A Self-Supervised Learning Framework for Under-Sampling Pattern Design Using Graph Convolution Network
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Purpose: To generate the under-sampling pattern using a self-supervised learning framework based on a graph convolutional network. Materials and Methods: We first decoded the k-space data into the graph and put it into the network. After the processing of graph convolution layers and graph pooling layers, the network generated the under-sampling pattern for MR reconstruction. We trained the network on the simulated brain dataset enabled by the selfsupervised learning strategy. We did simulation along with the in vivo brain and liver experiments under different noise levels and accelerating factors to compare the performance between the proposed method and traditional methods using the PSNR and SSIM index. Results: The simulation experiments showed that the proposed method can achieve the best performance with low accelerating factors (2 and 3) at all noise levels and in high accelerating factors (4 and 5) at high noise levels (50 and 70 dB). In in vivo experiments, the proposed method attained the highest PSNR and SSIM in the brain dataset as well as in the liver dataset after fine tuning on a small liver dataset. Conclusion: The self-supervised learning framework based on a graph convolutional network was able to design the under-sampling mask for MR reconstruction. The superior performance in the simulation and in vivo experiments demonstrated the feasibility and flexibility of the proposed method and its potential in clinical use.
Abstract
Purpose: To generate the under-sampling pattern using a self-supervised learning framework based on a graph convolutional network. Materials and Methods: We first decoded the k-space data into the graph and put it into the network. After the processing of graph convolution layers and graph pooling layers, the network generated the under-sampling pattern for MR reconstruction. We trained the network on the simulated brain dataset enabled by the selfsupervised learning strategy. We did simulation along with the in vivo brain and liver experiments under different noise levels and accelerating factors to compare the performance between the proposed method and traditional methods using the PSNR and SSIM index. Results: The simulation experiments showed that the proposed method can achieve the best performance with low accelerating factors (2 and 3) at all noise levels and in high accelerating factors (4 and 5) at high noise levels (50 and 70 dB). In in vivo experiments, the proposed method attained the highest PSNR and SSIM in the brain dataset as well as in the liver dataset after fine tuning on a small liver dataset. Conclusion: The self-supervised learning framework based on a graph convolutional network was able to design the under-sampling mask for MR reconstruction. The superior performance in the simulation and in vivo experiments demonstrated the feasibility and flexibility of the proposed method and its potential in clinical use.
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