Boosting quantification accuracy of chemical exchange saturation transfer MRI with a spatial–spectral redundancy‐based denoising method

Abstract

Chemical exchange saturation transfer (CEST) is a versatile technique that enables noninvasive detections of endogenous metabolites present in low concentrations in living tissue. However, CEST imaging suffers from an inherently low signal‐to‐noise ratio (SNR) due to the decreased water signal caused by the transfer of saturated spins. This limitation challenges the accuracy and reliability of quantification in CEST imaging. In this study, a novel spatial–spectral denoising method, called BOOST (suBspace denoising with nOnlocal lOw‐rank constraint and Spectral local‐smooThness regularization), was proposed to enhance the SNR of CEST images and boost quantification accuracy. More precisely, our method initially decomposes the noisy CEST images into a low‐dimensional subspace by leveraging the global spectral low‐rank prior. Subsequently, a spatial nonlocal self‐similarity prior is applied to the subspace‐based images. Simultaneously, the spectral local‐smoothness property of Z‐spectra is incorporated by imposing a weighted spectral total variation constraint. The efficiency and robustness of BOOST were validated in various scenarios, including numerical simulations and preclinical and clinical conditions, spanning magnetic field strengths from 3.0 to 11.7 T. The results demonstrated that BOOST outperforms state‐of‐the‐art algorithms in terms of noise elimination. As a cost‐effective and widely available post‐processing method, BOOST can be easily integrated into existing CEST protocols, consequently promoting accuracy and reliability in detecting subtle CEST effects.

Whole‐cerebrum guanidino and amide CEST mapping at 3 T by a 3D stack‐of‐spirals gradient echo acquisition

“…[72][73][74][75][76] Traditional coregistration methods, such as using a reference frame at a specific offset 77 or using robust principal component analysis for low-rank approximation, 78 have been standard, exploiting the Z-spectrum's image set similarity. Recent advancements include denoising in subspace for spatial-spectral noise with nonlocal low-rank constraint, 79 and neural network-based denoising based on a model of two parallel modified U-Nets to extract global and spectral features. 80 Our study primarily used SPM12 to coregister all the offsets to the frame of 3.5 ppm (details of coregistration seen in Supporting information and Figure S2), and applied multilinear singular value decomposition 20 for further correction on spatial and spectral noise, which proved sufficient for our mostly motionless volunteers.…”

Highly‐accelerated CEST MRI using frequency‐offset‐dependent k‐space sampling and deep‐learning reconstruction

“…This property satisfies the assumption of PSF. Recently, utilization of the partially separable function has been explored in CEST MRI, 44,45 providing efficient ways to promoting accuracy and reliability in detecting subtle CEST effects. These works also provided useful information for guiding the optimization of deep learning networks in CEST MRI reconstruction.…”