Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications

Quantitation of Brain and Blood Glutathione and Iron in Healthy Age Groups Using Biophysical and In Vivo MR Spectroscopy: Potential Clinical Application

The antioxidant glutathione (GSH) and pro-oxidant iron levels play a balancing role in the modulation of oxidative stress (OS). There is a significant depletion of GSH in the left hippocampus (LH) in patients with Alzheimer's disease (AD) with concomitant elevation of iron level. However, the correlation of GSH and iron distribution patterns between the brain and the peripheral system (blood) is not yet known. We measured GSH and magnetic susceptibility (e.g., iron) in the LH region along with GSH in plasma and iron in serum across four age groups consisting of healthy volunteers (age range 18-72 y, n = 70). We report non-variability of the mean GSH in the plasma and LH region across mentioned age groups. The mean iron level in the LH region does not change, but the iron level in the serum in the 51-72 y age group increases non-significantly. Regression analysis of our data indicated that GSH and iron levels (both in blood and in brain) are not related to age. This research pave the way for the identification of a risk/susceptibility biomarker for AD and Parkinson's disease from the evaluation of GSH (in plasma) and iron (in serum) levels concomitantly.

Integrating multimodality magnetic resonance imaging to the Allen Mouse Brain Common Coordinate Framework

High-resolution magnetic resonance imaging (MRI) affords unique image contrasts to nondestructively probe the tissue microstructure; validation of MRI findings with conventional histology is essential to better understand the MRI contrasts. However, the dramatic difference in the spatial resolution and image contrast of these two techniques impedes accurate comparison between MRI metrics and traditional histology. To better validate various MRI metrics, we acquired whole mouse brain multigradient recalled-echo and multishell diffusion MRI datasets at 25-μm isotropic resolution. The recently developed Allen Mouse Brain Common Coordinate Framework (CCFv3) provides opportunities to integrate multimodal and multiscale datasets of the whole mouse brain in a common three-dimensional (3D) space. The T2*, quantitative susceptibility mapping, diffusion tensor imaging, and neurite orientation dispersion and density imaging parameters were compared with both serial two-photon tomography images and 3D Nissl staining images in the CCFv3 at the same spatial resolution. The correlation between MRI and Nissl staining strongly depends on different metrics and different regions of the brain. Integrating different imaging modalities to the same space may substantially improve our understanding of the complexity of the brain at different scales.

Investigating the effect of oblique image acquisition on the accuracy of QSM and a robust tilt correction method

PURPOSE

Quantitative susceptibility mapping (QSM) is used increasingly for clinical research where oblique image acquisition is commonplace, but its effects on QSM accuracy are not well understood.

THEORY AND METHODS

The QSM processing pipeline involves defining the unit magnetic dipole kernel, which requires knowledge of the direction of the main magnetic fieldB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ with respect to the acquired image volume axes. The direction ofB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ is dependent on the axis and angle of rotation in oblique acquisition. Using both a numerical brain phantom and in vivo acquisitions in 5 healthy volunteers, we analyzed the effects of oblique acquisition on magnetic susceptibility maps. We compared three tilt-correction schemes at each step in the QSM pipeline: phase unwrapping, background field removal and susceptibility calculation, using the RMS error and QSM-tuned structural similarity index.

RESULTS

Rotation of wrapped phase images gave severe artifacts. Background field removal with projection onto dipole fields gave the most accurate susceptibilities when the field map was first rotated into alignment withB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ . Laplacian boundary value and variable-kernel sophisticated harmonic artifact reduction for phase data background field removal methods gave accurate results without tilt correction. For susceptibility calculation, thresholded k-space division, iterative Tikhonov regularization, and weighted linear total variation regularization, all performed most accurately when local field maps were rotated into alignment withB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ before susceptibility calculation.

CONCLUSION

For accurate QSM, oblique acquisition must be taken into account. Rotation of images into alignment withB ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ should be carried out after phase unwrapping and before background-field removal. We provide open-source tilt-correction code to incorporate easily into existing pipelines: https://github.com/o-snow/QSM_TiltCorrection.git.

Direct localization and delineation of human pedunculopontine nucleus based on a self-supervised magnetic resonance image super-resolution method

The pedunculopontine nucleus (PPN) is a small brainstem structure and has attracted attention as a potentially effective deep brain stimulation (DBS) target for the treatment of Parkinson's disease (PD). However, the in vivo location of PPN remains poorly described and barely visible on conventional structural magnetic resonance (MR) images due to a lack of high spatial resolution and tissue contrast. This study aims to delineate the PPN on a high-resolution (HR) atlas and investigate the visibility of the PPN in individual quantitative susceptibility mapping (QSM) images. We combine a recently constructed Montreal Neurological Institute (MNI) space unbiased QSM atlas (MuSus-100), with an implicit representation-based self-supervised image super-resolution (SR) technique to achieve an atlas with improved spatial resolution. Then guided by a myelin staining histology human brain atlas, we localize and delineate PPN on the atlas with improved resolution. Furthermore, we examine the feasibility of directly identifying the approximate PPN location on the 3.0-T individual QSM MR images. The proposed SR network produces atlas images with four times the higher spatial resolution (from 1 to 0.25 mm isotropic) without a training dataset. The SR process also reduces artifacts and keeps superb image contrast for further delineating small deep brain nuclei, such as PPN. Using the myelin staining histological atlas as guidance, we first identify and annotate the location of PPN on the T1-weighted (T1w)-QSM hybrid MR atlas with improved resolution in the MNI space. Then, we relocate and validate that the optimal targeting site for PPN-DBS is at the middle-to-caudal part of PPN on our atlas. Furthermore, we confirm that the PPN region can be identified in a set of individual QSM images of 10 patients with PD and 10 healthy young adults. The contrast ratios of the PPN to its adjacent structure, namely the medial lemniscus, on images of different modalities indicate that QSM substantially improves the visibility of the PPN both in the atlas and individual images. Our findings indicate that the proposed SR network is an efficient tool for small-size brain nucleus identification. HR QSM is promising for improving the visibility of the PPN. The PPN can be directly identified on the individual QSM images acquired at the 3.0-T MR scanners, facilitating a direct targeting of PPN for DBS surgery.

The Acute Superficial Siderosis Syndrome - Clinical Entity, Imaging Findings, and Histopathology

Superficial siderosis is a consequence of repetitive bleeding into the subarachnoid space, leading to toxic iron and hemosiderin deposits on the surface of the brain and spine. The clinical and radiological phenotypes of superficial siderosis are known to manifest over long time intervals. In contrast, this study defines the "acute superficial siderosis syndrome" and illustrates typical imaging and histopathological findings of this entity. We describe the case of a 61-year-old male patient who was diagnosed with a melanoma metastasis in the right frontal cortex in February 2019. Within a few weeks he developed a progressive syndrome characterized by cerebellar ataxia, gait disturbance, signs of myelopathy, and radiculopathy. MRI revealed ongoing hemorrhage from the metastasis into the lateral ventricle and the subarachnoid space. A semiquantitative assessment of three subsequent MRI within an 8-week period documented the rapid development of superficial siderosis along the surface of the cerebellum, the brain stem, and the lower parts of the supratentorial regions on T2*-weighted sequences. The diagnosis of a superficial siderosis was histopathologically confirmed by identifying iron and hemosiderin deposits on the cortex along with astrogliosis. The recognition of this "acute superficial siderosis syndrome" triggered surgical removal of the hemorrhagic metastasis. Based on a single case presentation, we define the "acute superficial siderosis syndrome" as a clinical entity and describe the radiological and histopathological characteristics of this entity. Early recognition of this syndrome may allow timely elimination of the bleeding source, in order to prevent further clinical deterioration.

Spatial patterns of elevated magnetic susceptibility in progressive apraxia of speech

PURPOSE

Progressive apraxia of speech (PAOS) is a neurodegenerative disorder affecting the planning or programming of speech. Little is known about its magnetic susceptibility profiles indicative of biological processes such as iron deposition and demyelination. This study aims to clarify (1) the pattern of susceptibility in PAOS patients, (2) the susceptibility differences between the phonetic (characterized by predominance of distorted sound substitutions and additions) and prosodic (characterized by predominance of slow speech rate and segmentation) subtypes of PAOS, and (3) the relationships between susceptibility and symptom severity.

METHODS

Twenty patients with PAOS (nine phonetic and eleven prosodic subtypes) were prospectively recruited and underwent a 3 Tesla MRI scan. They also underwent detailed speech, language, and neurological evaluations. Quantitative susceptibility maps (QSM) were reconstructed from multi-echo gradient echo MRI images. Region of interest analysis was conducted to estimate susceptibility coefficients in several subcortical and frontal regions. We compared susceptibility values between PAOS and an age-matched control group and performed a correlation analysis between susceptibilities and an apraxia of speech rating scale (ASRS) phonetic and prosodic feature ratings.

RESULTS

The magnetic susceptibility of PAOS was statistically greater than that of controls in subcortical regions (left putamen, left red nucleus, and right dentate nucleus) (p < 0.01, also survived FDR correction) and in the left white-matter precentral gyrus (p < 0.05, but not survived FDR correction). The prosodic patients showed greater susceptibilities than controls in these subcortical and precentral regions. The susceptibility in the left red nucleus and in the left precentral gyrus correlated with the prosodic sub-score of the ASRS.

CONCLUSION

Magnetic susceptibility in PAOS patients was greater than controls mainly in the subcortical regions. While larger samples are needed before QSM is considered ready for clinical differential diagnosis, the present study contributes to our understanding of magnetic susceptibility changes and the pathophysiology of PAOS.

Deep learning-regularized, single-step quantitative susceptibility mapping quantification

The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning-regularized, single-step QSM quantification model, named SS-POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0. In SS-POCSnet, a data fidelity term based on a single-step model was iteratively applied that combined the spherical mean value kernel and dipole model. Meanwhile, SS-POCSnet regularized susceptibility maps, avoiding underestimating susceptibility values. We evaluated the SS-POCSnet on 10 synthetic datasets, 24 clinical datasets with lesions of cerebral microbleed (CMB) and calcification, and 10 datasets with multiple sclerosis (MS).On synthetic datasets, SS-POCSnet showed the best performance among the methods evaluated, with a normalized root mean squared error of 37.3% ± 4.2%, susceptibility-tuned structured similarity index measure of 0.823 ± 0.02, high-frequency error norm of 37.0 ± 5.7, and peak signal-to-noise ratio of 42.8 ± 1.1. SS-POCSnet also reduced the underestimations of susceptibility values in deep brain nuclei compared with those from the other models evaluated. Furthermore, SS-POCSnet was sensitive to CMB/calcification and MS lesions, demonstrating its clinical applicability. Our method also supported variable imaging parameters, including matrix size and resolution. It was concluded that deep learning-regularized, single-step QSM quantification can mitigate underestimating susceptibility values in deep brain nuclei.

Multi-echo dipole inversion for magnetic susceptibility mapping

PURPOSE

Reconstructing tissue magnetic susceptibility (QSM) from MRI phase data involves solving multiple consecutive ill-posed inverse problems such as phase unwrapping, background field removal, and field-to-source inversion. Multi-echo acquisitions present an additional challenge, as the magnetization field is typically computed from the multiple phase data prior to reconstructing the susceptibility map. Processing the multiple phase data introduces errors during the field estimation, violating assumptions of the subsequent inverse problems, manifesting as streaking artifacts in the susceptibility map. To address this challenge, we propose a multi-echo field-to-source forward model that forgoes the field estimation step. Moreover, we propose a fully general underestimation correction step to recover susceptibility sources that were regularized away during the field-to-source inversion.

METHODS

The multi-echo forward model and correction step were validated on the QSM Challenge 2.0 datasets and compared to the standard single field-to-source model in in vivo human brains using different types of deconvolution algorithms.

RESULTS

On the QSM Challenge 2.0 datasets the multi-echo forward model and correction step attain state-of-the-art results on all metrics by a wide margin. Experiments in in vivo brains show that the multi-echo model is in agreement with the single field-to-source model and that the proposed forward model and correction step can be used with any available dipole inversion method.

CONCLUSION

A multi-echo field-to-source forward model forgoes the need to fit multi-echo phase data and achieves state-of-the-art results on the QSM Challenge 2.0 data. Underestimated low-frequency susceptibility distributions can be partially recovered using a correction step.

Magnetic susceptibility as a 1-year predictor of outcome in familial cerebral cavernous malformations: a pilot study

OBJECTIVES

To test whether quantitative susceptibility mapping (QSM) of cerebral cavernous malformations (CCMs) assessed at baseline may predict the presence or absence of haemorrhagic signs at 1-year follow-up.

METHODS

Familial CCM patients were enrolled in the longitudinal multicentre study Treat-CCM. The 3-T MRI scan allowed performing a semi-automatic segmentation of CCMs and computing the maximum susceptibility in each segmented CCM (QSMmax) at baseline. CCMs were classified as haemorrhagic and non-haemorrhagic at baseline and then subclassified according to the 1-year (t1) evolution. Between-group differences were tested, and the diagnostic accuracy of QSMmax in predicting the presence or absence of haemorrhagic signs in CCMs was calculated with ROC analyses.

RESULTS

Thirty-three patients were included in the analysis, and a total of 1126 CCMs were segmented. QSMmax was higher in haemorrhagic CCMs than in non-haemorrhagic CCMs (p < 0.001). In haemorrhagic CCMs at baseline, the accuracy of QSMmax in differentiating CCMs that were still haemorrhagic from CCMs that recovered from haemorrhage at t1 calculated as area under the curve (AUC) was 0.78 with sensitivity 62.69%, specificity 82.35%, positive predictive value (PPV) 93.3% and negative predictive value (NPV) 35.9% (QSMmax cut-off ≥ 1462.95 ppb). In non-haemorrhagic CCMs at baseline, AUC was 0.91 in differentiating CCMs that bled at t1 from stable CCMs with sensitivity 100%, specificity 81.9%, PPV 5.1%, and NPV 100% (QSMmax cut-off ≥ 776.29 ppb).

CONCLUSIONS

The QSMmax in CCMs at baseline showed high accuracy in predicting the presence or absence of haemorrhagic signs at 1-year follow-up. Further effort is required to test the role of QSM in follow-up assessment and therapeutic trials in multicentre CCM studies.

KEY POINTS

• QSM in semi-automatically segmented CCM was feasible. • The maximum magnetic susceptibility in a single CCM at baseline may predict the presence or absence of haemorrhagic signs at 1-year follow-up. • Multicentric studies are needed to enforce the role of QSM in predicting the CCMs' haemorrhagic evolution in patients affected by familial and sporadic forms.

Potential pharmacological mechanisms of four active compounds of Macleaya cordata extract against enteritis based on network pharmacology and molecular docking technology

Background: Macleaya cordata extract (MCE) is effective in the treatment of enteritis, but its mechanism has not been fully elucidated. Therefore, this study combined network pharmacology and molecular docking technologies to investigate the potential pharmacological mechanism of MCE in the treatment of enteritis. Methods: The information of active compounds in MCE was accessed through the literature. Furthermore, PubChem, PharmMapper, UniProt, and GeneCards databases were used to analyze the targets of MCE and enteritis. The intersection of drug and disease targets was imported into the STRING database, and the analysis results were imported into Cytoscape 3.7.1 software to construct a protein-protein interaction (PPI) network and to screen core targets. The Metascape database was used for conducting Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. AutoDock Tools software was used for the molecular docking of active compounds with the core targets. Results: MCE has four active compounds, namely, sanguinarine, chelerythrine, protopine, and allocryptopine, and a total of 269 targets after de-duplication. Furthermore, a total of 1,237 targets were associated with enteritis, 70 of which were obtained by aiding the drug-disease intersection with the aforementioned four active compound targets of MCE. Five core targets including mitogen-activated protein kinase 1 (MAPK1) and AKT serine/threonine kinase 1 (AKT1) were obtained using the PPI network, which are considered the potential targets for the four active compounds of MCE in the treatment of enteritis. The GO enrichment analysis involved 749 biological processes, 47 cellular components, and 64 molecular functions. The KEGG pathway enrichment analysis revealed 142 pathways involved in the treatment of enteritis by the four active compounds of MCE, among which PI3K-Akt and MAPK signaling pathways were the most important pathways. The results of molecular docking showed that the four active compounds demonstrated good binding properties at the five core targets. Conclusion: The pharmacological effects of the four active compounds of MCE in the treatment of enteritis involve acting on signaling pathways such as PI3K-Akt and MAPK through key targets such as AKT1 and MAPK1, thus providing new indications for further research to verify its mechanisms.

Fusion of quantitative susceptibility maps and T1-weighted images improve brain tissue contrast in primates

Recent progress in quantitative susceptibility mapping (QSM) has enabled the accurate delineation of submillimeter-scale subcortical brain structures in humans. However, the simultaneous visualization of cortical, subcortical, and white matter structure remains challenging, utilizing QSM data solely. Here we present TQ-SILiCON, a fusion method that enhances the contrast of cortex and subcortical structures and provides an excellent white matter delineation by combining QSM and conventional T1-weighted (T1w) images. In this study, we first applied QSM in the macaque monkey to map iron-rich subcortical structures. Implementing the same QSM acquisition and analysis methods allowed a similar accurate delineation of subcortical structures in humans. However, the QSM contrast of white and cortical gray matter was not sufficient for appropriate segmentation. Applying automatic brain tissue segmentation to TQ-SILiCON images of the macaque improved the classification of subcortical brain structures as compared to the single T1 contrast by maintaining an excellent white to cortical gray matter contrast. Furthermore, we validated our dual-contrast fusion approach in humans and similarly demonstrated improvements in automated segmentation of the cortex and subcortical structures. We believe the proposed contrast will facilitate translational studies in nonhuman primates to investigate the pathophysiology of neurodegenerative diseases that affect subcortical structures such as the basal ganglia in humans.

Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders

Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis, treatment, and tracking of complex conditions, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. While no definitive methods of diagnosis or treatment exist for either disease, researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers. Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment. However, such techniques require further development aimed at improving transparency, adaptability, and reproducibility. In this review, we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer's and Parkinson's diseases.

Multisite reproducibility of quantitative susceptibility mapping and effective transverse relaxation rate in deep gray matter at 3 T using locally optimized sequences in 24 traveling heads

Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R₂ *) mapping from multiecho T₂ *-weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R₂ * reproducibility at 3 T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross-site variability through postprocessing approaches. Twenty-four healthy subjects traveled between three sites and were scanned twice at each site. Scan-rescan measurements from seven deep gray matter regions were used for assessing within-site and cross-site reproducibility using intraclass correlation coefficient (ICC) and within-subject standard deviation (SDw) measures. In addition, multiple QSM and R₂ * postprocessing options were investigated with the aim to minimize cross-site sequence-related variations, including: mask generation approach, echo-timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same-subject cross-site region of interest measurements for QSM and R₂ * were highly correlated (R²  ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R₂ *, respectively). The mean cross-site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s^-1 for R₂ *. For within-site measurements of QSM and R₂ *, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s^-1 , respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross-site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts.

Brain volume and magnetic susceptibility differences in children and adolescents with prenatal alcohol exposure

BACKGROUND

Prenatal alcohol exposure (PAE) can negatively affect brain development thereby increasing the risk of cognitive deficits, behavioral challenges, and mental health problems. Brain iron is important for a number of physiological processes for healthy brain development. Animal studies show that PAE reduced brain iron; however, this has not been investigated in human children with PAE.

METHODS

We studied 20 children and adolescents with PAE and 44 unexposed children and adolescents aged 7.5 to 15 years. All children underwent quantitative susceptibility mapping and T1-weighted magnetic resonance imaging scans. Susceptibility and volume measurements of the caudate, putamen, pallidum, thalamus, amygdala, hippocampus, and nucleus accumbens were extracted using FreeSurfer. ANCOVAs were used to compare volume and susceptibility between groups for each region of interest, controlling for age and gender. For structures where susceptibility differed by group, we also tested for an association between intelligence quotient (IQ) and susceptibility.

RESULTS

There were no significant group differences in susceptibility after multiple comparison correction, though the PAE group had higher susceptibility in the thalamus compared to unexposed participants before correction (p = 0.032, q = 0.230). There was no association between IQ and thalamus susceptibility. The PAE group had significantly lower volume in the bilateral caudate, bilateral pallidum, and left putamen.

CONCLUSIONS

These findings suggest susceptibility may be altered in children and adolescents with PAE, though more research is needed. Volume reductions are consistent with previous literature and likely underlie cognitive and behavioral deficits associated with PAE.

Hippocampal glutathione depletion with enhanced iron level in patients with mild cognitive impairment and Alzheimer’s disease compared with healthy elderly participants

Oxidative stress has been implicated in Alzheimer’s disease, and it is potentially driven by the depletion of primary antioxidant, glutathione, as well as elevation of the pro-oxidant, iron. Present study evaluates glutathione level by magnetic resonance spectroscopy, iron deposition by quantitative susceptibility mapping in left hippocampus, as well as the neuropsychological scores of healthy old participants (N = 25), mild cognitive impairment (N = 16) and Alzheimer’s disease patients (N = 31). Glutathione was found to be significantly depleted in mild cognitive impaired (P &lt; 0.05) and Alzheimer’s disease patients (P &lt; 0.001) as compared with healthy old participants. A significant higher level of iron was observed in left hippocampus region for Alzheimer’s disease patients as compared with healthy old (P &lt; 0.05) and mild cognitive impairment (P &lt; 0.05). Multivariate receiver-operating curve analysis for combined glutathione and iron in left hippocampus region provided diagnostic accuracy of 82.1%, with 81.8% sensitivity and 82.4% specificity for diagnosing Alzheimer’s disease patients from healthy old participants. We conclude that tandem glutathione and iron provides novel avenue to investigate further research in Alzheimer’s disease.

Quantitative susceptibility mapping (QSM) of the cardiovascular system: challenges and perspectives

Quantitative susceptibility mapping (QSM) is a powerful, non-invasive, magnetic resonance imaging (MRI) technique that relies on measurement of magnetic susceptibility. So far, QSM has been employed mostly to study neurological disorders characterized by iron accumulation, such as Parkinson's and Alzheimer's diseases. Nonetheless, QSM allows mapping key indicators of cardiac disease such as blood oxygenation and myocardial iron content. For this reason, the application of QSM offers an unprecedented opportunity to gain a better understanding of the pathophysiological changes associated with cardiovascular disease and to monitor their evolution and response to treatment. Recent studies on cardiovascular QSM have shown the feasibility of a non-invasive assessment of blood oxygenation, myocardial iron content and myocardial fibre orientation, as well as carotid plaque composition. Significant technical challenges remain, the most evident of which are related to cardiac and respiratory motion, blood flow, chemical shift effects and susceptibility artefacts. Significant work is ongoing to overcome these challenges and integrate the QSM technique into clinical practice in the cardiovascular field.

Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks

Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that produces spatially resolved magnetic susceptibility maps from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but accumulates errors. This study aims to overcome existing limitations by developing a Laplacian-of-Trigonometric-functions (LoT) enhanced deep neural network for near-instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MRI phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the proposed neural networks. The proposed iQFM and iQSM methods in healthy subjects yielded comparable results to those involving the intermediate steps while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. High susceptibility contrast between multiple sclerosis lesions and healthy tissue was also achieved using the proposed methods. Comparative studies indicated that the most significant contributor to iQFM and iQSM over conventional multi-step methods was the elimination of traditional Laplacian unwrapping. The reconstruction time on the order of minutes for traditional approaches was shortened to around 0.1 seconds using the trained iQFM and iQSM neural networks.

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.

Motor Band Sign in Motor Neuron Disease: A Marker for Upper Motor Neuron Involvement

BACKGROUND AND OBJECTIVE

The prevalence and role of the motor band sign (MBS) remain unclear in motor neuron disease. We report the frequency of MBS in amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS), its correlation with clinical upper motor neuron (UMN) signs, and prognostic value in ALS.

METHODS

We conducted a retrospective study of ALS, PLS, and controls with retrievable MRI between 2010 and 2018. We compared the frequencies of MBS across the three groups, and studied correlation between susceptibility-weighted MRI measurements in primary motor cortices and contralateral UMN features. Clinical outcomes were compared between ALS with and without MBS.

RESULTS

Thirteen ALS, 5 PLS, and 10 controls were included (median age 60 years, IQR 54-66 years; 14/28 males). MBS was present in 9/13 (69.2%, 95% CI 38.9-89.6%) and 4/5 (80.0%, 95% CI 29.9-99.0%) of ALS and PLS, respectively, and none in controls. 2/13 (15.4%, 95% CI 2.7-46.3%) ALS and 3/5 (60.0%, 95% CI 17.0-92.7%) PLS had MBS in the absence of corticospinal T2/FLAIR hyperintensity sign. Susceptibility measurements in left motor cortices had a significantly positive correlation with contralateral UMN signs in ALS (τ_b = 0.628, p = 0.03). Similar but nonsignificant trends was observed for right motor cortices in ALS (τ_b = 0.516, p = 0.07). There were no significant differences in mRS at last follow-up, mortality, or time from symptom onset to last follow-up between ALS patients with and without MBS.

CONCLUSIONS

We provide limited evidence that MBS and susceptibility quantification measurements in motor cortices may serve as surrogate markers of UMN involvement in motor neuron disease.

MULTI-parametric MR imaging with fLEXible design (MULTIPLEX)

PURPOSE

To introduce a gradient echo (GRE) -based method, namely MULTIPLEX, for single-scan 3D multi-parametric MRI with high resolution, signal-to-noise ratio (SNR), accuracy, efficiency, and acquisition flexibility.

THEORY

With a comprehensive design with dual-repetition time (TR), dual flip angle (FA), multi-echo, and optional flow modulation features, the MULTIPLEX signals contain information on radiofrequency (RF) B_1t fields, proton density, T₁ , susceptibility and blood flows, facilitating multiple qualitative images and parametric maps.

METHODS

MULTIPLEX was evaluated on system phantom and human brains, via visual inspection for image contrasts and quality or quantitative evaluation via simulation, phantom scans and literature comparison. Region-of-interest (ROI) analysis was performed on parametric maps of the system phantom and brain scans, extracting the mean and SD of the T₁ , T2∗ , proton density (PD), and/or quantitative susceptibility mapping (QSM) values for comparison with reference values or literature.

RESULTS

One MULTIPLEX scan offers multiple sets of images, including but not limited to: composited PDW/T₁ W/ T2∗ W, aT₁ W, SWI, MRA (optional), B_1t map, T₁ map, T2∗ / R2∗ maps, PD map, and QSM. The quantitative error of phantom T₁ , T2∗ and PD mapping were <5%, and those in brain scans were in good agreement with literature. MULTIPLEX scan times for high resolution (0.68 × 0.68 × 2 mm³ ) whole brain coverage were about 7.5 min, while processing times were <1 min. With flow modulation, additional MRA images can be obtained without affecting the quality or accuracy of other images.

CONCLUSION

The proposed MUTLIPLEX method possesses great potential for multi-parametric MR imaging.

Endovascular treatment for cerebral venous thrombosis: current status, challenges, and opportunities

Cerebral venous thrombosis (CVT) mostly affects young people. So far, endovascular treatment (EVT) has not been shown to be beneficial in CVT, partially because venous EVT tools are not yet fully optimized, and therefore EVT is only used as a rescue treatment in rare cases. Identifying a subgroup of CVT patients that could benefit from EVT is challenging, given the milder course of disease compared with acute ischemic stroke, the paucity of data on prognostic factors (both in the clinical and imaging domain), and the lack of consensus on what constitutes 'technical success' in CVT EVT. In this review, we discuss the major obstacles that are encountered when trying to identify CVT patients that may benefit from EVT, and propose a roadmap that could help to overcome these challenges in the near future.

Methods for Monitoring Risk of Hypoxic Damage in Fetal and Neonatal Brains: A Review

Fetal, perinatal, and neonatal asphyxia are vital health issues for the most vulnerable groups in human beings, including fetuses, newborns, and infants. Severe reduction in oxygen and blood supply to the fetal brain can cause hypoxic-ischemic encephalopathy (HIE), leading to long-term neurological disorders, including mental impairment and cerebral palsy. Such neurological disorders are major healthcare concerns. Therefore, there has been a continuous effort to develop clinically useful diagnostic tools for accurately and quantitatively measuring and monitoring blood and oxygen supply to the fetal and neonatal brain to avoid severe consequences of asphyxia HIE and neonatal encephalopathy. Major diagnostic technologies used for this purpose include fetal heart rate monitoring, fetus scalp blood sampling, ultrasound imaging, magnetic resonance imaging, X-ray computed tomography, and nuclear medicine. In addition, given the limitations and shortcomings of traditional diagnostic methods, emerging technologies such as near-infrared spectroscopy and photoacoustic imaging have also been introduced as stand-alone or complementary solutions to address this critical gap in fetal and neonatal care. This review provides a thorough overview of the traditional and emerging technologies for monitoring fetal and neonatal brain oxygenation status and describes their clinical utility, performance, advantages, and disadvantages.

Clinical and neuroimaging findings in MOGAD-MRI and OCT

Myelin oligodendrocyte glycoprotein antibody-associated disorders (MOGAD) are rare in both children and adults, and have been recently suggested to be an autoimmune neuroinflammatory group of disorders that are different from aquaporin-4 autoantibody-associated neuromyelitis optica spectrum disorder and from classic multiple sclerosis. In-vivo imaging of the MOGAD patient central nervous system has shown some distinguishing features when evaluating magnetic resonance imaging of the brain, spinal cord and optic nerves, as well as retinal imaging using optical coherence tomography. In this review, we discuss key clinical and neuroimaging characteristics of paediatric and adult MOGAD. We describe how these imaging techniques may be used to study this group of disorders and discuss how image analysis methods have led to recent insights for consideration in future studies.

Noninvasive magnetic resonance imaging techniques in mild traumatic brain injury research and diagnosis

Mild traumatic brain injury (mTBI), frequently referred to as concussion, is one of the most common neurological disorders. The underlying neural mechanisms of functional disturbances in the brains of concussed individuals remain elusive. Novel forms of brain imaging have been developed to assess patients postconcussion, including functional magnetic resonance imaging (fMRI), susceptibility-weighted imaging (SWI), diffusion MRI (dMRI), and perfusion MRI [arterial spin labeling (ASL)], but results have been mixed with a more common utilization in the research environment and a slower integration into the clinical setting. In this review, the benefits and drawbacks of the methods are described: fMRI is an effective method in the diagnosis of concussion but it is expensive and time-consuming making it difficult for regular use in everyday practice; SWI allows detection of microhemorrhages in acute and chronic phases of concussion; dMRI is primarily used for the detection of white matter abnormalities, especially axonal injury, specific for mTBI; and ASL is an alternative to the BOLD method with its ability to track cerebral blood flow alterations. Thus, the absence of a universal diagnostic neuroimaging method suggests a need for the adoption of a multimodal approach to the neuroimaging of mTBI. Taken together, these methods, with their underlying functional and structural features, can contribute from different angles to a deeper understanding of mTBI mechanisms such that a comprehensive diagnosis of mTBI becomes feasible for the clinician.

Quantitative Susceptibility Mapping of the Basal Ganglia and Thalamus at 9.4 Tesla

The thalamus (Th) and basal ganglia (BG) are central subcortical connectivity hubs of the human brain, whose functional anatomy is still under intense investigation. Nevertheless, both substructures contain a robust and reproducible functional anatomy. The quantitative susceptibility mapping (QSM) at ultra-high field may facilitate an improved characterization of the underlying functional anatomy in vivo. We acquired high-resolution QSM data at 9.4 Tesla in 21 subjects, and analyzed the thalamic and BG by using a prior defined functional parcellation. We found a more substantial contribution of paramagnetic susceptibility sources such as iron in the pallidum in contrast to the caudate, putamen, and Th in descending order. The diamagnetic susceptibility sources such as myelin and calcium revealed significant contributions in the Th parcels compared with the BG. This study presents a detailed nuclei-specific delineation of QSM-provided diamagnetic and paramagnetic susceptibility sources pronounced in the BG and the Th. We also found a reasonable interindividual variability as well as slight hemispheric differences. The results presented here contribute to the microstructural knowledge of the Th and the BG. In specific, the study illustrates QSM values (myelin, calcium, and iron) in functionally similar subregions of the Th and the BG.

Age-related magnetic susceptibility changes in deep grey matter and cerebral cortex of normal young and middle-aged adults depicted by whole brain analysis

Background

Iron accumulates in brain tissue in healthy subjects during aging. Our goal was to conduct a detailed analysis of iron deposition patterns in the cerebral deep grey matter and cortex using region-based and whole-brain analyses of brain magnetic susceptibility.

Methods

Brain MRI was performed in 95 healthy individuals aged between 21 and 58 years on a 3T scanner. MRI protocol included T1-weighted (T1W) magnetization-prepared rapid acquisition with gradient echo images and 3D flow-compensated multi-echo gradient-echo images for quantitative susceptibility mapping (QSM). In the region-based analysis, QSM and T1W images entered an automated multi-atlas segmentation pipeline and regional mean bulk susceptibility values were calculated. The whole-brain analysis included a non-linear transformation of QSM images to the standard MNI template. For the whole-brain analysis voxel-wise maps of linear regression slopes β and P values were calculated. Regional masks of cortical voxels with a significant association between susceptibility and age were created and further analyzed.

Results

In cortical regions, the highest increase of susceptibility values with age was found in areas involved in motor functions (precentral and postcentral areas, premotor cortex), in cognitive processing (prefrontal cortex, superior temporal gyrus, insula, precuneus), and visual processing (occipital gyri, cuneus, posterior cingulum, fusiform, calcarine and lingual gyrus). Thalamic susceptibility increased until the fourth decade and decreased thereafter with the exception of the pulvinar where susceptibility increase was observed throughout the adult lifespan. Deep grey matter structures with the highest increase of susceptibility values with age included the red nucleus, putamen, substantia nigra, dentate nucleus, external globus pallidus, caudate nucleus, and the subthalamic nucleus in decreasing order.

Conclusions

Accumulation of iron in basal ganglia follows a linear pattern whereas in the thalamus, pulvinar, precentral cortex, and precuneus, it follows a quadratic or exponential pattern. Age-related changes of iron content are different in the pulvinar and the rest of the thalamus as well as in internal and external globus pallidus. In the cortex, areas involved in motor and cognitive functions and visual processing show the highest iron increase with aging. We suggest that the departure from normal patterns of regional brain iron trajectories during aging may be helpful in the detection of subtle neurodegenerative and neuroinflammatory processes.

Combining structural and metabolic markers in a quantitative MRI study of motor neuron diseases

OBJECTIVE

To assess the performance of a combination of three quantitative MRI markers (iron deposition, basal neuronal metabolism, and regional atrophy) for differential diagnosis between amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS).

METHODS

In total, 33 ALS, 12 PLS, and 28 healthy control (HC) subjects underwent a 3T MRI study including single- and multi-echo sequences for gray matter (GM) volumetry and quantitative susceptibility mapping (QSM) and a pseudo-continuous arterial spin labeling (ASL) sequence for cerebral blood flow (CBF) measurement. Mean values of QSM, CBF, and GM volumes were extracted in the motor cortex, basal ganglia, thalamus, amygdala, and hippocampus. A generalized linear model was applied to the three measures to binary discriminate between groups. The diagnostic performances were evaluated via receiver operating characteristic analyses.

RESULTS

A significant discrimination was obtained: between ALS and HCs in the left and right motor cortex, where QSM increases were respectively associated with disability scores and disease duration; between PLS and ALS in the left motor cortex, where PLS patients resulted significantly more atrophic; between ALS and HC in the right motor cortex, where GM volumes were associated with upper motor neuron scores. Significant discrimination between ALS and HC was achieved in subcortical structures only combining all three parameters.

INTERPRETATION

While increased QSM values in the motor cortex of ALS patients is a consolidated finding, combining QSM, CBF, and GM volumetry shows higher diagnostic potential for differentiating ALS patients from HC subjects and, in the motor cortex, between ALS and PLS.

Quantitative MRI susceptibility mapping reveals cortical signatures of changes in iron, calcium and zinc in malformations of cortical development in children with drug-resistant epilepsy

OBJECTIVE

Malformations of cortical development (MCD), including focal cortical dysplasia (FCD), are the most common cause of drug-resistant focal epilepsy in children. Histopathological lesion characterisation demonstrates abnormal cell types and lamination, alterations in myelin (typically co-localised with iron), and sometimes calcification. Quantitative susceptibility mapping (QSM) is an emerging MRI technique that measures tissue magnetic susceptibility (χ) reflecting it's mineral composition. We used QSM to investigate abnormal tissue composition in a group of children with focal epilepsy with comparison to effective transverse relaxation rate (R2*) and Synchrotron radiation X-ray fluorescence (SRXRF) elemental maps. Our primary hypothesis was that reductions in χ would be found in FCD lesions, resulting from alterations in their iron and calcium content. We also evaluated deep grey matter nuclei for changes in χ with age.

METHODS

QSM and R2* maps were calculated for 40 paediatric patients with suspected MCD (18 histologically confirmed) and 17 age-matched controls. Patients' sub-groups were defined based on concordant electro-clinical or histopathology data. Quantitative investigation of QSM and R2* was performed within lesions, using a surface-based approach with comparison to homologous regions, and within deep brain regions using a voxel-based approach with regional values modelled with age and epilepsy as covariates. Synchrotron radiation X-ray fluorescence (SRXRF) was performed on brain tissue resected from 4 patients to map changes in iron, calcium and zinc and relate them to MRI parameters.

RESULTS

Compared to fluid-attenuated inversion recovery (FLAIR) or T1-weighted imaging, QSM improved lesion conspicuity in 5% of patients. In patients with well-localised lesions, quantitative profiling demonstrated decreased χ, but not R2*, across cortical depth with respect to the homologous regions. Contra-lateral homologous regions additionally exhibited increased χ at 2-3 mm cortical depth that was absent in lesions. The iron decrease measured by the SRXRF in FCDIIb lesions was in agreement with myelin reduction observed by Luxol Fast Blue histochemical staining. SRXRF analysis in two FCDIIb tissue samples showed increased zinc and calcium in one patient, and decreased iron in the brain region exhibiting low χ and high R2* in both patients. QSM revealed expected age-related changes in the striatum nuclei, substantia nigra, sub-thalamic and red nucleus.

CONCLUSION

QSM non-invasively revealed cortical/sub-cortical tissue alterations in MCD lesions and in particular that χ changes in FCDIIb lesions were consistent with reduced iron, co-localised with low myelin and increased calcium and zinc content. These findings suggest that measurements of cortical χ could be used to characterise tissue properties non-invasively in epilepsy lesions.

QSM reconstruction challenge 2.0: Design and report of results

PURPOSE

The aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data.

METHODS

A two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground truth data available to mimic the clinical setting. At the second stage, ground truth data were provided for parameter optimization. Submissions were evaluated using eight numerical metrics and visual ratings.

RESULTS

A total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and total variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art.

CONCLUSION

The synthetic data provide a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithms produced the best results among all metrics. Future QSM challenges should assess whether this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.

PRATEEK: Integration of Multimodal Neuroimaging Data to Facilitate Advanced Brain Research

BACKGROUND

In vivo neuroimaging modalities such as magnetic resonance imaging (MRI), functional MRI (fMRI), magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), and quantitative susceptibility mapping (QSM) are useful techniques to understand brain anatomical structure, functional activity, source localization, neurochemical profiles, and tissue susceptibility respectively. Integrating unique and distinct information from these neuroimaging modalities will further help to enhance the understanding of complex neurological diseases.

OBJECTIVE

To develop a processing scheme for multimodal data integration in a seamless manner on healthy young population, thus establishing a generalized framework for various clinical conditions (e.g., Alzheimer's disease).

METHODS

A multimodal data integration scheme has been developed to integrate the outcomes from multiple neuroimaging data (fMRI, MEG, MRS, and QSM) spatially. Furthermore, the entire scheme has been incorporated into a user-friendly toolbox- "PRATEEK".

RESULTS

The proposed methodology and toolbox has been tested for viability among fourteen healthy young participants. The data-integration scheme was tested for bilateral occipital cortices as the regions of interest and can also be extended to other anatomical regions. Overlap percentage from each combination of two modalities (fMRI-MRS, MEG-MRS, fMRI-QSM, and fMRI-MEG) has been computed and also been qualitatively assessed for combinations of the three (MEG-MRS-QSM) and four (fMRI-MEG-MRS-QSM) modalities.

CONCLUSION

This user-friendly toolbox minimizes the need of an expertise in handling different neuroimaging tools for processing and analyzing multimodal data. The proposed scheme will be beneficial for clinical studies where geometric information plays a crucial role for advance brain research.

Decompose quantitative susceptibility mapping (QSM) to sub-voxel diamagnetic and paramagnetic components based on gradient-echo MRI data

PURPOSE

A method named DECOMPOSE-QSM is developed to decompose bulk susceptibility measured with QSM into sub-voxel paramagnetic and diamagnetic components based on a three-pool complex signal model.

METHODS

Multi-echo gradient echo signal is modeled as a summation of three weighted exponentials corresponding to three types of susceptibility sources: reference susceptibility, diamagnetic and paramagnetic susceptibility relative to the reference. Paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS) maps are constructed to represent the sub-voxel compartments by solving for linear and nonlinear parameters in the model.

RESULTS

Numerical forward simulation and phantom validation confirmed the ability of DECOMPOSE-QSM to separate the mixture of paramagnetic and diamagnetic components. The PCS obtained from temperature-variant brainstem imaging follows the Curie's Law, which further validated the model and the solver. Initial in vivo investigation of human brain images showed the ability to extract sub-voxel PCS and DCS sources that produce visually enhanced contrast between brain structures comparing to threshold QSM.

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

INTRODUCTION

Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can substantially shorten the scan time or enhance the spatial resolution without increasing scan time; however, this may lead to significant DGM susceptibility underestimation.

METHOD

A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages. The xQSM method is compared with two conventional dipole inversion methods using simulated and in vivo experiments from 4 healthy subjects at 3T. Pre-processed magnetic field maps are extended symmetrically from the centre of globus pallidus in the coronal plane to simulate QSM acquisitions of difference spatial coverages, ranging from 100% (∼32mm) to 400% (∼128mm) of the actual DGM physical size.

RESULTS

The proposed xQSM network led to the lowest DGM contrast loss in both simulated and in vivo subjects, with the smallest susceptibility variation range across all spatial coverages. For the digital brain phantom simulation, xQSM improved the DGM susceptibility underestimation more than 20% in small spatial coverages, as compared to conventional methods. For the in vivo acquisition, less than 5% DGM susceptibility error was achieved in 48mm axial slabs using the xQSM network, while a minimum of 112mm coverage was required for conventional methods. It is also shown that the background field removal process performed worse in reduced brain coverages, which further deteriorated the subsequent dipole inversion.

CONCLUSION

The recently proposed deep learning-based xQSM method significantly improves the accuracy of DGM QSM from small spatial coverages as compared with conventional QSM algorithms, which can shorten DGM QSM acquisition time substantially.

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction

Quantitative susceptibility mapping (QSM) and R2* mapping are MRI post-processing methods that quantify tissue magnetic susceptibility and transverse relaxation rate distributions. However, QSM and R2* acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal, as required by QSM, due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM and R2* acquisition. Magnitude, phase, R2*, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM), root-mean-squared error (RMSE), and region-of-interest susceptibility and R2* measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR, SSIM, and RMSE on the magnitude, R2*, local field, and susceptibility maps. Compared to two iterative and one deep learning methods, the DCRNet method demonstrated a 3.2% to 9.1% accuracy improvement in deep grey matter susceptibility when accelerated by a factor of four. The DCRNet also dramatically shortened the reconstruction time of single 2D brain images from 36-140 seconds using conventional approaches to only 15-70 milliseconds.

Single-step calculation of susceptibility through multiple orientation sampling

Quantitative susceptibility mapping (QSM) was developed to estimate the spatial distribution of magnetic susceptibility from MR signal phase acquired using a gradient echo (GRE) sequence. The field-to-susceptibility inversion in QSM is known to be ill-posed and needs numerical stabilization through either regularization or data oversampling. The calculation of susceptibility through the multiple orientation sampling (COSMOS) method uses phase data acquired at three or more head orientations to achieve a well-conditioned field-to-susceptibility inversion and is often considered the gold standard for in vivo QSM. However, the conventional COSMOS approach, here named multistep COSMOS (MSCOSMOS), solves the dipole inversion from the local field derived from raw GRE phase through multiple steps of phase preprocessing. Error propagations between these consecutive phase processing steps can thus affect the final susceptibility quantification. On the other hand, recently proposed single-step QSM (SSQSM) methods aim to solve an integrated inversion from unprocessed or total phase to mitigate such error propagations but have been limited to single orientation QSM. This study therefore aimed to test the feasibility of using single-step COSMOS (SSCOSMOS) to jointly perform background field removal and dipole inversion with multiple orientation sampling, which could serve as a better standard for gauging SSQSM methods. We incorporated multiple spherical mean value (SMV) kernels of various radii with the dipole inversion in SSCOSMOS. QSM reconstructions with SSCOSMOS and MSCOSMOS were compared using both simulations with a numerical head phantom and in vivo human brain data. SSCOSMOS permitted integrated background removal and dipole inversion without the need to adjust any regularization parameters. In addition, with sufficiently large SMV kernels, SSCOSMOS performed consistently better than MSCOSMOS in all the tested error metrics in our simulations, giving better susceptibility quantification and smaller reconstruction error. Consistent tissue susceptibility values were obtained between SSCOSMOS and MSCOSMOS.

Emerging Applications for Quantitative Susceptibility Mapping in the Detection of Traumatic Brain Injury Pathology

Traumatic brain injury (TBI) is a common but heterogeneous injury underpinned by numerous complex and interrelated pathophysiological mechanisms. An essential trace element, iron is abundant within the brain and involved in many fundamental neurobiological processes, including oxygen transportation, oxidative phosphorylation, myelin production and maintenance, as well as neurotransmitter synthesis and metabolism. Excessive levels of iron are neurotoxic and thus iron homeostasis is tightly regulated in the brain, however, many details about the mechanisms by which this is achieved are yet to be elucidated. A key mediator of oxidative stress, mitochondrial dysfunction and neuroinflammatory response, iron dysregulation is an important contributor to secondary injury in TBI. Advances in neuroimaging that leverage magnetic susceptibility properties have enabled increasingly comprehensive investigations into the distribution and behaviour of iron in the brain amongst healthy individuals as well as disease states such as TBI. Quantitative Susceptibility Mapping (QSM) is an advanced neuroimaging technique that promises quantitative estimation of local magnetic susceptibility at the voxel level. In this review, we provide an overview of brain iron and its homeostasis, describe recent advances enabling applications of QSM within the context of TBI and summarise the current state of the literature. Although limited, the emergent research suggests that QSM is a promising neuroimaging technique that can be used to investigate a host of pathophysiological changes that are associated with TBI.

Voxel-based quantitative susceptibility mapping revealed increased cerebral iron over the whole brain in chronic migraine

Background

The previous documents demonstrated that iron deposition was identified in brain deep nuclei and periaqueductal gray matter region in chronic migraine (CM), and less is known about the cerebral iron deposition in CM. The aim of this study is to investigate the cerebral iron deposition in CM using an advanced voxel-based quantitative susceptibility mapping.

Methods

A multi-echo gradient echo MR sequence was obtained from 14 CM patients and 28 normal controls (NC), and quantitative susceptibility mapping images were reconstructed and voxel-based analysis was performed over the whole cerebrum. The susceptibility value of all the positive brain regions was extracted and correlation was calculated between the susceptibility value and the clinical variables.

Results

The brain regions with increased susceptibility value in CM patients located in right precuneus, insula, supramarginal gyrus, dorsolateral superior frontal gyrus, postcentral gyrus, cuneus and left postcentral gyrus compared with NC. The correlation analysis demonstrated that a positive correlation was identified between susceptibility value of all the positive brain regions and VAS score.

Conclusion

The current study demonstrated increased cerebral iron deposition presented in chronic patients, which suggested that increased cerebral iron deposition might play a role in the migraine chronicization.

Change in Susceptibility Values in Knee Cartilage After Marathon Running Measured Using Quantitative Susceptibility Mapping

BACKGROUND

Quantitative susceptibility mapping (QSM) has been used to study the magnetic susceptibility properties of collagen fibers in articular cartilage; however, it is unclear whether QSM is sensitive to changes due to degradation caused by long-distance running. It is clinically important to understand the link between long-distance running and microstructural changes in knee cartilage.

PURPOSE

To investigate the ability of QSM to assess microstructural changes within cartilage after repetitive loading.

STUDY TYPE

Prospective.

POPULATION

Thirteen recreational, male long-distance runners.

FIELD STRENGTH/SEQUENCE

Three-dimensional gradient recalled echo acquired at 3 T.

ASSESSMENT

Magnetic resonance imaging (MRI) and 3D kinematics (translations and rotations during treadmill walking and running) of the knee joint were collected before and after marathon running. The compartments for analysis included the patella, trochlea, and subregions of femoral and tibial cartilage. Changes in regional susceptibility and cartilage thickness were calculated after marathon running. A susceptibility profile was obtained by fitting susceptibility as a function of the normalized depth of cartilage from the superficial to deep layers.

STATISTICAL TESTS

Paired t-test or Wilcoxon signed-rank test, 95% confidence interval (CI) of the depth-wise susceptibility profile, Pearson correlation or Spearman correlation.

RESULTS

There was a statistically significant increase in susceptibility value in the weight-bearing region of central medial femoral cartilage (cMF-c) after marathon running (pre-marathon: -0.0219 ± 0.0151 ppm, post-marathon: -0.0070 ± 0.0213 ppm, P < 0.05), while the cartilage thickness did not show significant changes in any regions (P-value range: 0.068-0.963). Significant susceptibility elevations occurred in the middle and deep layers of cMF-c (95% CIs did not overlap). A trend toward a positive correlation was found between the changes in susceptibility value in cMF-c and proximal-distal translation of the knee joint during walking (r = 0.55, P = 0.101) and running (r = 0.57, P = 0.089).

DATA CONCLUSION

Localized magnetic susceptibility alterations were observed within knee cartilage in the weight-bearing area after repetitive loading without any morphologic changes.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Non-Invasive Evaluation of Cerebral Microvasculature Using Pre-Clinical MRI: Principles, Advantages and Limitations

Alterations to the cerebral microcirculation have been recognized to play a crucial role in the development of neurodegenerative disorders. However, the exact role of the microvascular alterations in the pathophysiological mechanisms often remains poorly understood. The early detection of changes in microcirculation and cerebral blood flow (CBF) can be used to get a better understanding of underlying disease mechanisms. This could be an important step towards the development of new treatment approaches. Animal models allow for the study of the disease mechanism at several stages of development, before the onset of clinical symptoms, and the verification with invasive imaging techniques. Specifically, pre-clinical magnetic resonance imaging (MRI) is an important tool for the development and validation of MRI sequences under clinically relevant conditions. This article reviews MRI strategies providing indirect non-invasive measurements of microvascular changes in the rodent brain that can be used for early detection and characterization of neurodegenerative disorders. The perfusion MRI techniques: Dynamic Contrast Enhanced (DCE), Dynamic Susceptibility Contrast Enhanced (DSC) and Arterial Spin Labeling (ASL), will be discussed, followed by less established imaging strategies used to analyze the cerebral microcirculation: Intravoxel Incoherent Motion (IVIM), Vascular Space Occupancy (VASO), Steady-State Susceptibility Contrast (SSC), Vessel size imaging, SAGE-based DSC, Phase Contrast Flow (PC) Quantitative Susceptibility Mapping (QSM) and quantitative Blood-Oxygenation-Level-Dependent (qBOLD). We will emphasize the advantages and limitations of each strategy, in particular on applications for high-field MRI in the rodent's brain.

xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks

Quantitative susceptibility mapping (QSM) provides a valuable MRI contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing noise regularization and modified octave convolutional layers into a U-net backbone and trained with synthetic and in vivo datasets, respectively. The xQSM method was compared with two recent deep learning (QSMnet⁺ and DeepQSM) and two conventional dipole inversion (MEDI and iLSQR) methods, using both digital simulations and in vivo experiments. Reconstruction error metrics, including peak signal-to-noise ratio, structural similarity, normalized root mean squared error and deep gray matter susceptibility measurements, were evaluated for comparison of the different methods. The results showed that the proposed xQSM network trained with in vivo datasets achieved the best reconstructions of all the deep learning methods. In particular, it led to, on average, 32.3%, 25.4% and 11.7% improvement in the accuracy of globus pallidus susceptibility estimation for digital simulations and 39.3%, 21.8% and 6.3% improvements for in vivo acquisitions compared with DeepQSM, QSMnet⁺ and iLSQR, respectively. It also exhibited the highest linearity against different susceptibility intensity scales and demonstrated the most robust generalization capability to various spatial resolutions of all the deep learning methods. In addition, the xQSM method also substantially shortened the reconstruction time from minutes using MEDI to only a few seconds.

Quantitative susceptibility mapping of the rat brain after traumatic brain injury

The primary lesion arising from the initial insult after traumatic brain injury (TBI) triggers a cascade of secondary tissue damage, which may also progress to connected brain areas in the chronic phase. The aim of this study was, therefore, to investigate variations in the susceptibility distribution related to these secondary tissue changes in a rat model after severe lateral fluid percussion injury. We compared quantitative susceptibility mapping (QSM) and R₂ * measurements with histological analyses in white and grey matter areas outside the primary lesion but connected to the lesion site. We demonstrate that susceptibility variations in white and grey matter areas could be attributed to reduction in myelin, accumulation of iron and calcium, and gliosis. QSM showed quantitative changes attributed to secondary damage in areas located rostral to the lesion site that appeared normal in R₂ * maps. However, combination of QSM and R₂ * was informative in disentangling the underlying tissue changes such as iron accumulation, demyelination, or calcifications. Therefore, combining QSM with R₂ * measurement can provide a more detailed assessment of tissue changes and may pave the way for improved diagnosis of TBI, and several other complex neurodegenerative diseases.

Quantitative Susceptibility Mapping of the Hippocampal Fimbria in Alzheimer's Disease

BACKGROUND

The fimbria is a small white matter bundle that connects the hippocampus to the rest of the brain. Damage to the hippocampal gray matter is established in Alzheimer's disease (AD), but the hippocampal fimbrial status in the pathogenesis of AD is unclear. AD-related demyelination and iron deposition alter the diamagnetic and paramagnetic composition of tissues, which can be measured by quantitative susceptibility mapping (QSM).

HYPOTHESIS

AD is associated with microstructural changes in the fimbria that might be detected by QSM.

STUDY TYPE

Retrospective cross-sectional study.

SUBJECTS

In all, 53 adults comprised of controls (n = 30), subjects with early stage AD (n = 13), and late stage AD (n = 10) who were classified according to their amyloid and tau status and presence of hippocampal atrophy.

FIELD STRENGTH / SEQUENCE

3T; 3D fast-field echo sequence for QSM analysis and 3D T₁ -weighted MP-RAGE sequence for anatomical analysis.

ASSESSMENT

Segmentation of the left hippocampal fimbria subfield was performed on T₁ -weighted images and was applied to the coregistered QSM map for extraction of the mean, median, minimum, and maximum values of QSM.

STATISTICAL TESTS

Group comparison of QSM values using analysis of variance (ANOVA) with post-hoc Tukey's test, accuracy of binary differentiation using receiver operating characteristic (ROC), and individual classification using discriminant analysis.

RESULTS

QSM_mean and QSM_median values were significantly different among the three groups (P < 0.05) and showed a shifting from negative in the control group to positive in the AD group. The control and early AD subjects, who have normal hippocampal volumes, were differentiated by the QSM_mean value (area under the curve [AUC] 0.744, P < 0.05) and the QSM_median value (AUC 0.782, P < 0.05). Up to 76% of subjects (inclusive of 26 controls and six with early AD) were correctly classified using a model incorporating clinical and radiologic data.

DATA CONCLUSION

The fimbria showed higher magnetic susceptibility in AD compared with controls.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 3.

Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM)

High-quality Quantitative Susceptibility Mapping (QSM) with Nonlinear Dipole Inversion (NDI) is developed with pre-determined regularization while matching the image quality of state-of-the-art reconstruction techniques and avoiding over-smoothing that these techniques often suffer from. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7 T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data.

Feasibility of quantitative susceptibility mapping (QSM) of the human kidney

Objective

To evaluate the feasibility of in-vivo quantitative susceptibility mapping (QSM) of the human kidney.

Methods

An axial single-breath-hold 3D multi-echo sequence (acquisition time 33 s) was completed on a 3 T-MRI-scanner (Magnetom Prisma, Siemens Healthineers, Erlangen, Germany) in 19 healthy volunteers. Graph-cut-based unwrapping combined with the T₂*-IDEAL approach was performed to remove the chemical shift of fat and to quantify QSM of the upper abdomen. Mean susceptibility values of the entire, renal cortex and medulla in both kidneys and the liver were determined and compared. Five subjects were measured twice to examine the reproducibility. One patient with severe renal fibrosis was included in the study to evaluate the potential clinical relevance of QSM.

Results

QSM was successful in 17 volunteers and the patient with renal fibrosis. Anatomical structures in the abdomen were clearly distinguishable by QSM and the susceptibility values obtained in the liver were comparable to those found in the literature. The results showed a good reproducibility. Besides, the mean renal QSM values obtained in healthy volunteers (0.04 ± 0.07 ppm for the right and − 0.06 ± 0.19 ppm for the left kidney) were substantially higher than that measured in the investigated fibrotic kidney (− 0.43 ± − 0.02 ppm).

Conclusion

QSM of the human kidney could be a promising approach for the assessment of information about microscopic renal tissue structure. Therefore, it might further improve functional renal MR imaging.

Electronic supplementary material

The online version of this article (10.1007/s10334-020-00895-9) contains supplementary material, which is available to authorized users.

Precise targeting of the globus pallidus internus with quantitative susceptibility mapping for deep brain stimulation surgery

OBJECTIVE

The goal of this study was to demonstrate the use of quantitative susceptibility mapping (QSM)-based images to precisely localize the globus pallidus internus (GPi) for deep brain stimulation (DBS) planning and to enhance postsurgical visualization of the DBS lead positions.

METHODS

Presurgical T1-weighted (T1w), T2-weighted (T2w), and QSM images as well as postsurgical CT images were obtained in 29 patients with Parkinson's disease. To enhance the contrast within the GP, a hybrid contrast was created by linearly combining T1w and QSM images. Contrast-to-noise ratios (CNRs) of the GPi on T1w, T2w, QSM, and hybrid images were compared. The CNR differences were tested using the 1-way ANOVA method. The visualization of the DBS lead position was demonstrated by merging the postsurgical CT with presurgical MR images.

RESULTS

The hybrid images yield the best CNRs for GPi depiction and the visualization of the postsurgical DBS lead position was significantly improved.

CONCLUSIONS

QSM-based images allow for confident localization of borders of the GPi that is superior to T1w and T2w images. High-contrast hybrid images can be used for precisely directed DBS targeting, e.g., GPi DBS for the treatment of advanced Parkinson's disease.

Learned Proximal Networks for Quantitative Susceptibility Mapping

Quantitative Susceptibility Mapping (QSM) estimates tissue magnetic susceptibility distributions from Magnetic Resonance (MR) phase measurements by solving an ill-posed dipole inversion problem. Conventional single orientation QSM methods usually employ regularization strategies to stabilize such inversion, but may suffer from streaking artifacts or over-smoothing. Multiple orientation QSM such as calculation of susceptibility through multiple orientation sampling (COSMOS) can give well-conditioned inversion and an artifact free solution but has expensive acquisition costs. On the other hand, Convolutional Neural Networks (CNN) show great potential for medical image reconstruction, albeit often with limited interpretability. Here, we present a Learned Proximal Convolutional Neural Network (LP-CNN) for solving the ill-posed QSM dipole inversion problem in an iterative proximal gradient descent fashion. This approach combines the strengths of data-driven restoration priors and the clear interpretability of iterative solvers that can take into account the physical model of dipole convolution. During training, our LP-CNN learns an implicit regularizer via its proximal, enabling the decoupling between the forward operator and the data-driven parameters in the reconstruction algorithm. More importantly, this framework is believed to be the first deep learning QSM approach that can naturally handle an arbitrary number of phase input measurements without the need for any ad-hoc rotation or re-training. We demonstrate that the LP-CNN provides state-of-the-art reconstruction results compared to both traditional and deep learning methods while allowing for more flexibility in the reconstruction process.

Imaging microstructure with diffusion and susceptibility MR: neuronal density correlation in Disrupted-in-Schizophrenia-1 mutant mice

PURPOSE

To probe cerebral microstructural abnormalities and assess changes of neuronal density in Disrupted-in-Schizophrenia-1 (DISC1) mice using non-Gaussian diffusion and quantitative susceptibility mapping (QSM).

MATERIALS AND METHODS

Brain specimens of transgenic DISC1 mice (n = 8) and control mice (n = 7) were scanned. Metrics of neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging (DKI), as well as QSM, were acquired. Cell counting was performed on Nissl-stained sections. Group differences of imaging metrics and cell density were assessed. Pearson correlations between imaging metrics and cell densities were also examined.

RESULTS

Significant increases of neuronal density were observed in the hippocampus of DISC1 mice. DKI metrics such as mean kurtosis exhibited significant group differences in the caudate putamen (P = 0.015), cerebral cortex (P = 0.021), and hippocampus (P = 0.011). However, DKI metrics did not correlate with cell density. In contrast, significant positive correlation between density of neurons and the neurite density index of NODDI in the hippocampus was observed (r = 0.783, P = 0.007). Significant correlation between density of neurons and susceptibility (r = 0.657, P = 0.039), as well as between density of neuroglia and susceptibility (r = 0.750, P = 0.013), was also observed in the hippocampus.

CONCLUSION

The imaging metrics derived from DKI were not sensitive specifically to cell density, while NODDI could provide diffusion metrics sensitive to density of neurons. The magnetic susceptibility values derived from the QSM method can serve as a sensitive biomarker for quantifying neuronal density.