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

Abstract

Purpose To improve hyperpolarized 13C (HP‐13C) 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‐13C MR imaging data was investigated. Image quality and the accuracy of quantitative analyses following denoising were evaluated first using simulated data of [1‐13C]pyruvate and its metabolic product, [1‐13C]lactate, and compared the results to a global HOSVD method. The patch‐based HOSVD method was then applied to healthy volunteer HP [1‐13C]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 [1‐13C]pyruvate and [1‐13C]lactate with the patch‐based HOSVD denoising. The voxel‐wise quantification of kPL (pyruvate‐to‐lactate conversion rate) showed a 9‐fold decrease in standard errors for the fitted kPL after denoising. The patch‐based denoising performed superior to the global denoising in recovering kPL information. In volunteer data sets, [1‐13C]lactate and [13C]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 kPB (pyruvate‐to‐bicarbonate conversion rate) and whole brain coverage for mapping kPL. 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.

Lactate saturation limits bicarbonate detection in hyperpolarized 13C‐pyruvate MRI of the brain

“…The in vivo 13 C data were reconstructed and analyzed in MATLAB (MathWorks). After gridding, Fourier transformation, and denoising, 17 the SNR of the area under the curve of each metabolite was measured over the entire organ (brain, kidney, liver, or heart with blood pool). For quantification of apparent [1- 13 C]pyruvate-to-[ 13 C]bicarbonate metabolism (k PB ), a metabolic exchange model was fitted if SNR was > 3.…”

Kinetic analysis of multi‐resolution hyperpolarized 13C human brain MRI to study cerebral metabolism

“…Recent technical advances for HP studies have shown that metabolic quantification could be further improved through specialized acquisition and denoising techniques. [18][19][20][21] These techniques provide improved SNR and finer spatial resolution, enabling better localization of metabolism. In particular, a multi-resolution HP 13 C EPI approach was developed that enables the acquisition of the injected HP 13 C pyruvate at a higher spatial resolution than its metabolic products lactate and bicarbonate with their lower inherent SNR.…”

Deep Learning‐Based Diffusion‐Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction

“…Wu et al [ 23 ] used the HOSVD algorithm super resolution (SR) technology to reconstruct DWI datasets with high discrimination rate and achieved good results, which reflected that the improved HOSVD algorithm had a good effect on DWI image denoising, which was consistent with the results of this work. The HOSVD algorithm has also been applied to other imaging technologies, such as PET [ 24 ], hyperpolarized 13C magnetic resonance image [ 25 ], and CT [ 26 ], all of which have achieved good research results.…”