MR image reconstruction from undersampled data for image-guided radiation therapy using a patient-specific deep manifold image prior - Grandinetti, Jace; Gao, Yin; Gonzalez, Yesenia; Deng, Jie; Shen, Chenyang; Jia, Xun (Frontiers in Oncology, 2022)
Recent advancements in radiotherapy (RT) has allowed for the integration of a Magnetic Resonance (MR) imaging scanner with a medical linear accelerator to use MR images for image guidance to position tumors against the treatment beam. Undersampling in MR acquisition is desired to accelerate the imaging process, but unavoidably deteriorates the reconstructed image quality. In RT, a high-quality MR image of a patient is available for treatment planning. In light of this unique clinical scenario, we proposed to exploit the patient-specific image prior to facilitate high-quality MR image reconstruction. Utilizing the planning MR image, we established a deep auto-encoder to form a manifold of image patches of the patient. The trained manifold was then incorporated as a regularization to restore MR images of the same patient from undersampled data. We performed a simulation study using a patient case, a real patient study with three liver cancer patient cases, and a phantom experimental study using data acquired on an in-house small animal MR scanner. We compared the performance of the proposed method with those of the Fourier transform method, a tight-frame based Compressive Sensing method, and a deep learning method with a patient-generic manifold as the image prior. In the simulation study with 12.5% undersampling and 15% increase in noise, our method improved peak-signal-to-noise ratio by 4.56 dB and structural similarity index measure by 23% compared to the patient-generic manifold method. In the experimental study, our method outperformed others by producing reconstructions of visually improved image quality.