Quantitative susceptibility mapping (QSM) is a novel MRI method for quantifying tissue magnetic property. In the brain, it reflects the molecular composition and microstructure of the local tissue. However, susceptibility maps reconstructed from single-orientation data still suffer from streaking artifacts which obscure structural details and small lesions. We propose and have developed a general method for estimating streaking artifacts and subtracting them from susceptibility maps. Specifically, this method uses a sparse linear equation and least-squares (LSQR)-algorithm-based method to derive an initial estimation of magnetic susceptibility, a fast quantitative susceptibility mapping method to estimate the susceptibility boundaries, and an iterative approach to estimate the susceptibility artifact from ill-conditioned k-space regions only. With a fixed set of parameters for the initial susceptibility estimation and subsequent streaking artifact estimation and removal, the method provides an unbiased estimate of tissue susceptibility with negligible streaking artifacts, as compared to multi-orientation QSM reconstruction. This method allows for improved delineation of white matter lesions in patients with multiple sclerosis and small structures of the human brain with excellent anatomical details. The proposed methodology can be extended to other existing QSM algorithms.

Quantitative susceptibility mapping (QSM) is a novel MRI technique for measuring tissue magnetic susceptibility in 3D. While there are numerous algorithms developed to solve this ill-posed inverse problem, estimating susceptibility maps with a wide range of values is still problematic. In cases such as large veins, contrast agent uptake, and intracranial hemorrhages, extreme susceptibility values in focal areas cause severe streaking artifacts. To enable the reduction of these artifacts while preserving subtle susceptibility contrast, a two-level QSM reconstruction algorithm (STAR-QSM) was developed in this study by tuning a regularization parameter to automatically reconstruct both large and small susceptibility values. Compared to current state-of-the-art QSM methods such as iLSQR, STAR-QSM significantly reduced streaking artifacts while preserving sharp boundaries for blood vessels of mouse brains in vivo and fine anatomical details of high resolution mouse brains ex vivo. Brain image data from patients with cerebral hematoma and multiple sclerosis further illustrated the superiority of this method in reducing streaking artifacts caused by large susceptibility sources while maintaining sharp anatomical details. STAR-QSM is implemented in STI Suite, a comprehensive shareware for susceptibility imaging and quantification.

Purpose To evaluate the magnetic susceptibility properties of different anatomical structures within the knee joint using quantitative susceptibility mapping (QSM). Methods A collagen tissue model was simulated and ex vivo animal cartilage experiments were conducted at 9.4 T to evaluate the B0 orientation-dependent magnetic susceptibility contrast observed in cartilage. Furthermore, nine volunteers (six healthy subjects without knee pain history and three patients with known knee injury, age between 29 and 58 years) were scanned using gradient-echo acquisitions on a high-field (7 T) magnetic resonance (MR) scanner. Susceptibility values of different tissues were quantified and diseased cartilage and meniscus were compared against that of healthy volunteers. Results Simulation and ex vivo animal cartilage experiments demonstrated that collagen fibrils exhibit an anisotropic susceptibility. A gradual change of magnetic susceptibility was observed in the articular cartilage from the superficial zone to the deep zone, forming a multilayer ultra-structure consistent with anisotropy of collagen fibrils. Meniscal tears caused a clear reduction of susceptibility contrast between the injured meniscus and surrounding cartilage illustrated by a loss of the sharp boundaries between the two. Moreover, QSM showed more dramatic contrast in the focal degenerated articular cartilage than R2* mapping. Conclusion The arrangement of the collagen fibrils is a significant and likely the most dominant source of magnetic susceptibility anisotropy. QSM offers a means to characterize magnetic susceptibility properties of tissues in the knee joint. QSM is sensitive to collagen damage or degeneration and may be useful for evaluating the status of knee diseases, such as meniscal tears and cartilage disease.

Diffusion tensor histology holds great promise for quantitative characterization of structural connectivity in mouse models of neurological and psychiatric conditions. There has been extensive study in both the clinical and preclinical domains on the complex tradeoffs between the spatial resolution, the number of samples in diffusion q-space, scan time, and the reliability of the resultant data. We describe here a method for accelerating the acquisition of diffusion MRI data to support quantitative connectivity measurements in the whole mouse brain using compressed sensing (CS). The use of CS allows substantial increase in spatial resolution and/or reduction in scan time. Compared to the fully sampled results at the same scan time, the subtle anatomical details of the brain, such as cortical layers, dentate gyrus, and cerebellum, were better visualized using CS due to the higher spatial resolution. Compared to the fully sampled results at the same spatial resolution, the scalar diffusion metrics, including fractional anisotropy (FA) and mean diffusivity (MD), showed consistently low error across the whole brain (< 6.0%) even with 8.0 times acceleration. The node properties of connectivity (strength, cluster coefficient, eigenvector centrality, and local efficiency) demonstrated correlation of better than 95.0% between accelerated and fully sampled connectomes. The acceleration will enable routine application of this technology to a wide range of mouse models of neurologic diseases.

Quantitative susceptibility mapping (QSM) is increasingly used to measure variation in tissue composition both in the brain and in other areas of the body in a range of disease pathologies. Although QSM measurements were originally believed to be independent of the echo time (TE) used in the gradient-recalled echo (GRE) acquisition from which they are derived; recent literature (Sood et al., 2016) has shown that these measurements can be highly TE-dependent in a number of brain regions. In this work we systematically investigate possible causes of this effect through analysis of apparent frequency and QSM measurements derived from data acquired at multiple TEs in vivo in healthy brain regions and in cerebral microbleeds (CMBs); QSM data acquired in a gadolinium-doped phantom; and in QSM data derived from idealized simulated phase data. Apparent frequency measurements in the optic radiations (OR) and central corpus callosum (CC) were compared to those predicted by a 3-pool white matter model, however the model failed to fully explain contrasting frequency profiles measured in the OR and CC. Our results show that TE-dependent QSM measurements can be caused by a failure of phase unwrapping algorithms in and around strong susceptibility sources such as CMBs; however, in healthy brain regions this behavior appears to result from intrinsic non-linear phase evolution in the MR signal. From these results we conclude that care must be taken when deriving frequency and QSM measurements in strong susceptibility sources due to the inherent limitations in phase unwrapping; and that while signal compartmentalization due to tissue microstructure and content is a plausible cause of TE-dependent frequency and QSM measurements in healthy brain regions, better sampling of the MR signal and more complex models of tissue are needed to fully exploit this relationship.