Fast Magnetic Resonance Imaging on Regions of Interest: From Sensing to Reconstruction

Liyan Sun, Hongyu Huang, Xinghao Ding, Yue Huang, Xiaoqing Liu, Yizhou Yu

Abstract: Magnetic Resonance Imaging (MRI) in a specific Region Of Interest (ROI) is valuable in detecting biomarkers for diagnosis, treatment, prognosis accurately. However, few existing methods study ROI in both data acquisition and image reconstruction when accelerating MRI by partial k-space measurements. Aiming at utilizing limited sampling resources efficiently on most relevant and desirable imaging contents in fast MRI, we propose a deep network framework called ROICSNet. With a learnable k-space sampler, an ad-hoc sampling pattern is adapted to a certain type of ROI organ. A cascaded Convolutional Neural Network (CNN) is used as the MR image reconstructor. By using a ROI prediction branch and a three-phase training strategy, the reconstructor is better guided onto the regions where radiologists hope to look closer. Experiments are performed on T1-modality abdominal MRI to demonstrate its state-of-the-art reconstruction accuracy compared with recent general and ROI-based fast MRI approaches. Our model achieves accurate imaging on fine details in ROI under a high accelerator factor and showed promise in real-world MRI application.