Investigating the accuracy and precision of TE‐dependent versus multi‐echo QSM using Laplacian‐based methods at 3 T

Abstract

Purpose Multi‐echo gradient‐recalled echo acquisitions for QSM enable optimizing the SNR for several tissue types through multi‐echo (TE) combination or investigating temporal variations in the susceptibility (potentially reflecting tissue microstructure) by calculating one QSM image at each TE (TE‐dependent QSM). In contrast with multi‐echo QSM, applying Laplacian‐based methods (LBMs) for phase unwrapping and background field removal to single TEs could introduce nonlinear temporal variations (independent of tissue microstructure) into the measured susceptibility. Here, we aimed to compare the effect of LBMs on the QSM susceptibilities in TE‐dependent versus multi‐echo QSM. Methods TE–dependent recalled echo data simulated in a numerical head phantom and gradient‐recalled echo images acquired at 3 T in 10 healthy volunteers. Several QSM pipelines were tested, including four distinct LBMs: sophisticated harmonic artifact reduction for phase data (SHARP), variable‐radius sophisticated harmonic artifact reduction for phase data (V‐SHARP), Laplacian boundary value background field removal (LBV), and one‐step total generalized variation (TGV). Results from distinct pipelines were compared using visual inspection, summary statistics of susceptibility in deep gray matter/white matter/venous regions of interest, and, in the healthy volunteers, regional susceptibility bias analysis and nonparametric tests. Results Multi‐echo versus TE‐dependent QSM had higher regional accuracy, especially in high‐susceptibility regions and at shorter TEs. Everywhere except in the veins, a processing pipeline incorporating TGV provided the most temporally stable TE‐dependent QSM results with an accuracy similar to multi‐echo QSM. Conclusions For TE‐dependent QSM, carefully choosing LBMs can minimize the introduction of LBM‐related nonlinear temporal susceptibility variations.

Quantitative MRI susceptibility mapping reveals cortical signatures of changes in iron, calcium and zinc in malformations of cortical development in children with drug-resistant epilepsy

“…The magnetic susceptibility is estimated from the MRI signal phase, which is affected by the contributions from local microscopic tissue composition and macroscopic background fields which are removed via post-processing. QSM is also sensitive to acquisition parameters (particularly TE) (Biondetti et al, 2020) and tissue orientation with respect to the main magnetic field (Schweser et al, 2011; Wharton and Bowtell, 2012, 2015). Despite these limitations we found consistent differences in susceptibility values in the cortex.…”

“…QSM is also sensitive to acquisition parameters (particularly TE) (Biondetti et al, 2020) and tissue orientation with respect to the main magnetic field (Schweser et al, 2011;Bowtell, 2012, 2015). Despite these limitations we found consistent differences in susceptibility values in the cortex.…”

Investigating the Effect of Flow Compensation and Quantitative Susceptibility Mapping Method on the Accuracy of Venous Susceptibility Measurement

“…Because of the known effects of moving spins on signal phase, it has been suggested that GRE images acquired for venous QSM require full first-order (i.e., velocity) flow compensation (Brown et al, 2014). Indeed, in the absence of flow compensation, vessels containing flowing blood can appear displaced on GRE magnitude (Deistung et al, 2009), GRE phase (Xu et al, 2014), SWI (Deistung et al, 2009), and QSM (Biondetti et al, 2020) images, potentially affecting the accuracy of both image-based vessel segmentation and venous susceptibility estimation inside segmented vessels. Fully flow-compensated sequences aim to suppress the additional phase induced by flowing spins (velocities in veins: 10-25 cm/s) at each echo time (TE) and along all three signal encoding directions of the Cartesian k-space trajectory.…”

QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

“…This would allow us to extend the analysis done by Karsa et al 73 on the effects of FOV and anisotropic voxels sizes, or the analysis by Biondetti et al on the effects of Laplacian-based single echo versus multiple-echo techniques. 74 Although the simulation framework is useful for optimizing protocols, the simulation in its current form is a static one. Consequently, flow artifacts (which build up when a large number of echoes are used), respiration-related B 0 fluctuations, 75 and spatial distortions associated with the readout bandwidth are not considered.…”