Dynamic Imaging of Blood Coagulation Within the Hematoma of Patients With Acute Hemorrhagic Stroke.

BACKGROUND: The dynamics of blood clot (combination of Hb [hemoglobin], fibrin, and a higher concentration of aggregated red blood cells) formation within the hematoma of an intracerebral hemorrhage is not well understood. A quantitative neuroimaging method of localized coagulated blood volume/distribution within the hematoma might improve clinical decision-making. METHODS: The deoxyhemoglobin of aggregated red blood cells within extravasated blood exhibits a higher magnetic susceptibility due to unpaired heme iron electrons. We propose that coagulated blood, with higher aggregated red blood cell content, will exhibit (1) a higher positive susceptibility than noncoagulated blood and (2) increase in fibrin polymerization-restricted localized diffusion, which can be measured noninvasively using quantitative susceptibility mapping and diffusion tensor imaging. In this serial magnetic resonance imaging study, we enrolled 24 patients with acute intracerebral hemorrhage between October 2021 to May 2022 at a stroke center. Patients were 30 to 70 years of age and had a hematoma volume >15 cm3 and National Institutes of Health Stroke Scale score >1. The patients underwent imaging 3×: within 12 to 24 (T1), 36 to 48 (T2), and 60 to 72 (T3) hours of last seen well on a 3T magnetic resonance imaging system. Three-dimensional anatomic, multigradient echo and 2-dimensional diffusion tensor images were obtained. Hematoma and edema volumes were calculated, and the distribution of coagulation was measured by dynamic changes in the susceptibilities and fractional anisotropy within the hematoma. RESULTS: Using a coagulated blood phantom, we demonstrated a linear relationship between the percentage coagulation and susceptibility (R2=0.91) with a positive red blood cell stain of the clot. The quantitative susceptibility maps showed a significant increase in hematoma susceptibility (T1, 0.29±0.04 parts per millions; T2, 0.36±0.04 parts per millions; T3, 0.45±0.04 parts per millions; P<0.0001). A concomitant increase in fractional anisotropy was also observed with time (T1, 0.40±0.02; T2, 0.45±0.02; T3, 0.47±0.02; P<0.05). CONCLUSIONS: This quantitative neuroimaging study of coagulation within the hematoma has the potential to improve patient management, such as safe resumption of anticoagulants, the need for reversal agents, the administration of alteplase to resolve the clot, and the need for surgery.

Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis.

OBJECTIVE: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients. METHODS: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing-remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume. RESULTS: The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size. CONCLUSION: Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets.

A meta-analysis of tract-based spatial statistics studies examining white matter integrity in cocaine use disorder.

Tract-based spatial statistics (TBSS) of diffusion tensor imaging (DTI) studies have consistently shown diminished white matter (WM) integrity for individuals with cocaine use disorder (CUD). The present study used seed-based d mapping (SDM) to determine the extent to which a systematic difference in the WM integrity of cocaine users may exist (as compared with that of healthy controls). Articles from 2006 (when TBSS was first developed) to present were reviewed, with eight selected for inclusion. Meta-analysis found lower fractional anisotropy (FA) in the genu of the corpus callosum for cocaine users, with a small-to-moderate peak effect size (Hedge's g = -0.331). Sensitivity analyses mostly supported the robustness of the obtained difference. Differences detected at exploratory thresholds for significance suggested insult to WM integrity extending beyond the corpus callosum. The present results compliment a previous region-of-interest (ROI)-based meta-analysis of DTI studies in individuals with CUD. These findings have significant implications for the potential role of neuroprotective agents in the treatment of CUD and merit additional iteration as more studies accrue in the literature.

Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI.

Background Enhancing lesions on MRI scans obtained after contrast material administration are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable to develop methods that can predict enhancing lesions without the use of contrast material. Purpose To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods This study involved prospective analysis of existing MRI data. A convolutional neural network was used for classification of enhancing lesions on unenhanced MRI scans. This classification was performed for each slice, and the slice scores were combined by using a fully connected network to produce participant-wise predictions. The network input consisted of 1970 multiparametric MRI scans from 1008 patients recruited from 2005 to 2009. Enhanced lesions on postcontrast T1-weighted images served as the ground truth. The network performance was assessed by using fivefold cross-validation. Statistical analysis of the network performance included calculation of lesion detection rates and areas under the receiver operating characteristic curve (AUCs). Results MRI scans from 1008 participants (mean age, 37.7 years ± 9.7; 730 women) were analyzed. At least one enhancing lesion was observed in 519 participants. The sensitivity and specificity averaged across the five test sets were 78% ± 4.3 and 73% ± 2.7, respectively, for slice-wise prediction. The corresponding participant-wise values were 72% ± 9.0 and 70% ± 6.3. The diagnostic performances (AUCs) were 0.82 ± 0.02 and 0.75 ± 0.03 for slice-wise and participant-wise enhancement prediction, respectively. Conclusion Deep learning used with conventional MRI identified enhanced lesions in multiple sclerosis from images from unenhanced multiparametric MRI with moderate to high accuracy. © RSNA, 2019.

Deep-Learning-Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size.

BACKGROUND: The dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known. PURPOSE: To determine the required training size for a desired accuracy in brain MRI segmentation in multiple sclerosis (MS) using DL. STUDY TYPE: Retrospective analysis of MRI data acquired as part of a multicenter clinical trial. STUDY POPULATION: In all, 1008 patients with clinically definite MS. FIELD STRENGTH/SEQUENCE: MRIs were acquired at 1.5T and 3T scanners manufactured by GE, Philips, and Siemens with dual turbo spin echo, FLAIR, and T1 -weighted turbo spin echo sequences. ASSESSMENT: Segmentation results using an automated analysis pipeline and validated by two neuroimaging experts served as the ground truth. A DL model, based on a fully convolutional neural network, was trained separately using 16 different training sizes. The segmentation accuracy as a function of the training size was determined. These data were fitted to the learning curve for estimating the required training size for desired accuracy. STATISTICAL TESTS: The performance of the network was evaluated by calculating the Dice similarity coefficient (DSC), and lesion true-positive and false-positive rates. RESULTS: The DSC for lesions showed much stronger dependency on the sample size than gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). When the training size was increased from 10 to 800 the DSC values varied from 0.00 to 0.86 ± 0.016 for T2 lesions, 0.87 ± 009 to 0.94 ± 0.004 for GM, 0.86 ± 0.08 to 0.94 ± 0.005 for WM, and 0.91 ± 0.009 to 0.96 ± 0.003 for CSF. DATA CONCLUSION: Excellent segmentation was achieved with a training size as small as 10 image volumes for GM, WM, and CSF. In contrast, a training size of at least 50 image volumes was necessary for adequate lesion segmentation. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1487-1496.

Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.

OBJECTIVE: To investigate the performance of deep learning (DL) based on fully convolutional neural network (FCNN) in segmenting brain tissues in a large cohort of multiple sclerosis (MS) patients. METHODS: We developed a FCNN model to segment brain tissues, including T2-hyperintense MS lesions. The training, validation, and testing of FCNN were based on ~1000 magnetic resonance imaging (MRI) datasets acquired on relapsing-remitting MS patients, as a part of a phase 3 randomized clinical trial. Multimodal MRI data (dual-echo, FLAIR, and T1-weighted images) served as input to the network. Expert validated segmentation was used as the target for training the FCNN. We cross-validated our results using the leave-one-center-out approach. RESULTS: We observed a high average (95% confidence limits) Dice similarity coefficient for all the segmented tissues: 0.95 (0.92-0.98) for white matter, 0.96 (0.93-0.98) for gray matter, 0.99 (0.98-0.99) for cerebrospinal fluid, and 0.82 (0.63-1.0) for T2 lesions. High correlations between the DL segmented tissue volumes and ground truth were observed (R2 > 0.92 for all tissues). The cross validation showed consistent results across the centers for all tissues. CONCLUSION: The results from this large-scale study suggest that deep FCNN can automatically segment MS brain tissues, including lesions, with high accuracy.

Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks.

BACKGROUND: Deep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI. PURPOSE: To automatically evaluate the quality of multicenter structural brain MRI images using an ensemble DL model based on deep convolutional neural networks (DCNNs). STUDY TYPE: Retrospective. POPULATION: The study included 1064 brain images of autism patients and healthy controls from the Autism Brain Imaging Data Exchange (ABIDE) database. MRI data from 110 multiple sclerosis patients from the CombiRx study were included for independent testing. SEQUENCE: T1 -weighted MR brain images acquired at 3T. ASSESSMENT: The ABIDE data were separated into training (60%), validation (20%), and testing (20%) sets. The ensemble DL model combined the results from three cascaded networks trained separately on the three MRI image planes (axial, coronal, and sagittal). Each cascaded network consists of a DCNN followed by a fully connected network. The quality of image slices from each plane was evaluated by the DCNN and the resultant image scores were combined into a volumewise quality rating using the fully connected network. The DL predicted ratings were compared with manual quality evaluation by two experts. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, and positive (PPV) and negative (NPV) predictive values. RESULTS: The AUC, sensitivity, specificity, accuracy, PPV, and NPV for image quality evaluation of the ABIDE test set using the ensemble model were 0.90, 0.77, 0.85, 0.84, 0.42, and 0.96, respectively. On the CombiRx set the same model achieved performance of 0.71, 0.41, 0.84, 0.73, 0.48, and 0.80. DATA CONCLUSION: This study demonstrated the high accuracy of DL in evaluating image quality of structural brain MRI in multicenter studies. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1260-1267.

Patient-specific 3D FLAIR for enhanced visualization of brain white matter lesions in multiple sclerosis.

PURPOSE: To improve the conspicuity of white matter lesions (WMLs) in multiple sclerosis (MS) using patient-specific optimization of single-slab 3D fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI). MATERIALS AND METHODS: Sixteen MS patients were enrolled in a prospective 3.0T MRI study. FLAIR inversion time and echo time were automatically optimized for each patient during the same scan session based on measurements of the relative proton density and relaxation times of the brain tissues. The optimization criterion was to maximize the contrast between gray matter (GM) and white matter (WM), while suppressing cerebrospinal fluid. This criterion also helps increase the contrast between WMLs and WM. The performance of the patient-specific 3D FLAIR protocol relative to the fixed-parameter protocol was assessed both qualitatively and quantitatively. RESULTS: Patient-specific optimization achieved a statistically significant 41% increase in the GM-WM contrast ratio (P < 0.05) and 32% increase in the WML-WM contrast ratio (P < 0.01) compared with fixed-parameter FLAIR. The increase in WML-WM contrast ratio correlated strongly with echo time (P < 10-11 ). Two experienced neuroradiologists indicated substantially higher lesion conspicuity on the patient-specific FLAIR images over conventional FLAIR in 3-4 cases (intrarater correlation coefficient ICC = 0.72). In no case was the image quality of patient-specific FLAIR considered inferior to conventional FLAIR by any of the raters (ICC = 0.32). CONCLUSION: Changes in proton density and relaxation times render fixed-parameter FLAIR suboptimal in terms of lesion contrast. Patient-specific optimization of 3D FLAIR increases lesion conspicuity without scan time penalty, and has potential to enhance the detection of subtle and small lesions in MS. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:557-564.

Novel fMRI working memory paradigm accurately detects cognitive impairment in multiple sclerosis.

BACKGROUND: Cognitive impairment (CI) cannot be diagnosed by magnetic resonance imaging (MRI). Functional magnetic resonance imaging (fMRI) paradigms, such as the immediate/delayed memory task (I/DMT), detect varying degrees of working memory (WM). Preliminary findings using I/DMT showed differences in blood oxygenation level dependent (BOLD) activation between impaired (MSCI, n = 12) and non-impaired (MSNI, n = 9) multiple sclerosis (MS) patients. OBJECTIVES: The aim of the study was to confirm CI detection based on I/DMT BOLD activation in a larger cohort of MS patients. The role of T2 lesion volume (LV) and Expanded Disability Status Scale (EDSS) in magnitude of BOLD signal was also sought. METHODS: A total of 50 patients (EDSS mean ( m) = 3.2, disease duration (DD) m = 12 years, and age m = 40 years) underwent the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) and I/DMT. Working memory activation (WMa) represents BOLD signal during DMT minus signal during IMT. CI was based on MACFIMS. RESULTS: A total of 10 MSNI, 30 MSCI, and 4 borderline patients were included in the analyses. Analysis of variance (ANOVA) showed MSNI had significantly greater WMa than MSCI, in the left prefrontal cortex and left supplementary motor area ( p = 0.032). Regression analysis showed significant inverse correlations between WMa and T2 LV/EDSS in similar areas ( p = 0.005, 0.004, respectively). CONCLUSION: I/DMT-based BOLD activation detects CI in MS. Larger studies are needed to confirm these findings.

Automated patient-specific optimization of three-dimensional double-inversion recovery magnetic resonance imaging.

PURPOSE: To automatically optimize three-dimensional double-inversion recovery (3D-DIR) MRI of the brain on a patient-by-patient basis. METHODS: DIR is a powerful MRI technique that allows simultaneous suppression of white matter (WM) and cerebrospinal fluid (CSF) in brain imaging. Unfortunately, the tissue suppression is not always consistent across patients. We propose patient-specific optimization of WM suppression for improved gray matter (GM)-WM contrast. Relaxation times were measured in the same scan session, and through real time processing were used for calculating DIR inversion times for maximum tissue contrast. Signal evolution during the variable-flip-angle turbo-spin-echo readout was calculated using the extended phase graph algorithm. Patient-specific optimization was examined in five healthy volunteers and two multiple sclerosis patients. Two volunteers were scanned twice for reproducibility. The contrast ratios, GM signal-to-noise ratio (SNR), and image histogram were used to assess the performance of this patient-specific approach. RESULTS: Automated optimization of 3D-DIR was successfully completed in all experiments with processing time of ∼1 min. GM-WM contrast ratio tripled with the optimized DIR sequence, with only a 19% decrease in GM-CSF contrast and 30% SNR penalty. CONCLUSION: Patient-specific optimization is feasible and significantly improves GM-WM contrast on 3D-DIR with a moderate decrease in the GM SNR.

Optimal combination of FLAIR and T2-weighted MRI for improved lesion contrast in multiple sclerosis.

PURPOSE: Postacquisition combination of three-dimensional T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) images can improve the visualization of brain lesions in multiple sclerosis (MS). However, an optimal way to combine these images has not been described so far. The main objective of this study is to investigate an optimal combination of T2w and FLAIR to improve the conspicuity of MS lesions. MATERIALS AND METHODS: We determined the parameters for a generalized multiplicative image combination which maximize the contrast-to-noise ratio (CNR) between lesions and normal-appearing brain tissue through simulations and verified experimentally. MRI data from 11 MS patients acquired at 3 Tesla were retrospectively analyzed using the proposed approach and compared with conventional FLAIR, and to images obtained by direct multiplication of T2w and FLAIR (FLAIR2 ). Image quality was assessed by region-of-interest analysis. In addition, to evaluate the degree of cerebrospinal fluid (CSF) suppression, CSF-to-gray matter (CSF/GM) ratio was calculated. Reduction in global image contrast was assessed by computing the reduction in the contrast of mid-level intensity values. RESULTS: An optimal combination was found to be the third order expression: FLAIR3 = FLAIR1.55 × T2w1.45 . Compared with FLAIR, the lesion CNR was significantly increased by 1.9× (P < 0.005) and 2.5× (P < 0.001) using FLAIR2 and FLAIR3 , respectively. CSF/GM ratio was increased by 1.7× in FLAIR2 (P < 0.001) compared with FLAIR, while it was reduced to 0.7× on FLAIR3 (P < 0.05). The mid-intensity contrast was preserved on FLAIR2 (P = 0.2), and decreased by 29% on FLAIR3 (P < 0.001). CONCLUSION: These results show that the optimized combination of FLAIR and T2w can improve MS lesion conspicuity. J. Magn. Reson. Imaging 2016;44:1293-1300.

Effect of in-painting on cortical thickness measurements in multiple sclerosis: A large cohort study.

A comprehensive analysis of the effect of lesion in-painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing-remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in-painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in-painting. The effect of in-painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in-painting than without. The effect of in-painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in-painting is ∼2%. Based on these results, it appears that in-painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749-3760, 2015. © 2015 Wiley Periodicals, Inc.

Stochastic dynamic causal modeling of working memory connections in cocaine dependence.

Although reduced working memory brain activation has been reported in several brain regions of cocaine-dependent subjects compared with controls, very little is known about whether there is altered connectivity of working memory pathways in cocaine dependence. This study addresses this issue by using functional magnetic resonance imaging-based stochastic dynamic causal modeling (DCM) analysis to study the effective connectivity of 19 cocaine-dependent subjects and 14 healthy controls while performing a working memory task. Stochastic DCM is an advanced method that has recently been implemented in SPM8 that can obtain improved estimates, relative to deterministic DCM, of hidden neuronal causes before convolution with the hemodynamic response. Thus, stochastic DCM may be less influenced by the confounding effects of variations in blood oxygen level-dependent response caused by disease or drugs. Based on the significant regional activation common to both groups and consistent with previous working memory activation studies, seven regions of interest were chosen as nodes for DCM analyses. Bayesian family level inference, Bayesian model selection analyses, and Bayesian model averaging (BMA) were conducted. BMA showed that the cocaine-dependent subjects had large differences compared with the control subjects in the strengths of prefrontal-striatal modulatory (B matrix) DCM parameters. These findings are consistent with altered cortical-striatal networks that may be related to reduced dopamine function in cocaine dependence. As far as we are aware, this is the first between-group DCM study using stochastic methodology.

Randomized study combining interferon and glatiramer acetate in multiple sclerosis.

OBJECTIVE: A double-blind, randomized, controlled study was undertaken to determine whether combined use of interferon ß-1a (IFN) 30 µg intramuscularly weekly and glatiramer acetate (GA) 20 mg daily is more efficacious than either agent alone in relapsing-remitting multiple sclerosis. METHODS: A total of 1,008 participants were randomized and followed until the last participant enrolled completed 3 years. The primary endpoint was reduction in annualized relapse rate utilizing a strict definition of relapse. Secondary outcomes included time to confirmed disability, Multiple Sclerosis Functional Composite (MSFC) score, and magnetic resonance imaging (MRI) metrics. RESULTS: Combination IFN+GA was not superior to the better of the single agents (GA) in risk of relapse. Both the combination therapy and GA were significantly better than IFN in reducing the risk of relapse. The combination was not better than either agent alone in lessening confirmed Expanded Disability Status Scale progression or change in MSFC over 36 months. The combination was superior to either agent alone in reducing new lesion activity and accumulation of total lesion volumes. In a post hoc analysis, combination therapy resulted in a higher proportion of participants attaining disease activity-free status (DAFS) compared to either single arm, driven by the MRI results. INTERPRETATION: Combining the 2 most commonly prescribed therapies for multiple sclerosis did not produce a significant clinical benefit over 3 years. An effect was seen on some MRI metrics. In a test of comparative efficacy, GA was superior to IFN in reducing the risk of exacerbation. The extension phase for CombiRx will address whether the observed differences in MRI and DAFS findings predict later clinical differences.

Chronic cerebrospinal venous insufficiency: case-control neurosonography results.

OBJECTIVE: Chronic cerebrospinal venous insufficiency (CCSVI) has been implicated in the pathophysiology of multiple sclerosis (MS). We sought to determine whether neurosonography (NS) provides reliable information on cerebral venous outflow patterns specific to MS. METHODS: This was a single-center, prospective case-control study of volunteer MS and non-MS participants. A neurosonologist, blind to the subjects' diagnosis, used high-resolution B-mode imaging with color and spectral Doppler to systematically investigate, capture, and record extracranial and intracranial venous drainage. These neuroimaging results were evaluated and scored by an expert blinded to subjects' information and with no interactions with the participants. RESULTS: Altogether, 276 subjects were studied: 206 with MS and 70 non-MS. MS patients were older than non-MS subjects (48.3±9.9 vs 44.3±11.8 years, p<0.007), with durations from first symptoms and diagnosis of 13.7±10 and 9.9±7.8 years, and Expanded Disability Status Scale of 2.6±2.0. Overall, 82 subjects (29.7%) fulfilled 1 of 5 NS criteria proposed for CCSVI; 13 (4.7%) fulfilled 2 criteria required for diagnosis, and none fulfilled >2 criteria. The distribution of subjects with 0, 1, or 2 criteria did not differ significantly across all diagnostic groupings, between MS and non-MS subjects, or within MS subgroups. CCSVI was present in 7.14% of non-MS and 3.88% of MS patients (p=0.266). No significant differences emerged between MS and non-MS subjects for extracranial or intracranial venous flow rates. INTERPRETATION: NS findings described as CCSVI are much less prevalent than initially reported, and do not distinguish MS from other subjects. Our findings do not support the hypothesis that CCSVI is causally associated with MS.

High resolution diffusion tensor imaging of human nerves in forearm.

PURPOSE: To implement high resolution diffusion tensor imaging (DTI) for visualization and quantification of peripheral nerves in human forearm. MATERIALS AND METHODS: This HIPAA-compliant study was approved by our Institutional Review Board and written informed consent was obtained from all the study participants. Images were acquired with T1 -and T2 -weighted turbo spin echo with/without fat saturation, short tau inversion recovery (STIR). In addition, high spatial resolution (1.0 × 1.0 × 3.0 mm(3) ) DTI sequence was optimized for clearly visualizing ulnar, superficial radial and median nerves in the forearm. Maps of the DTI derived indices, fractional anisotropy (FA), mean diffusivity (MD), longitudinal diffusivity (λ// ) and radial diffusivity (λ⊥ ) were generated. RESULTS: For the first time, the three peripheral nerves, ulnar, superficial radial, and median, were visualized unequivocally on high resolution DTI-derived maps. DTI delineated the forearm nerves more clearly than other sequences. Significant differences in the DTI-derived measures, FA, MD, λ// and λ⊥ , were observed among the three nerves. A strong correlation between the nerve size derived from FA map and T2 -weighted images was observed. CONCLUSION: High spatial resolution DTI is superior in identifying and quantifying the median, ulnar, and superficial radial nerves in human forearm. Consistent visualization of small nerves and nerve branches is possible with high spatial resolution DTI. These normative data could potentially help in identifying pathology in diseased nerves. J. Magn. Reson. Imaging 2014;39:1374-1383. © 2013 Wiley Periodicals, Inc.

Contrast enhanced MR venography with gadofosveset trisodium: evaluation of the intracranial and extracranial venous system.

PURPOSE: To demonstrate the efficacy of contrast enhanced magnetic resonance venography (CEMRV) using gadofosveset trisodium in the comprehensive evaluation of the intracranial and extracranial venous system. MATERIALS AND METHODS: Temporal signal decay, in-plane saturation and flow artifacts were assessed in an institutional review board approved, HIPAA compliant CEMRV study of 99 subjects. In a 39 subject subset, percent diameter narrowing of the internal jugular (IJ), brachiocephalic and azygous veins were coded according to the following ordinal grades for both catheter venography (CV) and CEMRV: grade 0 ≤ 50%, grade 1 >50% and ≤ 75%, grade 2 >75% and <100% and grade 3 = 100% and compared with pressure gradient measurements obtained during CV. RESULTS: There was no significant signal decay, in-plane saturation or flow artifacts identified on CEMRV or hemodynamically significant pressure gradients identified on CV. All brachiocephalic and azygous veins had matched grade 0 narrowing on both modalities. Discrepancy between modalities occurred in the IJ veins at the level of thyroid gland where 15% of IJ veins had CEMRV grade ≥ 1 narrowing compared with 4% for CV or below the thyroid gland where 5% of IJ veins had CEMRV grade ≥ 1 narrowing compared with 20% for CV. There was fair agreement (κ = 0.24) between modalities for grade of narrowing in the combined data set of all coded veins. CONCLUSION: CEMRV using gadofosveset trisodium is accurate in the evaluation of the venous system.

Hypoperfusion and T1-hypointense lesions in white matter in multiple sclerosis.

BACKGROUND: Longitudinal magnetic resonance imaging (MRI) studies show that a fraction of the multiple sclerosis (MS) T2-lesions contain T1-hypointense components that may persist to represent severe, irreversible tissue damage. It is not known why certain lesions convert to persistent T1-hypointense lesions. OBJECTIVE: We hypothesized that the T1-hypointense lesions disproportionately distribute in the more hypoperfused areas of the brain. Here we investigated the association between hypoperfusion and T1-hypointense lesion distributions. METHODS: MRI and cerebral blood flow (CBF) data were acquired on 45 multiple sclerosis (MS) patients and 20 healthy controls. CBF maps were generated using pseudo-continuous arterial spin labeling technique. The lesion probability distribution maps were superimposed on the CBF maps. RESULTS: Two distinct CBF clusters were observed in the white matter (WM) both in healthy controls and MS patients. An overall reduction in CBF was observed in MS patients compared to healthy controls. The majority of the T1-hypointense lesions were concentrated almost exclusively in the WM regions with lower CBF. The T2-hyperintense lesions were more generally distributed in both higher and lower perfused WM. CONCLUSION: This study suggests an association between hypoperfusion and T1-hypointense lesions.

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.

BACKGROUND: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. OBJECTIVE: To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. MATERIALS AND METHODS: Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. RESULTS: LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. CONCLUSIONS: NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.