Author: Mary Feng, Yi Lao, Hui Lin, Hengjie Liu, Xin Miao, Michael Ohliger 👨🔬
Affiliation: University of California, Los Angeles, Department of Radiation Oncology, University of California San Francisco, Department of Radiation Oncology, City of Hope National Medical Center, University of California San Francisco, Siemens Medical Solutions USA Inc. 🌍
Purpose: 4D MRI with high spatiotemporal resolution is vital to characterize the tumor/tumor motion for liver radiotherapy. However, high-quality 4D MRI requires an impractically long scanning time for dense k-space signal acquisition covering all respiratory phases. Accelerated sparse sampling followed by reconstruction enhancement is desired but often results in degraded image quality and long reconstruction time. We hereby propose the chained iterative reconstruction network (CIRNet) for efficient sparse-sampling reconstruction while maintaining clinically deployable quality.
Methods: CIRNet adopts the denoising diffusion probabilistic framework to condition the image reconstruction through a stochastic iterative denoising process. During training, a forward Markovian diffusion process is designed to gradually add Gaussian noise to the densely sampled ground truth (GT), while CIRNet is optimized to iteratively reverse the Markovian process from the forward outputs. Per inference, CIRNet is scheduled to solely run the reverse process to recover signals from noise, conditioned upon the undersampled input. CIRNet is structured with a U-Net architecture, optimized to minimize the difference between estimated and GT noises. CIRNet processed the 4D data (3D+t) as temporal slices (2D+t). The proposed framework is evaluated on a data cohort consisting of 48 patients (12332 temporal slices) who underwent free-breathing liver 4D MRI. 3-, 6-, 10-, 20- and 30-times acceleration were examined with retrospective random undersampling scheme. Compressed sensing (CS) reconstruction with a spatiotemporal constraint and a recently proposed deep network, Re-Con-GAN, are selected as baselines.
Results: CIRNet consistently achieved superior performance compared to CS and Re-Con-GAN (e.g., PNSR of CIRNet, CS and Re-Con-GAN is at 22.35+/-2.94, 13.27.89+/-3.89 and 15.89+/-3.65 dB in 30 times acceleration). The inference time of CIRNet, CS and Re-Con-GAN are 11s, 120s and 0.15s.
Conclusion: A robust framework operates under stochastic iterative refinement is presented to facilitate adaptive MR-guided liver radiotherapy with promising outcomes demonstrated on an in-house dataset.