Denoising of hyperpolarized 13C MR images of the human brain using patch‐based higher‐order singular value decomposition. Issue 5 (25th June 2021)

• Record Type: Journal Article
• Authors: Kim, Yaewon; Chen, Hsin‐Yu; Autry, Adam W.; Villanueva‐Meyer, Javier; Chang, Susan M.; Li, Yan; Larson, Peder E. Z.; Brender, Jeffrey R.; Krishna, Murali C.; Xu, Duan; Vigneron, Daniel B.; Gordon, Jeremy W.
• Is Part Of: Magnetic resonance in medicine. Volume 86:Issue 5(2021)
• Page Start: 2497
• Page End: 2511
• Publication Date: 2021-06-25
• Subjects: higher‐order singular value decomposition -- hyperpolarized C‐13 pyruvate -- image denoising; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28887
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To improve hyperpolarized 13 C (HP‐ 13 C) MRI by image denoising with a new approach, patch‐based higher‐order singular value decomposition (HOSVD). Methods: The benefit of using a patch‐based HOSVD method to denoise dynamic HP‐ 13 C MR imaging data was investigated. Image quality and the accuracy of quantitative analyses following denoising were evaluated first using simulated data of [1‐ 13 C]pyruvate and its metabolic product, [1‐ 13 C]lactate, and compared the results to a global HOSVD method. The patch‐based HOSVD method was then applied to healthy volunteer HP [1‐ 13 C]pyruvate EPI studies. Voxel‐wise kinetic modeling was performed on both non‐denoised and denoised data to compare the number of voxels quantifiable based on SNR criteria and fitting error. Results: Simulation results demonstrated an 8‐fold increase in the calculated SNR of [1‐ 13 C]pyruvate and [1‐ 13 C]lactate with the patch‐based HOSVD denoising. The voxel‐wise quantification of k PL (pyruvate‐to‐lactate conversion rate) showed a 9‐fold decrease in standard errors for the fitted k PL after denoising. The patch‐based denoising performed superior to the global denoising in recovering k PL information. In volunteer data sets, [1‐ 13 C]lactate and [ 13 C]bicarbonate signals became distinguishable from noise across captured time points with over a 5‐fold apparent SNR gain. This resulted in >3‐fold increase in the number of voxels quantifiable for mapping k PB (pyruvate‐to‐bicarbonate conversion rate) and whole brain coverage for mapping k PL . Conclusions: Sensitivity enhancement provided by this denoising significantly improved quantification of metabolite dynamics and could benefit future studies by improving image quality, enabling higher spatial resolution, and facilitating the extraction of metabolic information for clinical research.