Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping

Liu, Tian; Wisnieff, Cynthia; Lou, Min; Chen, Weiwei; Spincemaille, Pascal; Wang, Yi

doi:10.1002/mrm.24272

Cited by 312 publications

(507 citation statements)

References 37 publications

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“…The spatial variation of the induced heterogeneity due to microstructure is closely linked to the underlying fiber orientation. More sophisticated inversion methods that incorporate edge priors and noise reduction strategies (12,46) may reduce this unwanted heterogeneity, but are unlikely to remove it completely. Also, many of the structures exhibiting large microstructure effects, such as the splenium and optic radiation, can appear in the edge-based priors used by these algorithms, and therefore may be particularly difficult to remove.…”

Section: Discussionmentioning

confidence: 99%

Effects of white matter microstructure on phase and susceptibility maps

Wharton

Bowtell

2015

Magn. Reson. Med.

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Purpose: To investigate the effects on quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI) of the frequency variation produced by the microstructure of white matter (WM). Methods: The frequency offsets in a WM tissue sample that are not explained by the effect of bulk isotropic or anisotropic magnetic susceptibility, but rather result from the local microstructure, were characterized for the first time. QSM and STI were then applied to simulated frequency maps that were calculated using a digitized whole-brain, WM model formed from anatomical and diffusion tensor imaging data acquired from a volunteer. In this model, the magnitudes of the frequency contributions due to anisotropy and microstructure were derived from the results of the tissue experiments. Results: The simulations suggest that the frequency contribution of microstructure is much larger than that due to bulk effects of anisotropic magnetic susceptibility. In QSM, the microstructure contribution introduced artificial WM heterogeneity. For the STI processing, the microstructure contribution caused the susceptibility anisotropy to be significantly overestimated. Conclusion: Microstructure-related phase offsets in WM yield artifacts in the calculated susceptibility maps. If susceptibility mapping is to become a robust MRI technique, further research should be carried out to reduce the confounding effects of microstructure-related frequency contributions.

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Section: Discussionmentioning

confidence: 99%

Effects of white matter microstructure on phase and susceptibility maps

Wharton

Bowtell

2015

Magn. Reson. Med.

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“…19 After the complex multiecho MR images were saved, a nonlinear fitting was performed to estimate the magnetic field inhomogeneity, followed by a magnitude-guided phase unwrapping. 20 The background field was further removed by applying the projection to the dipole fields method. 26 Finally, the remaining tissue field was inverted to generate a susceptibility map by using the morphology enabled dipole inversion method.…”

Section: Mr Imagingmentioning

confidence: 99%

“…26 Finally, the remaining tissue field was inverted to generate a susceptibility map by using the morphology enabled dipole inversion method. 20 An empirically determined regularization parameter of 1000 was consistently applied to all cases. All 11 echoes were used with weightings proportional to their signal-to-noise ratios for reconstructing QSM and R2*.…”

Section: Mr Imagingmentioning

confidence: 99%

“…On the other hand, the inhomogeneous field estimated from the MR imaging signal is the tissue susceptibility linearly convolving with the dipole kernel. 28,35 Deconvolution of the field generates QSM, [18][19][20] which, divided by iron molar susceptibility, is the iron concentration map. Therefore, QSM is more accurate than R2* mapping for measuring iron content.…”

Section: Fig 2 R2* (Top Row)mentioning

confidence: 99%

“…[15][16][17] Recently, quantitative susceptibility mapping (QSM) has been developed to determine tissue magnetic susceptibility from gradient-echo data. [18][19][20] Because ferritin susceptibility is much stronger than other tissues in brains free of hemorrhages and aggregates of other metals, QSM can be used to quantify brain iron distribution. 21,22 Clinical data from patients with multiple sclerosis suggest that QSM is more sensitive than R2* mapping in detecting changes in multiple sclerosis brains.…”

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confidence: 99%

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Usefulness of Quantitative Susceptibility Mapping for the Diagnosis of Parkinson Disease

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et al. 2015

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A multi‐contrast MRI approach to thalamus segmentation

Corona

Lellmann

Nestor

et al. 2020

Human Brain Mapping

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Thalamic alterations occur in many neurological disorders including Alzheimer's disease, Parkinson's disease and multiple sclerosis. Routine interventions to improve symptom severity in movement disorders, for example, often consist of surgery or deep brain stimulation to diencephalic nuclei. Therefore, accurate delineation of grey matter thalamic subregions is of the upmost clinical importance.MRI is highly appropriate for structural segmentation as it provides different views of the anatomy from a single scanning session. Though with several contrasts potentially available, it is also of increasing importance to develop new image segmentation techniques that can operate multi-spectrally. We hereby propose a new segmentation method for use with multi-modality data, which we evaluated for automated segmentation of major thalamic subnuclear groups using T 1 -weighted, T * 2 -weighted and quantitative susceptibility mapping (QSM) information. The proposed method consists of four steps: Highly iterative image co-registration, manual segmentation on the average training-data template, supervised learning for pattern recognition, and a final convex optimisation step imposing further spatial constraints to refine the solution. This led to solutions in greater agreement with manual segmentation than the standard Morel atlas based approach. Furthermore, we show that the multi-contrast approach boosts segmentation performances. We then investigated whether prior knowledge using the training-template contours could further improve convex segmentation accuracy and robustness, which led to highly precise multi-contrast segmentations in single subjects. This approach can be extended to most 3D imaging data types and any region of interest discernible in single scans or multi-subject templates. K E Y W O R D S magnetic resonance imaging, multi-contrast segmentation, thalamic nuclei, thalamus segmentation

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Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping

Cited by 312 publications

References 37 publications

Effects of white matter microstructure on phase and susceptibility maps

Effects of white matter microstructure on phase and susceptibility maps

Usefulness of Quantitative Susceptibility Mapping for the Diagnosis of Parkinson Disease

A multi‐contrast MRI approach to thalamus segmentation

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