Improved lesion conspicuity on liver ADC maps using self supervised DDPMs

Motivation: Abdominal DW-MRI suffers from low SNR and motion artifacts, compromising ADC reliability. Goal(s): Improve ADC estimation in low SNR abdominal DW-MRI using a single-image acquisition per b-value. Approach: A novel self-supervised training approach using denoising diffusion probabilistic models (ssDDPM). Tailored for multi-b-value DW-MRI images, it requires only a single gradient image per b-value for denoising, which reduces scan time. Results: ssDDPM demonstrated superior lesion conspicuity in low b-value images and quantitative ADC maps in comparison to competing methods. In lesion versus normal tissue, a logistic classifier had improved sensitivity from 0.93 to 0.98, and specificity from 0.88 to 0.97, over non-denoised NEX1 images. Impact: ssDDPM enhances abdominal DW-MRI ADC accuracy from single acquisitions, reducing scan times and patient discomfort. This gives promise to an earlier, precise tumor detection and monitoring, impacting clinical care and healthcare efficiency.