The influence of molecular order and microstructure on the R2* and the magnetic susceptibility tensor

Magnetic Susceptibility Source Separation Solely from Gradient Echo Data: Histological Validation

Quantitative susceptibility mapping (QSM) facilitates mapping of the bulk magnetic susceptibility of tissue from the phase of complex gradient echo (GRE) MRI data. QSM phase processing combined with an R2* model of magnitude of multiecho gradient echo data (R2*QSM) allows separation of dia- and para-magnetic components (e.g., myelin and iron) that contribute constructively to R2* value but destructively to the QSM value of a voxel. This R2*QSM technique is validated against quantitative histology—optical density of myelin basic protein and Perls’ iron histological stains of rim and core of 10 ex vivo multiple sclerosis lesions, as well as neighboring normal appearing white matter. We found that R2*QSM source maps are in good qualitative agreement with histology, e.g., showing increased iron concentration at the edge of the rim+ lesions and myelin loss in the lesions’ core. Furthermore, our results indicate statistically significant correlation between paramagnetic and diamagnetic tissue components estimated with R2*QSM and optical densities of Perls’ and MPB stains. These findings provide direct support for the use of R2*QSM magnetic source separation based solely on GRE complex data to characterize MS lesion composition.

Microstructural imaging in the spinal cord and validation strategies

In vivo histology using magnetic resonance imaging (MRI) is a newly emerging research field that aims to non-invasively characterize tissue microstructure. The implications of in vivo histology are many, from discovering novel biomarkers to studying human development, to providing tools for disease diagnosis and monitoring the effects of novel treatments on tissue. This review focuses on quantitative MRI (qMRI) techniques that are used to map spinal cord microstructure. Opening with a rationale for non-invasive imaging of the spinal cord, this article continues with a brief overview of the existing MRI techniques for axon and myelin imaging, followed by the specific challenges and potential solutions for acquiring and processing such data. The final part of this review focuses on histological validation, with suggested tissue preparation, acquisition and processing protocols for large-scale microscopy.

Quantitative Susceptibility Mapping (QSM) Algorithms: Mathematical Rationale and Computational Implementations

Quantitative susceptibility mapping (QSM) solves the magnetic field-to-magnetization (tissue susceptibility) inverse problem under conditions of noisy and incomplete field data acquired using magnetic resonance imaging. Therefore, sophisticated algorithms are necessary to treat the ill-posed nature of the problem and are reviewed here. The forward problem is typically presented as an integral form, where the field is the convolution of the dipole kernel and tissue susceptibility distribution. This integral form can be equivalently written as a partial differential equation (PDE). Algorithmic challenges are to reduce streaking and shadow artifacts characterized by the fundamental solution of the PDE. Bayesian maximum a posteriori estimation can be employed to solve the inverse problem, where morphological and relevant biomedical knowledge (specific to the imaging situation) are used as priors. As the cost functions in Bayesian QSM framework are typically convex, solutions can be robustly computed using a gradient-based optimization algorithm. Moreover, one can not only accelerate Bayesian QSM, but also increase its effectiveness at reducing shadows using prior knowledge based preconditioners. Improving the efficiency of QSM is under active development, and a rigorous analysis of preconditioning needs to be carried out for further investigation.Quantitative susceptibility mapping (QSM) solves the magnetic field-to-magnetization (tissue susceptibility) inverse problem under conditions of noisy and incomplete field data acquired using magnetic resonance imaging. Therefore, sophisticated algorithms are necessary to treat the ill-posed nature of the problem and are reviewed here. The forward problem is typically presented as an integral form, where the field is the convolution of the dipole kernel and tissue susceptibility distribution. This integral form can be equivalently written as a partial differential equation (PDE). Algorithmic challenges are to reduce streaking and shadow artifacts characterized by the fundamental solution of the PDE. Bayesian maximum a posteriori estimation can be employed to solve the inverse problem, where morphological and relevant biomedical knowledge (specific to the imaging situation) are used as priors. As the cost functions in Bayesian QSM framework are typically convex, solutions can be robustly computed using a gradient-based optimization algorithm. Moreover, one can not only accelerate Bayesian QSM, but also increase its effectiveness at reducing shadows using prior knowledge based preconditioners. Improving the efficiency of QSM is under active development, and a rigorous analysis of preconditioning needs to be carried out for further investigation.

The clinical utility of QSM: disease diagnosis, medical management, and surgical planning

Quantitative susceptibility mapping (QSM) is an MR technique that depicts and quantifies magnetic susceptibility sources. Mapping iron, the dominant susceptibility source in the brain, has many important clinical applications. Herein, we review QSM applications in the diagnosis, medical management, and surgical treatment of disease. To assist in early disease diagnosis, QSM can identify elevated iron levels in the motor cortex of amyotrophic lateral sclerosis patients, in the substantia nigra of Parkinson's disease (PD) patients, in the globus pallidus, putamen, and caudate of Huntington's disease patients, and in the basal ganglia of Wilson's disease patients. Additionally, QSM can distinguish between hemorrhage and calcification, which could prove useful in tumor subclassification, and can measure microbleeds in traumatic brain injury patients. In guiding medical management, QSM can be used to monitor iron chelation therapy in PD patients, to monitor smoldering inflammation of multiple sclerosis (MS) lesions after the blood-brain barrier (BBB) seals, to monitor active inflammation of MS lesions before the BBB seals without using gadolinium, and to monitor hematoma volume in intracerebral hemorrhage. QSM can also guide neurosurgical treatment. Neurosurgeons require accurate depiction of the subthalamic nucleus, a tiny deep gray matter nucleus, prior to inserting deep brain stimulation electrodes into the brains of PD patients. QSM is arguably the best imaging tool for depiction of the subthalamic nucleus. Finally, we discuss future directions, including bone QSM, cardiac QSM, and using QSM to map cerebral metabolic rate of oxygen. Copyright © 2016 John Wiley & Sons, Ltd.

Joint eigenvector estimation from mutually anisotropic tensors improves susceptibility tensor imaging of the brain, kidney, and heart

PURPOSE

To develop a susceptibility-based MRI technique for probing microstructure and fiber architecture of magnetically anisotropic tissues-such as central nervous system white matter, renal tubules, and myocardial fibers-in three dimensions using susceptibility tensor imaging (STI) tools.

THEORY AND METHODS

STI can probe tissue microstructure, but is limited by reconstruction artifacts because of absent phase information outside the tissue and noise. STI accuracy may be improved by estimating a joint eigenvector from mutually anisotropic susceptibility and relaxation tensors. Gradient-recalled echo image data were simulated using a numerical phantom and acquired from the ex vivo mouse brain, kidney, and heart. Susceptibility tensor data were reconstructed using STI, regularized STI, and the proposed algorithm of mutually anisotropic and joint eigenvector STI (MAJESTI). Fiber map and tractography results from each technique were compared with diffusion tensor data.

RESULTS

MAJESTI reduced the estimated susceptibility tensor orientation error by 30% in the phantom, 36% in brain white matter, 40% in the inner medulla of the kidney, and 45% in myocardium. This improved the continuity and consistency of susceptibility-based fiber tractography in each tissue.

CONCLUSION

MAJESTI estimation of the susceptibility tensors yields lower orientation errors for susceptibility-based fiber mapping and tractography in the intact brain, kidney, and heart. Magn Reson Med 77:2331-2346, 2017. © 2016 International Society for Magnetic Resonance in Medicine.