Joint Under-Sampling Pattern Optimization and Content-Based Reconstruction Network for Fast MRI Reconstruction

Magnetic Resonance Imaging (MRI) is frequently used by physicians for diagnosing human tissue, which requires focusing on specific content regions. However, existing compressed-sensing MRI (CS-MRI) reconstruction methods do not optimize under-sampling adaptively based on content or utilize k-space sensing resources effectively. To address these issues, we propose CBRecNet, a model that combines under-sampling pattern optimization with content-based reconstruction network. We extract multi-scale features of the content and calibrate the features of the reconstruction network using a pixel attention mechanism, which can guide the optimizable pattern to get a more suitable sampling pattern. To validate the proposed CBRecNet, we conduct extensive experiments on the CHAOS dataset with a four-stage learning strategy and the result demonstrates a very favorable outcome in CS-MRI. Our model provides a new approach to calibrated feature schemes based on meaningful medical content and demonstrates promising applications in CS-MRI.