Tensor MP-PCA Denoising for Prostate MRI

Motivation: Prostate MRI primarily relies on diffusion-weighted imaging (DWI) but is notoriously challenged by low SNR, impacting the diagnostic process. Goal(s): To implement the state-of-the-art tensor denoising method for prostate DWIApproach: We applied the tMPPCA algorithm that makes use of the redundancy in multi-dimensional data to separate the most significant components (the diffusion-signal) and the remaining the thermal/scanner noise. We quantified the denoising efficacy with comprehensive qualitative and quantitative analysis. Results: The tMP-PCA method, previously proved to be efficient on ex-vivo scans are extremely effective to enhance in-vivo prostate MRI images when a similar multi-dimensional protocol is followed. Impact: The tMPPCA can effectively reduce noise without the trade-off of blurring—an achievement that has critical implications in cancer detection. This study is the first in-vivo implementation of tMPPCA for enhancing prostate DWI, employed under 10 minutes of scan time.