Accelerating Low-Field Prostate DWI: Self-Supervised Denoising for Rapid Scan Acquisition

Motivation: Diffusion-weighted imaging (DWI) is crucial for lesion detection but suffers from inherently low signal-to-noise ratio (SNR), especially in low-field settings. Goal(s): The goal of this work is to accelerate low-field prostate DWI, reducing the number of image repetitions and scan time while maintaining image quality. Approach: We present a self-supervised denoising method employing Stein's unbiased risk estimator (SURE) and a physics-based noise model and evaluate the denoising results without relying on ground-truth data. Results: Our method excels in preserving image content, outperforming other denoising techniques. This allows a substantial reduction in scan time, making it a promising advancement in low-field DWI. Impact: Our proposed denoising approach accelerates low-field prostate DWI via self-supervised denoising, improving scan efficiency without compromising image quality. We further demonstrate how to employ a physics-based noise model to evaluate denoising performance in the absence of noise-free ground-truth data.