Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping

Abstract

Summary Adaptive Total Field Inversion is described for quantitative susceptibility mapping (QSM) reconstruction from total field data through a spatially adaptive suppression of shadow artifacts through spatially adaptive regularization. The regularization for shadow suppression consists of penalizing low-frequency components of susceptibility in regions of small susceptibility contrasts as estimated by R2∗ derived signal intensity. Compared with a conventional local field method and two previously proposed regularized total field inversion methods, improvements were demonstrated in phantoms and subjects without and with hemorrhages. This algorithm, named TFIR, demonstrates the lowest error in numerical and gadolinium phantom datasets. In COSMOS data, TFIR performs well in matching ground truth in high-susceptibility regions. For patient data, TFIR comes close to meeting the quality of the reference local field method and outperforms other total field techniques in both clinical scores and shadow reduction.

Maximum spherical mean value filtering for whole‐brain QSM

“…Shadow artifact was quantified 48 as susceptibility variance within the gray‐matter mask and compared using a Wilcoxon signed‐rank test with significance level set at $$$ p<0.01 $$$. QSM accuracy was measured by correlation and slope obtained by linear regression between the reconstructions across major subcortical gray‐matter ROIs (globus pallidus, putamen, thalamus, and caudate nucleus).…”

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

“…183 Shadowing artifacts may arise from residual background fields. This shadowing may be reduced by improving background field removal such as harmonic incompatibility removal 102,184 and by suppressing slowly-varying spatial frequency components through regularization 185 or preconditioning. 55,181 Some algorithms do not incorporate spatial constraints for suppressing streaking and shadowing artifacts but explicitly modify the dipole kernel instead, 55,157 for example, thresholded k-space division.…”

“…Shadowing artifacts may arise from residual background fields. This shadowing may be reduced by improving background field removal such as harmonic incompatibility removal 102,184 and by suppressing slowly‐varying spatial frequency components through regularization 185 or preconditioning 55,181 …”