Title | Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Balasubramanian PS, Spincemaille P, Guo L, Huang W, Kovanlikaya I, Wang Y |
Journal | iScience |
Volume | 23 |
Issue | 10 |
Pagination | 101553 |
Date Published | 2020 Oct 23 |
ISSN | 2589-0042 |
Abstract | 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. |
DOI | 10.1016/j.isci.2020.101553 |
Alternate Journal | iScience |
PubMed ID | 33083722 |
PubMed Central ID | PMC7522736 |
Grant List | S10 OD021782 / OD / NIH HHS / United States R01 NS105144 / NS / NINDS NIH HHS / United States R01 NS095562 / NS / NINDS NIH HHS / United States R01 CA181566 / CA / NCI NIH HHS / United States R01 NS090464 / NS / NINDS NIH HHS / United States R01 DK116126 / DK / NIDDK NIH HHS / United States |
Related Institute:
MRI Research Institute (MRIRI)