory burden required by 3-D reconstructions using Transformer. The network can still obtain a global understanding of the image volume with the Transformer attention blocks. Finally, already reconstructed slices are used as the input to the network so that SSTrans-3D can potentially obtain more informative features from these slices. Validated on porcine, phantom, and human studies acquired using a GE dedicated cardiac SPECT scanner, the proposed method produced images with clearer heart cavity, higher cardiac defect contrast, and more accurate quantitative measurements on the testing data as compared with a deep U-net.