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Abstract #4677

Tensor MP-PCA Denoising for Prostate MRI

Batuhan Gundogdu1, Aritrick Chatterjee1, Benan Akca2, Grace Lee1, Nisa C Oren1, Gregory S Karczmar1, and Aytekin Oto1
1University of Chicago, Chicago, IL, United States, 2Marmara University, Istanbul, Turkey

Synopsis

Keywords: Software Tools, Diffusion/other diffusion imaging techniques

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 DWI

Approach: 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.

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Keywords