Denoising of hyperpolarized 13C MR images of the human brain using patch-based higher-order singular value decomposition

Purpose To improve hyperpolarized C-13 (HP-C-13) 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-C-13 MR imaging data was investigated. Image quality and the accuracy of quantitative analyses following denoising were evaluated first using simulated data of [1-C-13]pyruvate and its metabolic product, [1-C-13]lactate, and compared the results to a global HOSVD method. The patch-based HOSVD method was then applied to healthy volunteer HP [1-C-13]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-C-13]pyruvate and [1-C-13]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-C-13]lactate and [C-13]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.

Automated summary

The study investigated the use of patch-based higher-order singular value decomposition (HOSVD) method to denoise dynamic HP-C-13 MR imaging data, demonstrating significant improvement in image quality and quantification accuracy. The results suggest that this new approach has the potential to enhance sensitivity in metabolite dynamics quantification and improve image quality for future clinical research.