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.

Thin slab quantitative susceptibility mapping

“…To eliminate unreliable regions while maximizing coverage near sinuses, we masked the phase information at each TE using the corresponding magnitude mask prior to computing the total field map. Alternatively, one might try to estimate the unreliable regions iteratively using forward dipole field modeling 38 . In general, QSM is sensitive to the definition of the VOI boundaries (i.e., brain mask) and thus an inherent variation is expected in the proposed method due to masking differences between the low‐ and high‐res volumes.…”

“…Alternatively, one might try to estimate the unreliable regions iteratively using forward dipole field modeling. 38 In general, QSM is sensitive to the definition of the VOI boundaries (i.e., brain mask) and thus an inherent variation is expected in the proposed method due to masking differences between the low-and high-res volumes. Minimizing the difference between the masks of the two data sets would further improve the accuracy and thus investigating better masking approaches is warranted, including deep learning-based segmentation.…”

Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro‐magnetic tissue properties study group

“…Some exact phase unwrapping algorithms also provide phase-based quality maps, 100,128 which can again be thresholded to identify voxels within the brain with unreliable phase values, and to provide a better estimation of the brain boundary. 111,122,129 Masking imperfections may result in small exclusion areas ("holes") in the ROI that need special attention. It is important to fill all holes, as the background field removal algorithms will suppress any susceptibility sources within these holes.…”

“…To fine‐tune the threshold value, it may be needed to inspect both the background field removal and dipole inversion results, as these will more easily reveal noisy voxels and artifacts arising from unmasked unreliable regions. Some exact phase unwrapping algorithms also provide phase‐based quality maps, 100,128 which can again be thresholded to identify voxels within the brain with unreliable phase values, and to provide a better estimation of the brain boundary 111,122,129 …”