A Convolutional Neural Network for SPECT Image Reconstruction

Purpose: Single photon emission computed tomography (SPECT) is considered as a functional nuclear medicine imaging technique which is commonly used in the clinic. However, it suffers from low resolution and high noise because of the physical structure and photon scatter and attenuation. This research aims to develop a compact neural network reconstructing SPECT images from projection data, with better resolution and low noise. Methods and Materials: This research developed a MATLAB program to generate 2-D brain phantoms. We totally generated 20,000 2-D phantoms and corresponding projection data. Furthermore, those projection data were processed with Gaussian filter and Poisson noise to simulate the real clinical situation. And 16,000 of them were used to train the neural network, 2,000 for validation, and the final 2,000 for testing. To simulate the real clinical situation, there are five groups of projection data with decreasing acquisition views are used to train the network. Inspired by the SPECTnet, we used a two-step training strategy for network design. The full-size phantom images (128×128 pixels) were compressed into a vector (256×1) at first, then they were decompressed to full-size images again. This process was achieved by the AutoEncoder (AE) consisting of encoder and decoder. The compressed vector generated by the encoder works as targets in the second network, which map projection to compressed images. Then those compressed vectors corresponding to the projection were reconstructed to full-size images by the decoder. Results: A total of 10,000 testing dataset divided into 5 groups with 360 degrees, 180 degrees, 150 degrees, 120 degrees and 90 degrees acquisition, respectively, are generated by the developed neural network. Results were compared with those generated by conventional FBP methods. Compared with FBP algorithm, the neural network can provide reconstruction images with high resolution and low noise, even if under the limited-angles acquisitions. In addition, the new neural network had a better performance than SPECTnet. Conclusions: The network successfully reconstruct projection data to activity images. Especially for the groups whose view angles is less than 180 degrees, the reconstruction images by neural network have the same excellent quality as other images reconstructed by projection data over 360 degrees, even has a higher efficiency than the SPECTnet. Keywords: SPECT; SPECT image reconstruction; Deep learning; convolution neural network. Purpose: Single photon emission computed tomography (SPECT) is considered as a functional nuclear medicine imaging technique which is commonly used in the clinic. However, it suffers from low resolution and high noise because of the physical structure and photon scatter and attenuation. This research aims to develop a compact neural network reconstructing SPECT images from projection data, with better resolution and low noise. Methods and Materials: This research developed a MATLAB program to generate 2-D brain phantoms. We totally generated 20,000 2-D phantoms and corresponding projection data. Furthermore, those projection data were processed with Gaussian filter and Poisson noise to simulate the real clinical situation. And 16,000 of them were used to train the neural network, 2,000 for validation, and the final 2,000 for testing. To simulate the real clinical situation, there are five groups of projection data with decreasing acquisition views are used to train the network. Inspired by the SPECTnet, we used a two-step training strategy for network design. The full-size phantom images (128×128 pixels) were compressed into a vector (256×1) at first, then they were decompressed to full-size images again. This process was achieved by the AutoEncoder (AE) consisting of encoder and decoder. The compressed vector generated by the encoder works as targets in the second network, which map projection to compressed images. Then those compressed vectors corresponding to the projection were reconstructed to full-size images by the decoder. Results: A total of 10,000 testing dataset divided into 5 groups with 360 degrees, 180 degrees, 150 degrees, 120 degrees and 90 degrees acquisition, respectively, are generated by the developed neural network. Results were compared with those generated by conventional FBP methods. Compared with FBP algorithm, the neural network can provide reconstruction images with high resolution and low noise, even if under the limited-angles acquisitions. In addition, the new neural network had a better performance than SPECTnet. Conclusions: The network successfully reconstruct projection data to activity images. Especially for the groups whose view angles is less than 180 degrees, the reconstruction images by neural network have the same excellent quality as other images reconstructed by projection data over 360 degrees, even has a higher efficiency than the SPECTnet. Keywords: SPECT; SPECT image reconstruction; Deep learning; convolution neural network.