Dataset Information

Iterative Restoration of the Fringe Phase (REFRASE) for QSM.

ABSTRACT: In quantitative susceptibility mapping (QSM), reconstructed results can be critically biased by misinterpreted or missing phase data near the edges of the brain support originating from the non-local relationship between field and susceptibility. These data either have to be excluded or corrected before further processing can take place. To address this, our iterative restoration of the fringe phase (REFRASE) approach simultaneously enhances the accuracy of multi-echo phase data QSM maps and the extent of the area available for evaluation. Data loss caused by strong local phase gradients near the surface of the brain support is recovered within the original phase data using harmonic and dipole-based fields extrapolated from a robust support region toward an extended brain mask. Over several iterations, phase data are rectified prior to the application of further QSM processing steps. The concept is successfully validated on numerical phantoms and brain scans from a cohort of volunteers. The increased extent of the mask and improved numerical stability within the segmented globus pallidus confirm the efficacy of the presented method in comparison to traditional evaluation.

SUBMITTER: Lindemeyer J

PROVIDER: S-EPMC8155380 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Iterative Restoration of the Fringe Phase (REFRASE) for QSM.

Lindemeyer Johannes J Worthoff Wieland A WA Shymanskaya Aliaksandra A Shah N Jon NJ

Frontiers in neuroscience 20210513

In quantitative susceptibility mapping (QSM), reconstructed results can be critically biased by misinterpreted or missing phase data near the edges of the brain support originating from the non-local relationship between field and susceptibility. These data either have to be excluded or corrected before further processing can take place. To address this, our iterative restoration of the fringe phase (REFRASE) approach simultaneously enhances the accuracy of multi-echo phase data QSM maps and the ...[more]

PMID: 34054401

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Project description:IntroductionReconstructing a bounded object from incomplete k-space data is a well posed problem, and it was recently shown that this incomplete spectrum approach can be used to reconstruct undersampled MRI images with similar quality to compressed sensing approaches. Here, we apply this incomplete spectrum approach to the field-to-source inverse problem encountered in quantitative magnetic susceptibility mapping (QSM). The field-to-source problem is an ill-posed problem because of conical regions in frequency space where the dipole kernel is zero or very small, which leads to the kernel's inverse being ill-defined. These "ill-posed" regions typically lead to streaking artifacts in QSM reconstructions. In contrast to compressed sensing, our approach relies on knowledge of the image-space support, more commonly referred to as the mask, of our object as well as the region in k-space with ill-defined values. In the QSM case, this mask is usually available, as it is required for most QSM background field removal and reconstruction methods.MethodsWe tuned the incomplete spectrum method (mask and band-limit) for QSM on a simulated dataset from the most recent QSM challenge and validated the QSM reconstruction results on brain images acquired in five healthy volunteers, comparing incomplete spectrum QSM to current state-of-the art-methods: FANSI, nonlinear dipole inversion, and conventional thresholded k-space division.ResultsWithout additional regularization, incomplete spectrum QSM performs slightly better than direct QSM reconstruction methods such as thresholded k-space division (PSNR of 39.9 vs. 39.4 of TKD on a simulated dataset) and provides susceptibility values in key iron-rich regions similar or slightly lower than state-of-the-art algorithms, but did not improve the PSNR in comparison to FANSI or nonlinear dipole inversion. With added (ℓ1-wavelet based) regularization the new approach produces results similar to compressed sensing based reconstructions (at sufficiently high levels of regularization).DiscussionIncomplete spectrum QSM provides a new approach to handle the "ill-posed" regions in the frequency-space data input to QSM.

Dataset Information

Iterative Restoration of the Fringe Phase (REFRASE) for QSM.

Publications

Iterative Restoration of the Fringe Phase (REFRASE) for QSM.

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