Single-step quantitative susceptibility mapping with variational penalties: Single-Step Qsm with Variational Penalties
Copyright © 2016 John Wiley & Sons, Ltd. Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the gradient echo (GRE) phase signal through background phase removal and dipole inversion steps. Each of these steps typically requires the solution...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021
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| Online Access: | https://hdl.handle.net/1721.1/135703 |
| _version_ | 1826210036101152768 |
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| author | Chatnuntawech, Itthi McDaniel, Patrick Cauley, Stephen F Gagoski, Borjan A Langkammer, Christian Martin, Adrian Grant, P Ellen Wald, Lawrence L Setsompop, Kawin Adalsteinsson, Elfar Bilgic, Berkin |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Chatnuntawech, Itthi McDaniel, Patrick Cauley, Stephen F Gagoski, Borjan A Langkammer, Christian Martin, Adrian Grant, P Ellen Wald, Lawrence L Setsompop, Kawin Adalsteinsson, Elfar Bilgic, Berkin |
| author_sort | Chatnuntawech, Itthi |
| collection | MIT |
| description | Copyright © 2016 John Wiley & Sons, Ltd. Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the gradient echo (GRE) phase signal through background phase removal and dipole inversion steps. Each of these steps typically requires the solution of an ill-posed inverse problem and thus necessitates additional regularization. Recently developed single-step QSM algorithms directly relate the unprocessed GRE phase to the unknown susceptibility distribution, thereby requiring the solution of a single inverse problem. In this work, we show that such a holistic approach provides susceptibility estimation with artifact mitigation and develop efficient algorithms that involve simple analytical solutions for all of the optimization steps. Our methods employ total variation (TV) and total generalized variation (TGV) to jointly perform the background removal and dipole inversion in a single step. Using multiple spherical mean value (SMV) kernels of varying radii permits high-fidelity background removal whilst retaining the phase information in the cortex. Using numerical simulations, we demonstrate that the proposed single-step methods reduce the reconstruction error by up to 66% relative to the multi-step methods that involve SMV background filtering with the same number of SMV kernels, followed by TV- or TGV-regularized dipole inversion. In vivo single-step experiments demonstrate a dramatic reduction in dipole streaking artifacts and improved homogeneity of image contrast. These acquisitions employ the rapid three-dimensional echo planar imaging (3D EPI) and Wave-CAIPI (controlled aliasing in parallel imaging) trajectories for signal-to-noise ratio-efficient whole-brain imaging. Herein, we also demonstrate the multi-echo capability of the Wave-CAIPI sequence for the first time, and introduce an automated, phase-sensitive coil sensitivity estimation scheme based on a 4-s calibration acquisition. Copyright © 2016 John Wiley & Sons, Ltd. |
| first_indexed | 2024-09-23T14:40:22Z |
| format | Article |
| id | mit-1721.1/135703 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:40:22Z |
| publishDate | 2021 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1357032023-10-13T20:18:45Z Single-step quantitative susceptibility mapping with variational penalties: Single-Step Qsm with Variational Penalties Chatnuntawech, Itthi McDaniel, Patrick Cauley, Stephen F Gagoski, Borjan A Langkammer, Christian Martin, Adrian Grant, P Ellen Wald, Lawrence L Setsompop, Kawin Adalsteinsson, Elfar Bilgic, Berkin Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Harvard University--MIT Division of Health Sciences and Technology Copyright © 2016 John Wiley & Sons, Ltd. Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the gradient echo (GRE) phase signal through background phase removal and dipole inversion steps. Each of these steps typically requires the solution of an ill-posed inverse problem and thus necessitates additional regularization. Recently developed single-step QSM algorithms directly relate the unprocessed GRE phase to the unknown susceptibility distribution, thereby requiring the solution of a single inverse problem. In this work, we show that such a holistic approach provides susceptibility estimation with artifact mitigation and develop efficient algorithms that involve simple analytical solutions for all of the optimization steps. Our methods employ total variation (TV) and total generalized variation (TGV) to jointly perform the background removal and dipole inversion in a single step. Using multiple spherical mean value (SMV) kernels of varying radii permits high-fidelity background removal whilst retaining the phase information in the cortex. Using numerical simulations, we demonstrate that the proposed single-step methods reduce the reconstruction error by up to 66% relative to the multi-step methods that involve SMV background filtering with the same number of SMV kernels, followed by TV- or TGV-regularized dipole inversion. In vivo single-step experiments demonstrate a dramatic reduction in dipole streaking artifacts and improved homogeneity of image contrast. These acquisitions employ the rapid three-dimensional echo planar imaging (3D EPI) and Wave-CAIPI (controlled aliasing in parallel imaging) trajectories for signal-to-noise ratio-efficient whole-brain imaging. Herein, we also demonstrate the multi-echo capability of the Wave-CAIPI sequence for the first time, and introduce an automated, phase-sensitive coil sensitivity estimation scheme based on a 4-s calibration acquisition. Copyright © 2016 John Wiley & Sons, Ltd. 2021-10-27T20:28:53Z 2021-10-27T20:28:53Z 2017 2019-04-25T17:54:52Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135703 Chatnuntawech, Itthi, et al. "Single-Step Quantitative Susceptibility Mapping with Variational Penalties." Nmr in Biomedicine 30 4 (2017). en 10.1002/NBM.3570 NMR in Biomedicine Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley PMC |
| spellingShingle | Chatnuntawech, Itthi McDaniel, Patrick Cauley, Stephen F Gagoski, Borjan A Langkammer, Christian Martin, Adrian Grant, P Ellen Wald, Lawrence L Setsompop, Kawin Adalsteinsson, Elfar Bilgic, Berkin Single-step quantitative susceptibility mapping with variational penalties: Single-Step Qsm with Variational Penalties |
| title | Single-step quantitative susceptibility mapping with variational penalties: Single-Step Qsm with Variational Penalties |
| title_full | Single-step quantitative susceptibility mapping with variational penalties: Single-Step Qsm with Variational Penalties |
| title_fullStr | Single-step quantitative susceptibility mapping with variational penalties: Single-Step Qsm with Variational Penalties |
| title_full_unstemmed | Single-step quantitative susceptibility mapping with variational penalties: Single-Step Qsm with Variational Penalties |
| title_short | Single-step quantitative susceptibility mapping with variational penalties: Single-Step Qsm with Variational Penalties |
| title_sort | single step quantitative susceptibility mapping with variational penalties single step qsm with variational penalties |
| url | https://hdl.handle.net/1721.1/135703 |
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