Susceptibility Tensor Imaging Research Articles

Incorporating the effect of white matter microstructure in the estimation of magnetic susceptibility in ex vivo mouse brain.

To extend quantitative susceptibility mapping to account for microstructure of white matter (WM) and demonstrate its effect on ex vivo mouse brain at 16.4T. Previous studies have shown that the MRI measured Larmor frequency also depends on local magnetic microstructure at the mesoscopic scale. Here, we include effects from WM microstructure using our previous results for the mesoscopic Larmor frequency of cylinders with arbitrary orientations. We scrutinize the validity of our model and QSM in a digital brain phantom including from a WM susceptibility tensor and biologically stored iron with scalar susceptibility. We also apply susceptibility tensor imaging to the phantom and investigate how the fitted tensors are biased from . Last, we demonstrate how to combine multi-gradient echo and diffusion MRI images of ex vivo mouse brains acquired at 16.4T to estimate an apparent scalar susceptibility without sample rotations. Our new model improves susceptibility estimation compared to QSM for the brain phantom. Applying susceptibility tensor imaging to the phantom with from WM axons with scalar susceptibility produces a highly anisotropic susceptibility tensor that mimics results from previous susceptibility tensor imaging studies. For the ex vivo mouse brain we find the due to WM microstructure to be substantial, changing susceptibility in WM up to 25% root-mean-squared-difference. impacts susceptibility estimates and biases susceptibility tensor imaging fitting substantially. Hence, it should not be neglected when imaging structurally anisotropic tissue such as brain WM.

DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging.

Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging technique that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor model. STI has the potential to provide information for both the reconstruction of white matter fiber pathways and detection of myelin changes in the brain at mm resolution or less, which would be of great value for understanding brain structure and function in healthy and diseased brain. However, the application of STI in vivo has been hindered by its cumbersome and time-consuming acquisition requirement of measuring susceptibility induced MR phase changes at multiple head orientations. Usually, sampling at more than six orientations is required to obtain sufficient information for the ill-posed STI dipole inversion. This complexity is enhanced by the limitation in head rotation angles due to physical constraints of the head coil. As a result, STI has not yet been widely applied in human studies in vivo. In this work, we tackle these issues by proposing an image reconstruction algorithm for STI that leverages data-driven priors. Our method, called DeepSTI, learns the data prior implicitly via a deep neural network that approximates the proximal operator of a regularizer function for STI. The dipole inversion problem is then solved iteratively using the learned proximal network. Experimental results using both simulation and in vivo human data demonstrate great improvement over state-of-the-art algorithms in terms of the reconstructed tensor image, principal eigenvector maps and tractography results, while allowing for tensor reconstruction with MR phase measured at much less than six different orientations. Notably, promising reconstruction results are achieved by our method from only one orientation in human in vivo, and we demonstrate a potential application of this technique for estimating lesion susceptibility anisotropy in patients with multiple sclerosis.

Quantitative susceptibility mapping of the normal-appearing white matter as a potential new marker of disability progression in multiple sclerosis.

To investigate the normal-appearing white matter (NAWM) susceptibility in a cohort of newly diagnosed multiple sclerosis (MS) patients and to evaluate possible correlations between NAWM susceptibility and disability progression. Fifty-nine patients with a diagnosis of MS (n = 53) or clinically isolated syndrome (CIS) (n = 6) were recruited and followed up. All participants underwent neurological examination, blood sampling for serum neurofilament light chain (sNfL) level assessment, lumbar puncture for the quantification of cerebrospinal fluid (CSF) β-amyloid1-42 (Aβ) levels, and brain MRI. T2-weighted scans were used to quantify white matter (WM) lesion loads. For each scan, we derived the NAWM volume fraction and the WM lesion volume fraction. Quantitative susceptibility mapping (QSM) of the NAWM was calculated using the susceptibility tensor imaging (STI) suite. Susceptibility maps were computed with the STAR algorithm. Primary progressive patients (n = 9) showed a higher mean susceptibility value in the NAWM than relapsing-remitting (n = 44) and CIS (n = 6) (p = 0.01 and p = 0.02). Patients with a higher susceptibility in the NAWM showed increased sNfL concentration (ρ = 0.38, p = 0.004) and lower CSF Aβ levels (ρ = -0.34, p = 0.009). Mean NAWM susceptibility turned out to be a predictor of the expanded disability status scale (EDSS) worsening at follow-up (β = 0.41, t = 2.66, p = 0.01) and of the MS severity scale (MSSS) (β = 0.38, t = 2.43, p = 0.019). QSM in the NAWM seems to predict the EDSS increment over time. This finding might provide evidence on the role of QSM in identifying patients with an increased risk of early disability progression. • NAWM-QSM is higher in PPMS patients than in RRMS. • NAWM-QSM seems to be a predictor of EDSS worsening over time. • Patients with higher NAWM-QSM show increased sNfL concentration and lower CSF Aβ levels.

An improved asymmetric susceptibility tensor imaging model with frequency offset correction.

To improve susceptibility tensor imaging (STI) reconstruction using the asymmetric STI model with the correction of non-bulk-magnetic-susceptibility (NBMS) effects. A frequency offset term was introduced into the asymmetric STI model to account for the bias between measured MRI frequency signals and conventional susceptibility tensor models because of NBMS contributions. Experiments were conducted to compare the proposed model with conventional STI, conventional STI with the proposed frequency offset correction, and asymmetric STI on simulation, ex vivo mouse brain, and in vivo human brain data. In the simulation where NBMS contributions are head rotation-invariant, the proposed method achieves the lowest errors in mean magnetic susceptibility (MMS) and magnetic susceptibility anisotropy (MSA) and is more robust to noise in the estimation of principal eigenvector (PEV). When considering the head orientation dependency of NBMS contributions, the proposed method shows advantages in estimating MSA and PEV. On the mouse and human brain data, the proposed method produces more reliable MSA maps and more consistent white matter fiber directions when referring to those from DTI than the compared STI methods. The proposed method can reduce the effects of NBMS-related frequency shifts on the susceptibility tensors in the brain white matter. This study inspires STI reconstruction from the perspective of better modeling the sources of frequency shifts.

Regularized Asymmetric Susceptibility Tensor Imaging in the Human Brain in Vivo.

Susceptibility tensor imaging (STI) is a promising tool for studying orientation-dependent tissue magnetic susceptibility and for mapping white matter fiber orientations complementary to diffusion tensor imaging (DTI). However, the limited head rotation range within modern head coils for data acquisition makes in vivo STI reconstruction ill-conditioned. Conventional STI reconstruction method is usually vulnerable to noise and requires sufficiently large head rotations to solve this ill-conditioned inverse problem. In this study, based on the recently proposed asymmetric STI (aSTI) model, a new method termed aSTI+ was proposed to improve in vivo STI reconstruction by enforcing isotropic susceptibility tensor inside cerebrospinal fluid (CSF) and applying morphology constraint in white matter. Experimental results showed superior performance of the proposed method with reduced noise, improved tissue contrast and better fiber orientation estimation over previous methods. Thus aSTI+ may promote in vivo human brain STI studies on white matter and myelin-related brain diseases.

Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset

Recently, deep neural networks have shown great potential for solving dipole inversion of quantitative susceptibility mapping (QSM) with improved results. However, these studies utilized their limited dataset for network training and inference, which may lead to untrustworthy conclusions. Thus, a common dataset is needed for a fair comparison between different QSM reconstruction networks. Additionally, finding an in vivo reference susceptibility map that matches acquired single-orientation phase data remains an open problem. Susceptibility tensor imaging (STI) χ33 and Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) are considered reference susceptibility candidates. However, a large number of multi-orientation GRE data for both STI and COSMOS reconstruction are now unavailable for training supervised neural networks for QSM. In this study, we reported the largest multi-orientation dataset, to the best of our knowledge in the QSM research field, with a total of 144 scans from 8 healthy subjects collected using a 3D GRE sequence from the same MR scanner. In addition, the parcellation of deep gray matter is also provided for automatically extracting susceptibility values. Five recently developed deep neural networks, i.e., xQSM, QSMnet, autoQSM, LPCNN, and MoDL-QSM were performed on this dataset. This potential data source could provide a common framework and labels to test the accuracy and robustness of deep neural networks for QSM reconstruction. This dataset has the potential to provide a benchmark of reference susceptibility for the deep learning-based QSM methods. Additionally, the trained COSMOS-labeled and χ33-labeled networks were tested on the pathological data to explore their potential applications. The data together with deep gray matter parcellation maps are now publicly available via an open repository at https://osf.io/yfms7/, and the raw multi-orientation GRE data are also available at https://osf.io/y6rc3/.

MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (χ13, χ23 and χ33) and the acquired single-orientation phase. The convolutional neural networks are embedded into the physical model to learn a regularization term containing prior information. χ33 and phase induced by χ13 and χ23 terms were used as the labels for network training. Quantitative evaluation metrics were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications.

Asymmetric susceptibility tensor imaging.

To investigate the symmetry constraint in susceptibility tensor imaging. The linear relationship between the MRI frequency shift and the magnetic susceptibility tensor is derived without constraining the tensor to be symmetric. In the asymmetric case, the system matrix is shown to be maximally rank 6. Nonetheless, relaxing the symmetry constraint may still improve tensor estimation because noise and image artifacts do not necessarily follow the constraint. Gradient echo phase data are obtained from postmortem mouse brain and kidney samples. Both symmetric and asymmetric tensor reconstructions are applied to the data. The reconstructions are then used for susceptibility tensor imaging fiber tracking. Simulations with ground truth and at various noise levels are also performed. The reconstruction methods are compared qualitatively and quantitatively. Compared to regularized and unregularized symmetric reconstructions, the asymmetric reconstruction shows reduced noise and streaking artifacts, better contrast, and more complete fiber tracking. In simulation, the asymmetric reconstruction achieves better mean squared error and better angular difference in the presence of noise. Decomposing the asymmetric tensor into its symmetric and antisymmetric components confirms that the underlying susceptibility tensor is symmetric and that the main sources of asymmetry are noise and streaking artifacts. Whereas the susceptibility tensor is symmetric, asymmetric reconstruction is more effective in suppressing noise and artifacts, resulting in more accurate estimation of the susceptibility tensor.

Diffusion-regularized susceptibility tensor imaging (DRSTI) of tissue microstructures in the human brain.

Susceptibility tensor imaging (STI) has been proposed as an alternative to diffusion tensor imaging (DTI) for non-invasive in vivo characterization of brain tissue microstructure and white matter fiber architecture, potentially benefitting from its high spatial resolution. In spite of different biophysical mechanisms, animal studies have demonstrated white matter fiber directions measured using STI to be reasonably consistent with those from diffusion tensor imaging (DTI). However, human brain STI is hampered by its requirement of acquiring data at more than 10 head rotations and a complicated processing pipeline. In this paper, we propose a diffusion-regularized STI method (DRSTI) that employs a tensor spectral decomposition constraint to regularize the STI solution using the fiber directions estimated by DTI as a priori. We then explore the high-resolution DRSTI with MR phase images acquired at only 6 head orientations. Compared to other STI approaches, the DRSTI generated susceptibility tensor components, mean magnetic susceptibility (MMS), magnetic susceptibility anisotropy (MSA) and fiber direction maps with fewer artifacts, especially in regions with large susceptibility variations, and with less erroneous quantifications. In addition, the DRSTI method allows us to distinguish more structural features that could not be identified in DTI, especially in deep gray matters. DRSTI enables a more accurate susceptibility tensor estimation with a reduced number of sampling orientations, and achieves better tracking of fiber pathways than previous STI attempts on in vivo human brain.

Imaging diamagnetic susceptibility of collagen in hepatic fibrosis using susceptibility tensor imaging.

To characterize the magnetic susceptibility changes of liver fibrosis using susceptibility tensor imaging. Liver biopsy tissue samples of patients with liver fibrosis were obtained. Three-dimensional gradient-echo and diffusion-weighted images were acquired at 9.4 T. Susceptibility tensors of the samples were calculated using the gradient-echo phase signal acquired at 12 different orientations relative to the B0 field. Susceptibility anisotropy of the liver collagen fibers was quantified and compared with diffusion anisotropy, measured by DTI. For validation, a comparison was made to histology including hematoxylin and eosin staining, iron staining, and Masson's trichrome staining. Areas with strong diamagnetic susceptibility were observed in the tissue samples forming fibrous patterns. This diamagnetic susceptibility was highly anisotropic. Both the mean magnetic susceptibility and susceptibility anisotropy of collagen fibers exhibited a strong contrast against the surrounding nonfibrotic tissues. The same regions also showed an elevated diffusion anisotropy but with much lower tissue contrast. Masson's trichrome staining identified concentrated collagens in the fibrous regions with high susceptibility anisotropy, and a linear correlation was found between the susceptibility anisotropy and the histology-based staging. Diamagnetic susceptibility indicates the presence of collagen in the fibrotic liver tissues. Mapping magnetic susceptibility anisotropy may serve as a potential marker to quantify collagen fiber changes in patients with liver fibrosis.

Susceptibility tensor imaging and tractography of collagen fibrils in the articular cartilage.

To investigate the B0 orientation-dependent magnetic susceptibility of collagen fibrils within the articular cartilage and to determine whether susceptibility tensor imaging (STI) can detect the 3D collagen network within cartilage. Multiecho gradient echo datasets (100-μm isotropic resolution) were acquired from fixed porcine articular cartilage specimens at 9.4 T. The susceptibility tensor was calculated using phase images acquired at 12 or 15 different orientations relative to B0 . The susceptibility anisotropy of the collagen fibril was quantified and diffusion tensor imaging (DTI) was compared against STI. 3D tractography was performed to visualize and track the collagen fibrils with DTI and STI. STI experiments showed the distinct and significant anisotropic magnetic susceptibility of collagen fibrils within the articular cartilage. STI can be used to measure and quantify susceptibility anisotropy maps. Furthermore, STI provides orientation information of the underlying collagen network via 3D tractography. The findings of this study demonstrate that STI can characterize the orientation variation of collagen fibrils where diffusion anisotropy fails. We believe that STI could serve as a sensitive and noninvasive marker to study the collagen fibrils microstructure. Magn Reson Med 78:1683-1690, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge.

The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully. Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions. Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance. Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med 79:1661-1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Overview of quantitative susceptibility mapping.

Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and represents an important parameter in the field of MRI. With the recently introduced method of quantitative susceptibility mapping (QSM) and its conceptual extension to susceptibility tensor imaging (STI), the non-invasive assessment of this important physical quantity has become possible with MRI. Both methods solve the ill-posed inverse problem to determine the magnetic susceptibility from local magnetic fields. Whilst QSM allows the extraction of the spatial distribution of the bulk magnetic susceptibility from a single measurement, STI enables the quantification of magnetic susceptibility anisotropy, but requires multiple measurements with different orientations of the object relative to the main static magnetic field. In this review, we briefly recapitulate the fundamental theoretical foundation of QSM and STI, as well as computational strategies for the characterization of magnetic susceptibility with MRI phase data. In the second part, we provide an overview of current methodological and clinical applications of QSM with a focus on brain imaging. Copyright © 2016 John Wiley & Sons, Ltd.

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

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. 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. 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. 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.

Magnetic susceptibility anisotropy outside the central nervous system.

Magnetic-susceptibility-based MRI has made important contributions to the characterization of tissue microstructure, chemical composition, and organ function. This has motivated a number of studies to explore the link between microstructure and susceptibility in organs and tissues throughout the body, including the kidney, heart, and connective tissue. These organs and tissues have anisotropic magnetic susceptibility properties and cellular organizations that are distinct from the lipid organization of myelin in the brain. For instance, anisotropy is traced to the epithelial lipid orientation in the kidney, the myofilament proteins in the heart, and the collagen fibrils in the knee cartilage. The magnetic susceptibility properties of these and other tissues are quantified using specific MRI tools: susceptibility tensor imaging (STI), quantitative susceptibility mapping (QSM), and individual QSM measurements with respect to tubular and filament directions determined from diffusion tensor imaging. These techniques provide complementary and supplementary information to that produced by traditional MRI methods. In the kidney, STI can track tubules in all layers including the cortex, outer medulla, and inner medulla. In the heart, STI detected myofibers throughout the myocardium. QSM in the knee revealed three unique layers in articular cartilage by exploiting the anisotropic susceptibility features of collagen. While QSM and STI are promising tools to study tissue susceptibility, certain technical challenges must be overcome in order to realize routine clinical use. This paper reviews essential experimental findings of susceptibility anisotropy in the body, the underlying mechanisms, and the associated MRI methodologies. Copyright © 2016 John Wiley & Sons, Ltd.

Susceptibility tensor imaging (STI) of the brain.

Susceptibility tensor imaging (STI) is a recently developed MRI technique that allows quantitative determination of orientation-independent magnetic susceptibility parameters from the dependence of gradient echo signal phase on the orientation of biological tissues with respect to the main magnetic field. By modeling the magnetic susceptibility of each voxel as a symmetric rank-2 tensor, individual magnetic susceptibility tensor elements as well as the mean magnetic susceptibility and magnetic susceptibility anisotropy can be determined for brain tissues that would still show orientation dependence after conventional scalar-based quantitative susceptibility mapping to remove such dependence. Similar to diffusion tensor imaging, STI allows mapping of brain white matter fiber orientations and reconstruction of 3D white matter pathways using the principal eigenvectors of the susceptibility tensor. In contrast to diffusion anisotropy, the main determinant factor of the susceptibility anisotropy in brain white matter is myelin. Another unique feature of the susceptibility anisotropy of white matter is its sensitivity to gadolinium-based contrast agents. Mechanistically, MRI-observed susceptibility anisotropy is mainly attributed to the highly ordered lipid molecules in the myelin sheath. STI provides a consistent interpretation of the dependence of phase and susceptibility on orientation at multiple scales. This article reviews the key experimental findings and physical theories that led to the development of STI, its practical implementations, and its applications for brain research. Copyright © 2016 John Wiley & Sons, Ltd.

B0 -orientation dependent magnetic susceptibility-induced white matter contrast in the human brainstem at 11.7T.

To investigate B0 -field-orientation dependent white matter contrast in the human brainstem based on R2 * and frequency difference (Δf) mapping from gradient echo (GRE) imaging at 11.7T. Multi-echo GRE data were acquired from two fixed human brainstem specimens at multiple orientations with respect to the static B0 field. The B0 -orientation dependent modulation curves of R2 * and Δf measurements between short and long echo time regimes were used to reconstruct maps of three-dimensional (3D) white matter orientation vectors. The results were compared with maps from diffusion MRI, susceptibility tensor imaging, and histological staining of the same specimens. R2 * and Δf maps demonstrated distinct and significant contrast modulation between the corticospinal tract (CST) and transverse pontine fibers (TPF) dependent on B0 orientation. Interleaved fiber orientations of the CST and TPF could be sensitively resolved based on field-orientation-dependent fitting of the R2 * and Δf measurements. The fitted 3D orientation vector maps and peak-to-peak amplitude of R2 * and Δf modulation exhibited close correspondence to primary eigenvector and anisotropy maps derived from diffusion MRI. The amplitude of B0 -orientation dependent R2 * modulation was significantly (P < 0.005) higher in the CST compared with TPF, while fractional anisotropies were comparable. The findings of this study demonstrate the potential of B0 -orientation dependent susceptibility-induced R2 * and Δf contrasts to probe tract-specific orientation and microstructure in white matter. Magn Reson Med 75:2455-2463, 2016. © 2016 Wiley Periodicals, Inc.

Principles and applications of susceptibility weighted imaging

Susceptibility-weighted imaging (SWI), initially developed to provide an improved method for cerebral magnetic resonance (MR) venography, is now an integral part of neuroradiological diagnostics and is steadily gaining importance in non-cerebral imaging. Tissue-inherent susceptibility differences generate a local magnetic field in which the dephasing of signal-producing protons occurs. This leads to a characteristic phase shift that can be used as a means to enhance contrast in the well-known T2*-weighted imaging. Many medically relevant pathologies induce tissue alterations that also influence the magnetic properties of tissue. Thus, the detection of blood residues and calcifications in SWI is superior to conventional MR sequences. New techniques, such as quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI) allow improved differentiation between blood residues and calcifications and provide an alternative imaging method for fiber tractography with respect to diffusion tensor imaging.

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

In this work, we demonstrate that in the presence of ordered sub-voxel structure such as tubular organization, biomaterials with molecular isotropy exhibits only apparent R2* anisotropy, while biomaterials with molecular anisotropy exhibit both apparent R2* and susceptibility anisotropy by means of susceptibility tensor imaging (STI). To this end, R2* and STI from gradient echo magnitude and phase data were examined in phantoms made from carbon fiber and Gadolinium (Gd) solutions with and without intrinsic molecular order and sub-voxel structure as well as in the in vivo brain. Confidence in the tensor reconstructions was evaluated with a wild bootstrap analysis. Carbon fiber showed both apparent anisotropy in R2* and anisotropy in STI, while the Gd filled capillary tubes only showed apparent anisotropy on R2*. Similarly, white matter showed anisotropic R2* and magnetic susceptibility with higher confidence, while the cerebral veins displayed only strong apparent R2* tensor anisotropy. Ordered sub-voxel tissue microstructure leads to apparent R2* anisotropy, which can be found in both white matter tracts and cerebral veins. However, additional molecular anisotropy is required for magnetic susceptibility anisotropy, which can be found in white matter tracts but not in cerebral veins.

Rapid multi-orientation quantitative susceptibility mapping

Three-dimensional gradient echo (GRE) is the main workhorse sequence used for susceptibility weighted imaging (SWI), quantitative susceptibility mapping (QSM), and susceptibility tensor imaging (STI). Achieving optimal phase signal-to-noise ratio requires late echo times, thus necessitating a long repetition time (TR). Combined with the large encoding burden of whole-brain coverage with high resolution, this leads to increased scan time. Further, the dipole kernel relating the tissue phase to the underlying susceptibility distribution undersamples the frequency content of the susceptibility map. Scans at multiple head orientations along with calculation of susceptibility through multi-orientation sampling (COSMOS) are one way to effectively mitigate this issue. Additionally, STI requires a minimum of 6 head orientations to solve for the independent tensor elements in each voxel. The requirements of high-resolution imaging with long TR at multiple orientations substantially lengthen the acquisition of COSMOS and STI. The goal of this work is to dramatically speed up susceptibility mapping at multiple head orientations. We demonstrate highly efficient acquisition using 3D-GRE with Wave-CAIPI and dramatically reduce the acquisition time of these protocols. Using R=15-fold acceleration with Wave-CAIPI permits acquisition per head orientation in 90s at 1.1mm isotropic resolution, and 5:35min at 0.5mm isotropic resolution. Since Wave-CAIPI fully harnesses the 3D spatial encoding capability of receive arrays, the maximum g-factor noise amplification remains below 1.30 at 3T and 1.12 at 7T. This allows a 30-min exam for STI with 12 orientations, thus paving the way to its clinical application.