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Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method
- Source :
- NeuroImage. 179:166-175
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- A new dipole field inversion method for whole head quantitative susceptibility mapping (QSM) is proposed. Instead of performing background field removal and local field inversion sequentially, the proposed method performs dipole field inversion directly on the total field map in a single step. To aid this under-determined and ill-posed inversion process and obtain robust QSM images, Tikhonov regularization is implemented to seek the local susceptibility solution with the least-norm (LN) using the L-curve criterion. The proposed LN-QSM does not require brain edge erosion, thereby preserving the cerebral cortex in the final images. This should improve its applicability for QSM-based cortical grey matter measurement, functional imaging and venography of full brain. Furthermore, LN-QSM also enables susceptibility mapping of the entire head without the need for brain extraction, which makes QSM reconstruction more automated and less dependent on intermediate pre-processing methods and their associated parameters. It is shown that the proposed LN-QSM method reduced errors in a numerical phantom simulation, improved accuracy in a gadolinium phantom experiment, and suppressed artefacts in nine subjects, as compared to two-step and other single-step QSM methods. Measurements of deep grey matter and skull susceptibilities from LN-QSM are consistent with established reconstruction methods.
- Subjects :
- Adult
Male
Cognitive Neuroscience
Inverse transform sampling
Imaging phantom
030218 nuclear medicine & medical imaging
Tikhonov regularization
03 medical and health sciences
0302 clinical medicine
Image Processing, Computer-Assisted
Humans
Local field
Physics
Brain Mapping
Quantitative susceptibility mapping
Magnetic Resonance Imaging
Dipole
Neurology
Norm (mathematics)
Female
Artifacts
Head
Magnetic dipole
Algorithm
Algorithms
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 10538119
- Volume :
- 179
- Database :
- OpenAIRE
- Journal :
- NeuroImage
- Accession number :
- edsair.doi.dedup.....c388fe616fa73654dcd71c071a43ff13
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2018.06.036