Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an essential biomarker used to directly assess tissue viability and function in neurologic disorders. In ischemic stroke, for example, increased OEF can indicate the presence of penumbra-tissue with low perfusion yet intact cellular integrity-making it a primary therapeutic target. However, practical OEF mapping methods are not currently available in clinical settings, owing to the impractical data acquisitions in positron emission tomography (PET) and the limitations of existing MRI techniques. Recently, a novel MRI-based OEF mapping technique, termed QQ, was proposed. It shows high potential for clinical use by utilizing a routine sequence and removing the need for impractical multiple gas inhalations. However, QQ relies on the assumptions of Gaussian noise in susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low signal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic Gaussian noise in mGRE complex signals. We implemented mcQQ using a deep learning framework (mcQQ-NET) and compared it with the existing QQ-NET in simulations, ischemic stroke patients, and healthy subjects, using identical training and testing datasets and schemes. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the subacute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaffected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR, such as SNR ≤ 15 (dB). Therefore, mcQQ-NET improves OEF accuracy by more accurately reflecting realistic Gaussian noise in complex mGRE signals. Its enhanced sensitivity to OEF abnormalities, based on more realistic biophysics modeling, suggests that mcQQ-NET has potential for investigating tissue variability in neurologic disorders.

mcTFI QSM MRI ABC/2 intracranial hemorrhage to noncontrast head CT volume measurement equivalence.

BACKGROUND/OBJECTIVES: Intracranial hemorrhage (ICH) volume assessment is an important part of patient management and is routinely obtained by non-contrast head CT (NCHCT) using the validated ABC/2 measurement method. Because conventional MRI imaging sequences demonstrate variability in ICH appearance, volumetric analyses for MRI bleed volume in a standardized manner using ABC/2 is not possible. The recently introduced multiecho-complex total field inversion quantitative susceptibility mapping (mcTFI QSM) MRI technique, which maps brain tissue susceptibility to both depict brain tissue structures and quantify tissue susceptibility, may provide a viable alternative. In this study we evaluated mcTFI QSM ABC/2 ICH volume assessment relative to NCHCT. METHODS: Patients with ICH who had undergone NCHCT and MRI brain scans within 48 h were recruited for this retrospective study. The ABC/2 method was applied to estimate the bleed volume for both NCHCT and MRI by a CAQ-certified neuroradiologist with 10 years of experience and a trained laboratory assistant. Results were analyzed via Bland-Altman (B-A) and linear regression. RESULTS: 54 patients (27 females) who had undergone NCHCT and MRI within 48 h (<24 h., n = 31, 24-48 h, n = 10) were enrolled. mcTFI QSM ICH volume measurement method showed a positive correlation (99.5%) compared to NCHCT. B-A plot comparing ABC/2 ICH volume on NCHCT and mcTFI MRI done for patients within 24 h demonstrates a bias of -0.09%. CONCLUSIONS: ICH volume calculation using ABC/2 on mcTFI QSM showed a high correlation with NCHCT measurement. These results suggest mcTFI QSM is a promising MRI method for ABC/2 for bleed volume measurement.

Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group.

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.

Maximum spherical mean value filtering for whole-brain QSM.

PURPOSE: To develop a tissue field-filtering algorithm, called maximum spherical mean value (mSMV), for reducing shadow artifacts in QSM of the brain without requiring brain-tissue erosion. THEORY AND METHODS: Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field-magnitude values near the border, where the maximum value of the harmonic background field is located. The effectiveness of mSMV for artifact removal was evaluated by comparing existing QSM algorithms in numerical brain simulation as well as using in vivo human data acquired from 11 healthy volunteers and 93 patients. RESULTS: Numerical simulation showed that mSMV reduces shadow artifacts and improves QSM accuracy. Better shadow reduction, as demonstrated by lower QSM variation in the gray matter and higher QSM image quality score, was also observed in healthy subjects and in patients with hemorrhages, stroke, and multiple sclerosis. CONCLUSION: The mSMV algorithm allows QSM maps that are substantially equivalent to those obtained using SMV-filtered dipole inversion without eroding the volume of interest.

Microstructure-Informed Myelin Mapping (MIMM) from Gradient Echo MRI using Stochastic Matching Pursuit.

Quantification of the myelin content of the white matter is important for studying demyelination in neurodegenerative diseases such as Multiple Sclerosis (MS), particularly for longitudinal monitoring. A novel noninvasive MRI method, called Microstructure-Informed Myelin Mapping (MIMM), is developed to quantify the myelin volume fraction (MVF) by utilizing a multi gradient echo sequence (mGRE) and a detailed biophysical model of tissue microstructure. Myelin is modeled as anisotropic negative susceptibility source based on the Hollow Cylindrical Fiber Model (HCFM), and iron as isotropic positive susceptibility source in the extracellular region. Voxels with a range of biophysical parameters are simulated to create a dictionary of MR echo time magnitude signals and total susceptibility values. MRI signals measured using a mGRE sequence are then matched voxel-by-voxel to the created dictionary to obtain the spatial distributions of myelin and iron. Three different MIMM versions are presented to deal with the fiber orientation dependent susceptibility effects of the myelin sheaths: a basic variation, which assumes fiber orientation is an unknown to fit, two orientation informed variations, which assume the fiber orientation distribution is available either from a separate diffusion tensor imaging (DTI) acquisition or from a DTI atlas based fiber orientation map. While all showed a significant linear correlation with the reference method based on T2-relaxometry (p < 0.0001), DTI orientation informed and atlas orientation informed variations reduced overestimation at white matter tracts compared to the basic variation. Finally, the implications and usefulness of attaining an additional iron susceptibility distribution map are discussed. Highlights: novel stochastic matching pursuit algorithm called microstructure-informed myelin mapping (MIMM) is developed to quantify Myelin Volume Fraction (MVF) using Magnetic Resonance Imaging (MRI) and microstructural modeling.utilizes a detailed biophysical model to capture the susceptibility effects on both magnitude and phase to quantify myelin and iron.matter fiber orientation effects are considered for the improved MVF quantification in the major fiber tracts.acquired myelin and iron maps may be utilized to monitor longitudinal disease progress.

Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group.

This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting.

QSM Throughout the Body.

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM. Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Next, this optimized sampling pattern was implemented in an mGRE sequence using Cartesian fan-beam k-space segmenting and ordering for prospective scans. Furthermore, we propose to insert a recurrent temporal feature fusion module into the reconstruction network to capture signal redundancies along echo time. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions. Generalization experiments show that LARO is robust on the test data with new pathologies and different sequence parameters. Our code is available at https://github.com/Jinwei1209/LARO-QSM.git.

Fluid Mechanics Approach to Perfusion Quantification: Vasculature Computational Fluid Dynamics Simulation, Quantitative Transport Mapping (QTM) Analysis of Dynamics Contrast Enhanced MRI, and Application in Nonalcoholic Fatty Liver Disease Classification.

OBJECTIVE: We quantify liver perfusion using quantitative transport mapping (QTM) method that is free of arterial input function (AIF). QTM method is validated in a vasculature computational fluid dynamics (CFD) simulation and is applied for processing dynamic contrast enhanced (DCE) MRI images in differentiating liver with nonalcoholic fatty liver disease (NAFLD) from healthy controls using pathology reference in a preclinical rabbit model. METHODS: QTM method was validated on a liver perfusion simulation based on fluid dynamics using a rat liver vasculature model and the mass transport equation. In the NAFLD grading task, DCE MRI images of 7 adult rabbits with methionine choline-deficient diet-induced nonalcoholic steatohepatitis (NASH), 8 adult rabbits with simple steatosis (SS) were acquired and processed using QTM method and dual-input two compartment Kety's method respectively. Statistical analysis was performed on six perfusion parameters: velocity magnitude | u | derived from QTM, liver arterial blood flow LBFa, liver venous blood flow LBFv, permeability Ktrans, blood volume Vp and extravascular space volume Ve averaged in liver ROI. RESULTS: In the simulation, QTM method successfully reconstructed blood flow, reduced error by 48% compared to Kety's method. In the preclinical study, only QTM |u| showed significant difference between high grade NAFLD group and low grade NAFLD group. CONCLUSION: QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with Kety's method, QTM method showed higher accuracy and better differentiation in NAFLD classification task. SIGNIFICANCE: We propose to apply QTM method in liver DCE MRI perfusion quantification.

Quantitative susceptibility mapping improves cerebral microbleed detection relative to susceptibility-weighted images.

BACKGROUND AND PURPOSE: Cerebral microbleed (CMB) detection impacts disease diagnosis and management. Susceptibility-weighted imaging (SWI) MRI depictions of CMBs are used with phase images (SWIP) to distinguish blood from calcification, via qualitative intensity evaluation (bright/dark). However, the intensities depicted for a single lesion can vary within and across consecutive SWIP image planes, impairing the classification of findings as a CMB. We hypothesize that quantitative susceptibility mapping (QSM) MRI, which maps tissue susceptibility, demonstrates less in- and through-plane intensity variation, improving the clinician's ability to categorize a finding as a CMB. METHODS: Forty-eight patients with acute intracranial hemorrhage who received multi-echo gradient echo MRI used to generate both SWI/SWIP and morphology-enabled dipole inversion QSM images were enrolled. Five hundred and sixty lesions were visually classified as having homogeneous or heterogeneous in-plane and through-plane intensity by a neuroradiologist and two diagnostic radiology residents using published rating criteria. When available, brain CT scans were analyzed for calcification or acute hemorrhage. Relative risk (RR) ratios and confidence intervals (CIs) were calculated using a generalized linear model with log link and binary error. RESULTS: QSM showed unambiguous lesion signal intensity three times more frequently than SWIP (RR = 0.3235, 95% CI 0.2386-0.4386, p<.0001). The probability of QSM depicting homogeneous lesion intensity was three times greater than SWIP for small (RR = 0.3172, 95% CI 0.2382-0.4225, p<.0001), large (RR = 0.3431, 95% CI 0.2045-0.5758, p<.0001), lobar (RR = 0.3215, 95% CI 0.2151-0.4805, p<.0001), cerebellar (RR = 0.3215, 95% CI 0.2151-0.4805, p<.0001), brainstem (RR = 0.3100, 95% CI 0.1192-0.8061, p = .0163), and basal ganglia (RR = 0.3380, 95% CI 0.1980-0.5769, p<.0001) lesions. CONCLUSIONS: QSM more consistently demonstrates interpretable lesion intensity compared to SWIP as used for distinguishing CMBs from calcification.

Quantitative susceptibility mapping demonstrates different patterns of iron overload in subtypes of early-onset Alzheimer's disease.

OBJECTIVES: We aimed to define brain iron distribution patterns in subtypes of early-onset Alzheimer's disease (EOAD) by the use of quantitative susceptibility mapping (QSM). METHODS: EOAD patients prospectively underwent MRI on a 3-T scanner and concomitant clinical and neuropsychological evaluation, between 2016 and 2019. An age-matched control group was constituted of cognitively healthy participants at risk of developing AD. Volumetry of the hippocampus and cerebral cortex was performed on 3DT1 images. EOAD subtypes were defined according to the hippocampal to cortical volume ratio (HV:CTV). Limbic-predominant atrophy (LPMRI) is referred to HV:CTV ratios below the 25th percentile, hippocampal-sparing (HpSpMRI) above the 75th percentile, and typical-AD between the 25th and 75th percentile. Brain iron was estimated using QSM. QSM analyses were made voxel-wise and in 7 regions of interest within deep gray nuclei and limbic structures. Iron distribution in EOAD subtypes and controls was compared using an ANOVA. RESULTS: Sixty-eight EOAD patients and 43 controls were evaluated. QSM values were significantly higher in deep gray nuclei (p < 0.001) and limbic structures (p = 0.04) of EOAD patients compared to controls. Among EOAD subtypes, HpSpMRI had the highest QSM values in deep gray nuclei (p < 0.001) whereas the highest QSM values in limbic structures were observed in LPMRI (p = 0.005). QSM in deep gray nuclei had an AUC = 0.92 in discriminating HpSpMRI and controls. CONCLUSIONS: In early-onset Alzheimer's disease patients, we observed significant variations of iron distribution reflecting the pattern of brain atrophy. Iron overload in deep gray nuclei could help to identify patients with atypical presentation of Alzheimer's disease. KEY POINTS: • In early-onset AD patients, QSM indicated a significant brain iron overload in comparison with age-matched controls. • Iron load in limbic structures was higher in participants with limbic-predominant subtype. • Iron load in deep nuclei was more important in participants with hippocampal-sparing subtype.

Prediction of Lung Shunt Fraction for Yttrium-90 Treatment of Hepatic Tumors Using Dynamic Contrast Enhanced MRI with Quantitative Perfusion Processing.

There is no noninvasive method to estimate lung shunting fraction (LSF) in patients with liver tumors undergoing Yttrium-90 (Y90) therapy. We propose to predict LSF from noninvasive dynamic contrast enhanced (DCE) MRI using perfusion quantification. Two perfusion quantification methods were used to process DCE MRI in 25 liver tumor patients: Kety's tracer kinetic modeling with a delay-fitted global arterial input function (AIF) and quantitative transport mapping (QTM) based on the inversion of transport equation using spatial deconvolution without AIF. LSF was measured on SPECT following Tc-99m macroaggregated albumin (MAA) administration via hepatic arterial catheter. The patient cohort was partitioned into a low-risk group (LSF ≤ 10%) and a high-risk group (LSF > 10%). Results: In this patient cohort, LSF was positively correlated with QTM velocity |u| (r = 0.61, F = 14.0363, p = 0.0021), and no significant correlation was observed with Kety's parameters, tumor volume, patient age and gender. Between the low LSF and high LSF groups, there was a significant difference for QTM |u| (0.0760 ± 0.0440 vs. 0.1822 ± 0.1225 mm/s, p = 0.0011), and Kety's Ktrans (0.0401 ± 0.0360 vs 0.1198 ± 0.3048, p = 0.0471) and Ve (0.0900 ± 0.0307 vs. 0.1495 ± 0.0485, p = 0.0114). The area under the curve (AUC) for distinguishing between low LSF and high LSF was 0.87 for |u|, 0.80 for Ve and 0.74 for Ktrans. Noninvasive prediction of LSF is feasible from DCE MRI with QTM velocity postprocessing.

Susceptibility source separation from gradient echo data using magnitude decay modeling.

BACKGROUND AND PURPOSE: The objective is to demonstrate feasibility of separating magnetic sources in quantitative susceptibility mapping (QSM) by incorporating magnitude decay rates R 2 ∗ $R_2^{\rm{*}}$ in gradient echo (GRE) MRI. METHODS: Magnetic susceptibility source separation was developed using R 2 ∗ $R_2^{\rm{*}}$ and compared with a prior method using R 2 ' = R 2 ∗ - R 2 ${R^{\prime}_2} = R_2^* - {R_2}$ that required an additional sequence to measure the transverse relaxation rate R2 . Both susceptibility separation methods were compared in multiple sclerosis (MS) patients (n = 17). Susceptibility values of negative sources estimated with R 2 ∗ $R_2^{\rm{*}}$ -based source separation in a set of enhancing MS lesions (n = 44) were correlated against longitudinal myelin water fraction (MWF) changes. RESULTS: In in vivo data, linear regression of the estimated χ + ${\chi}^{+}$ and χ - ${\chi}^{-}$ susceptibility values between the R 2 ∗ $R_2^*$ - and the R 2 ' ${R^{\prime}_2}$ -based separation methods performed across 182 segmented lesions revealed correlation coefficient r = .96 and slope close .99. Correlation analysis in enhancing lesions revealed a significant positive association between the χ - ${\chi}^{-}$ increase at 1-year post-onset relative to 0 year and the MWF increase at 1 year relative to 0 year (ß = -0.144, 95% confidence interval: [-0.199, -0.1], p = .0008) and good agreement between R 2 ' ${R^{\prime}_2}$ and R 2 ∗ $R_2^*$ methods (r = .79, slope = .95). CONCLUSIONS: Separation of magnetic sources based solely on GRE complex data is feasible by combining magnitude decay rate modeling and phase-based QSM and χ - ${\chi}^{-}$ change may serve as a biomarker for myelin recovery or damage in acute MS lesions.

Association between liver fibrosis and incident dementia in the UK Biobank study.

BACKGROUND AND PURPOSE: There is growing recognition that chronic liver conditions influence brain health. The impact of liver fibrosis on dementia risk was unclear. We evaluated the association between liver fibrosis and incident dementia in a cohort study. METHODS: We performed a cohort analysis using data from the UK Biobank study, which prospectively enrolled adults starting in 2007, and continues to follow them. People with a Fibrosis-4 (FIB-4) liver fibrosis score >2.67 were categorized as at high risk of advanced fibrosis. The primary outcome was incident dementia, ascertained using a validated approach. We excluded participants with prevalent dementia at baseline. We used Cox proportional hazards models to evaluate the association between liver fibrosis and dementia while adjusting for potential confounders. RESULTS: Among 455,226 participants included in this analysis, the mean age was 56.5 years and 54% were women. Approximately 2.17% (95% confidence interval [CI] 2.13%-2.22%) had liver fibrosis. The rate of dementia per 1000 person-years was 1.76 (95% CI 1.50-2.07) in participants with liver fibrosis and 0.52 (95% CI 0.50-0.54) in those without. After adjusting for demographics, socioeconomic deprivation, educational attainment, metabolic syndrome, hypertension, diabetes, dyslipidemia, and tobacco and alcohol use, liver fibrosis was associated with an increased risk of dementia (hazard ratio 1.52, 95% CI 1.22-1.90). Results were robust to sensitivity analyses. Effect modification by sex, metabolic syndrome, and apolipoprotein E4 carrier status was not observed. CONCLUSION: Liver fibrosis in middle age was associated with an increased risk of incident dementia, independent of shared risk factors. Liver fibrosis may be an underrecognized risk factor for dementia.

A New Advanced MRI Biomarker for Remyelinated Lesions in Multiple Sclerosis.

OBJECTIVES: Neuropathological studies have shown that multiple sclerosis (MS) lesions are heterogeneous in terms of myelin/axon damage and repair as well as iron content. However, it remains a challenge to identify specific chronic lesion types, especially remyelinated lesions, in vivo in patients with MS. METHODS: We performed 3 studies: (1) a cross-sectional study in a prospective cohort of 115 patients with MS and 76 healthy controls, who underwent 3 T magnetic resonance imaging (MRI) for quantitative susceptibility mapping (QSM), myelin water fraction (MWF), and neurite density index (NDI) maps. White matter (WM) lesions in QSM were classified into 5 QSM lesion types (iso-intense, hypo-intense, hyperintense, lesions with hypo-intense rims, and lesions with paramagnetic rim legions [PRLs]); (2) a longitudinal study of 40 patients with MS to study the evolution of lesions over 2 years; (3) a postmortem histopathology-QSM validation study in 3 brains of patients with MS to assess the accuracy of QSM classification to identify neuropathological lesion types in 63 WM lesions. RESULTS: At baseline, hypo- and isointense lesions showed higher mean MWF and NDI values compared to other QSM lesion types (p < 0.0001). Further, at 2-year follow-up, hypo-/iso-intense lesions showed an increase in MWF. Postmortem analyses revealed that QSM highly accurately identifies (1) fully remyelinated areas as hypo-/iso-intense (sensitivity = 88.89% and specificity = 100%), (2) chronic inactive lesions as hyperintense (sensitivity = 71.43% and specificity = 92.00%), and (3) chronic active/smoldering lesions as PRLs (sensitivity = 92.86% and specificity = 86.36%). INTERPRETATION: These results provide the first evidence that it is possible to distinguish chronic MS lesions in a clinical setting, hereby supporting with new biomarkers to develop and assess remyelinating treatments. ANN NEUROL 2022;92:486-502.

QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps.

BACKGROUND AND PURPOSE: Chronic active multiple sclerosis (MS) lesions are characterized by a paramagnetic rim at the edge of the lesion and are associated with increased disability in patients. Quantitative susceptibility mapping (QSM) is an MRI technique that is sensitive to chronic active lesions, termed rim + lesions on the QSM. We present QSMRim-Net, a data imbalance-aware deep neural network that fuses lesion-level radiomic and convolutional image features for automated identification of rim + lesions on QSM. METHODS: QSM and T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI of the brain were collected at 3 T for 172 MS patients. Rim + lesions were manually annotated by two human experts, followed by consensus from a third expert, for a total of 177 rim + and 3986 rim negative (rim-) lesions. Our automated rim + detection algorithm, QSMRim-Net, consists of a two-branch feature extraction network and a synthetic minority oversampling network to classify rim + lesions. The first network branch is for image feature extraction from the QSM and T2-FLAIR, and the second network branch is a fully connected network for QSM lesion-level radiomic feature extraction. The oversampling network is designed to increase classification performance with imbalanced data. RESULTS: On a lesion-level, in a five-fold cross validation framework, the proposed QSMRim-Net detected rim + lesions with a partial area under the receiver operating characteristic curve (pROC AUC) of 0.760, where clinically relevant false positive rates of less than 0.1 were considered. The method attained an area under the precision recall curve (PR AUC) of 0.704. QSMRim-Net out-performed other state-of-the-art methods applied to the QSM on both pROC AUC and PR AUC. On a subject-level, comparing the predicted rim + lesion count and the human expert annotated count, QSMRim-Net achieved the lowest mean square error of 0.98 and the highest correlation of 0.89 (95% CI: 0.86, 0.92). CONCLUSION: This study develops a novel automated deep neural network for rim + MS lesion identification using T2-FLAIR and QSM images.

Subsecond accurate myelin water fraction reconstruction from FAST-T2 data with 3D UNET.

PURPOSE: To develop a 3D UNET convolutional neural network for rapid extraction of myelin water fraction (MWF) maps from six-echo fast acquisition with spiral trajectory and T2 -prep data and to evaluate its accuracy in comparison with multilayer perceptron (MLP) network. METHODS: The MWF maps were extracted from 138 patients with multiple sclerosis using an iterative three-pool nonlinear least-squares algorithm (NLLS) without and with spatial regularization (srNLLS), which were used as ground-truth labels to train, validate, and test UNET and MLP networks as a means to accelerate data fitting. Network testing was performed in 63 patients with multiple sclerosis and a numerically simulated brain phantom at SNR of 200, 100 and 50. RESULTS: Simulations showed that UNET reduced the MWF mean absolute error by 30.1% to 56.4% and 16.8% to 53.6% over the whole brain and by 41.2% to 54.4% and 21.4% to 49.4% over the lesions for predicting srNLLS and NLLS MWF, respectively, compared to MLP, with better performance at lower SNRs. UNET also outperformed MLP for predicting srNLLS MWF in the in vivo multiple-sclerosis brain data, reducing mean absolute error over the whole brain by 61.9% and over the lesions by 67.5%. However, MLP yielded 41.1% and 51.7% lower mean absolute error for predicting in vivo NLLS MWF over the whole brain and the lesions, respectively, compared with UNET. The whole-brain MWF processing time using a GPU was 0.64 seconds for UNET and 0.74 seconds for MLP. CONCLUSION: Subsecond whole-brain MWF extraction from fast acquisition with spiral trajectory and T2 -prep data using UNET is feasible and provides better accuracy than MLP for predicting MWF output of srNLLS algorithm.

The appearance of magnetic susceptibility objects in SWI phase depends on object size: Comparison with QSM and CT.

PURPOSE: Tissue magnetic susceptibility sign can potentially be detected on susceptibility weighted imaging (SWI) phase (SW-P). This study aims to investigate its performance for depicting brain susceptibility structures. METHODS: A simulation was performed to depict magnetic susceptibility structures of various geometries on SW-P and quantitative susceptibility mapping (QSM). Brain MRI was performed on 25 subjects using SWI on a 3 T MRI system. QSM was generated from the same data. SW-P and QSM were analyzed according to radiological assessment for depicting globus pallidus nuclei, optic radiation white matter tracts, and lateral ventricular choroid plexus calcifications. In 11 of these subjects, CT was available and correlated with SW-P and QSM to assess their performance in quantifying calcifications in the choroid plexus. RESULTS: In simulation, the appearance of a sphere on SW-P ranged from centric nodule to mixed positive and negative values as the diameter increased. Large cylinders also appeared as mixed positive and negative values. In comparison, QSM correctly depicted the susceptibility distribution of all magnetic structures. On human brain images, SW-P depicted the globus pallidus and optic radiation with mixed positive and negative values, consistent with simulation, and small choroid plexus calcifications as either mixed positive and negative values or as centric nodules; QSM depicted all structures as solid structures with the expected signs. For measuring calcification in the choroid plexus, QSM vs CT linear regression had a higher coefficient of determination compared to SW-P vs CT and SW-P vs QSM. CONCLUSION: Appearance of susceptibility sources on SW-P changes with object size. This problem can be overcome using QSM.