Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM)
Name
nihms-1627057.pdf
Description
Accepted version
Size
2.19 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Iyer, Siddharth(Siddharth Srinivasan)
Adalsteinsson, Elfar
Setsompop, Kawin
Bilgic, Berkin
Date Issued
December 2020
Journal
NMR in Biomedicine
Publisher
Wiley
Citation
Polak, Daniel et al. “Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM).” NMR in Biomedicine, 33, 12 (December 2020): e4271 © 2020 The Author(s)
Version
Author's final manuscript
Abstract
We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-the-art reconstruction techniques. In addition to avoiding over-smoothing that these techniques often suffer from, we also obviate the need for parameter selection. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7 T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1002/NBM.4271
