The Effect of Oblique Image Acquisition on the Accuracy of Quantitative Susceptibility Mapping and a Robust Tilt Correction Method

Abstract

PurposeQuantitative susceptibility mapping (QSM) is increasingly used for clinical research where oblique image acquisition is commonplace but its effects on QSM accuracy are not well understood.Theory and MethodsThe QSM processing pipeline involves defining the unit magnetic dipole kernel, which requires knowledge of the direction of the main magnetic field with respect to the acquired image volume axes. The direction of is dependent upon the axis and angle of rotation in oblique acquisition. Using both a numerical brain phantom and in-vivo acquisitions, we analysed the effects of oblique acquisition on magnetic susceptibility maps. We compared three tilt correction schemes at each step in the QSM pipeline: phase unwrapping, background field removal and susceptibility calculation, using the root-mean-squared error and QSM-tuned structural similarity index (XSIM).ResultsRotation of wrapped phase images gave severe artefacts. Background field removal with projection onto dipole fields gave the most accurate susceptibilities when the field map was first rotated into alignment with . LBV and VSHARP background field removal methods gave accurate results without tilt correction. For susceptibility calculation, thresholded k-space division, iterative Tikhonov regularisation and weighted linear total variation regularisation all performed most accurately when local field maps were rotated into alignment with before susceptibility calculation.ConclusionFor accurate QSM, oblique acquisition must be taken into account. Rotation of images into alignment with should be carried out after phase unwrapping and before background field removal. We provide open-source tilt-correction code to incorporate easily into existing pipelines: https://github.com/o-snow/QSM_TiltCorrection.git.

Multisite reproducibility of quantitative susceptibility mapping and effective transverse relaxation rate in deep gray matter at 3 T using locally optimized sequences in 24 traveling heads

“…However, in quantifying small structures (such as SN and RN), resolution and slice orientation can cause substantial variability due to irreversible partial volume effects. Differences in slice orientation can also affect other regions if not properly handled during QSM postprocessing, as some background removal and susceptibility inversion methods have been shown to be sensitive to variation in slice orientation, and thus prealigning slices might be required 63 . The methods applied in this study (i.e., V‐SHARP background removal and iterative total variation‐based inversions) are not susceptible to variation in slice orientation.…”

Investigating the effect of oblique image acquisition on the accuracy of QSM and a robust tilt correction method

“…Calculation of $$$ {d}_z\left(\boldsymbol{k}\right) $$$ requires knowledge of the “ z ” direction of the main magnetic field, $$$ {\mathbf{B}}_{\mathbf{0}} $$$, with respect to the image volume acquired. Therefore, oblique acquisition must be taken into account within the QSM pipeline; otherwise, incorrect $$$ \chi $$$ estimates arise, as suggested by a preliminary study 8 and our preliminary data 9 . With the increase in clinical applications of QSM, 10,11 accuracy in $$$ \chi $$$ estimates for oblique acquisition, typical in clinical protocols, is of paramount importance in ensuring smooth translation of QSM into clinical practice.…”

A Comparison of MRI Quantitative Susceptibility Mapping and TRUST-Based Measures of Brain Venous Oxygen Saturation in Sickle Cell Anaemia

“…Maps of magnetic susceptibility ( χ ) were calculated from each of the three MPM FLASH acquisitions in their own native space via the following pipeline: total field maps were estimated from a non-linear fit of the complex multi-echo images ( Liu et al, 2013 ), residual wraps in the field map were removed using SEGUE phase unwrapping ( Karsa and Shmueli 2018 ), brain masks were estimated using FSL brain extraction tool ( Smith 2002 ), to account for oblique acquisition images were realigned with the B 0 direction ( Kiersnowski et al, 2021 ) prior to background field removal via projection onto dipole fields ( Liu et al, 2011 ) and susceptibility maps were calculated using the iterative Tikhonov method ( Karsa et al, 2020 ) ( Figure 1 ). To account for head movement between acquisitions, the PD- and MT-weighted images were rigidly transformed into the T 1 -w native space using NiftyReg ( Modat et al, 2014 ).…”

“…Frontiers in Physiology | www.frontiersin.org August 2022 | Volume 13 | Article 913443 estimated using FSL brain extraction tool (Smith 2002), to account for oblique acquisition images were realigned with the B 0 direction (Kiersnowski et al, 2021) prior to background field removal via projection onto dipole fields (Liu et al, 2011) and susceptibility maps were calculated using the iterative Tikhonov method (Karsa et al, 2020) (Figure 1). To account for head movement between acquisitions, the PD-and MT-weighted images were rigidly transformed into the T 1 -w native space using NiftyReg (Modat et al, 2014).…”