Self-Supervised Deep Learning Framework for Rician Distribution Based Denoising and Modeling of Multi-b Prostate Diffusion MRI.

purposeConvolutional neural networks (CNNs) are evaluated for improved and accelerated denoising and Rician bias correction in multi-b DW images with simultaneous signal modeling.

methodsProstate diffusion images from 46 individuals acquired at 20 linearly distributed b-values (

resultsFor all signal models, synthetic DW images generated from CNN output are of similar quality compared to OBSIDIAN, a model-based method employing iterative pixel-wise fitting. Computed ADC and parameter maps obtained with CNN models were less noisy than those obtained with OBSIDIAN. Good quantitative agreement with OBSIDIAN in ROI-averaged ADC values and, to a slightly lesser extent, kurtosis model parameters was observed. Results with omitted Rician bias correction clearly deviate, while the impact of the noise map input for the CNNs is less pronounced. Computation time was reduced to seconds compared to several hours for OBSIDIAN.

conclusionThe CNN-based method has potential in clinical use for achieving higher quality DW images and biomarker maps with drastically reduced computation time. Further refinement and larger training datasets are needed to achieve better generalizability and increased robustness.