Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM)
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 als...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Wiley
2021
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| Online Access: | https://hdl.handle.net/1721.1/129492 |
| _version_ | 1826199143607959552 |
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| author | Iyer, Siddharth(Siddharth Srinivasan) Adalsteinsson, Elfar Setsompop, Kawin Bilgic, Berkin |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Iyer, Siddharth(Siddharth Srinivasan) Adalsteinsson, Elfar Setsompop, Kawin Bilgic, Berkin |
| author_sort | Iyer, Siddharth(Siddharth Srinivasan) |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:15:04Z |
| format | Article |
| id | mit-1721.1/129492 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:15:04Z |
| publishDate | 2021 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1294922022-09-27T18:11:34Z Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM) Iyer, Siddharth(Siddharth Srinivasan) Adalsteinsson, Elfar Setsompop, Kawin Bilgic, Berkin Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Institute for Medical Engineering & Science 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. National Institutes of Health (U.S.) (Grants R01EB020613, R01EB019437, P41EB015896, U01EB025162, S10RR023401, S10RR019307, S10RR019254, S10RR023043)) 2021-01-21T16:44:38Z 2021-01-21T16:44:38Z 2020-12 2020-12-16T15:49:19Z Article http://purl.org/eprint/type/JournalArticle 0952-3480 https://hdl.handle.net/1721.1/129492 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) en 10.1002/NBM.4271 NMR in Biomedicine Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley PMC |
| spellingShingle | Iyer, Siddharth(Siddharth Srinivasan) Adalsteinsson, Elfar Setsompop, Kawin Bilgic, Berkin Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM) |
| title | Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM) |
| title_full | Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM) |
| title_fullStr | Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM) |
| title_full_unstemmed | Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM) |
| title_short | Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM) |
| title_sort | nonlinear dipole inversion ndi enables robust quantitative susceptibility mapping qsm |
| url | https://hdl.handle.net/1721.1/129492 |
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