Data-Driven Quantitative Susceptibility Mapping Using Loss Adaptive Dipole Inversion (LADI).

Journal of Magnetic Resonance Imaging : JMRI
Srikant Kamesh IyerWalter R Witschey

Abstract

Quantitative susceptibility mapping (QSM) uses prior information to reconstruct maps, but prior information may not show pathology and introduce inconsistencies with susceptibility maps, degrade image quality and inadvertently smoothing image features. To develop a local field data-driven QSM reconstruction that does not depend on spatial edge prior information. Retrospective. A dataset from 2016 ISMRM QSM Challenge, 11 patients with glioblastoma, a patient with microbleeds and porcine heart. 3D gradient echo sequence on 3T and 7T scanners. Accuracy was compared to Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS), and several published techniques using region of interest (ROI) measurements, root-mean-squared error (RMSE), structural similarity index metric (SSIM), and high-frequency error norm (HFEN). Numerical ranking and semiquantitative image grading was performed by three expert observers to assess overall image quality (IQ) and image sharpness (IS). Bland-Altman, Friedman test, and Conover multiple comparisons. Loss adaptive dipole inversion (LADI) (β = 0.82, R2 = 0.96), morphology-enabled dipole inversion (MEDI) (β = 0.91, R2 = 0.97), and fast nonlinear susceptibility inversion (FANSI) (β = 0.8...Continue Reading

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Citations

Aug 7, 2021·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Brianna F MoonWalter R Witschey

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