Quantitative susceptibility mapping (QSM) with an extended physical model for MRI frequency contrast in the brain: a proof‐of‐concept of quantitative susceptibility and residual (QUASAR) mapping

Abstract

Quantitative susceptibility mapping (QSM) aims to calculate the tissue’s magnetic susceptibility distribution from its perturbing effect on the MRI’s static main magnetic field. The method is increasingly being applied to study iron and myelin in clinical and preclinical settings. However, recent experimental and theoretical findings challenged the fundamental theoretical assumptions that form the basis of current numerical implementations of QSM algorithms. The present work introduces a new class of susceptibility mapping algorithms, termed QUAntitative Susceptibility And Residual mapping (QUASAR), that takes into account frequency contributions not related to the spatial variation of bulk magnetic susceptibility in the Lorentz sphere model. We present a simple proof-of-concept QUASAR algorithm that, unlike most of the QSM algorithms currently used widely, results in an improved anatomical accuracy of the susceptibility distribution without any a priori assumptions about the susceptibility distribution during the field-to-source inversion. The algorithm was evaluated both in silico and in vivo in the preclinical setting. Our preliminary application of QUASAR in rodents provides the first in vivo evidence that the susceptibility-field model traditionally used in the QSM field cannot fully explain the frequency contrast in brain tissues. Only when an additional local frequency contribution is added to the physical model, the frequency contrast in the brain can be related properly to the underlying anatomy.

Visualization of thalamic calcium influx with quantitative susceptibility mapping as a potential imaging biomarker for repeated mild traumatic brain injury

“…We zero-padded the raw k-space to achieve a nominal isotropic spatial resolution of 96 µm. Susceptibility maps were reconstructed from these data with best-path phase unwrapping (Abdul-Rahman et al, 2007), multi-echo multi-channel phase combination (Robinson et al, 2011), R 2 *-weighted field-mapping (Wu et al, 2012), V-SHARP (Özbay et al, 2017;Schweser et al, 2011;Wu et al, 2011), and QUASAR-HEIDI (Schweser and Zivadinov, 2018). For improved anatomical contrast on magnitude images, we averaged all magnitude images across different echo times.…”

QSM Reconstruction Challenge 2.0: a realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

“…One avenue to obtain higher resolution DWI data are novel 3D multi-slab acquisition with gradient or rf encoding (51,52) that have significant SNR improvements in respect to conventional 2D-SMS as used in this work. Here we have simply interpolated our 1.5mm DWI to the anatomical space and expect this to be sufficient to develop and validate QSM methods that account for microstructural effects in white matter (53).…”

DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping

“…Thus, a deep learning approach incorporating additional measurements, such as diffusion information, could deliver more accurate results in anisotropic white matter regions, because the susceptibility contrast depends on white matter fiber orientation (Li et al, 2012). Utilizing a more sophisticated model for QSM was recently proposed in the QUASAR approach (Schweser and Zivadinov, 2018) and implemented in DEEPOLE that separates the magnetic field into two components originating from different contrast mechanisms and yields an improved susceptibility map accounting for microstructural anisotropy (Jochmann et al, 2019).…”