Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge.

PURPOSE: T1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. METHODS: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. RESULTS: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. CONCLUSION: The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities.

Importance: Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-ß-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software. Objective: To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA. Design, Setting, and Participants: This diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023. Exposures: Use of icobrain aria, an AI-based assistive software for ARIA detection and quantification. Main Outcomes and Measures: Coprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC). Results: Among the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types. Conclusions and Relevance: This diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-ß-directed monoclonal antibody therapies.

Identifying the white matter pathways involved in multiple sclerosis-related tremor using diffusion tensor imaging.

Background: Tremor affects up to 45% of patients with Multiple Sclerosis (PwMS). Current understanding is based on insights from other neurological disorders, thus, not fully addressing the distinctive aspects of MS pathology. Objective: To characterize the brain white matter (WM) correlates of MS-related tremor using diffusion tensor imaging (DTI). Methods: In a prospective case-control study, PwMS with tremor were assessed for tremor severity and underwent MRI scans including DTI. PwMS without tremor served as matched controls. After tract selection and segmentation, the resulting diffusivity measures were used to calculate group differences and correlations with tremor severity. Results: This study included 72 PwMS. The tremor group (n = 36) exhibited significant changes in several pathways, notably in the right inferior longitudinal fasciculus (Cohen's d = 1.53, q < 0.001) and left corticospinal tract (d = 1.32, q < 0.001), compared to controls (n = 36). Furthermore, specific tracts showed a significant correlation with tremor severity, notably in the left medial lemniscus (Spearman's coefficient [rsp] = -0.56, p < 0.001), and forceps minor of corpus callosum (rsp = -0.45, p < 0.01). Conclusion: MS-related tremor is associated with widespread diffusivity changes in WM pathways and its severity correlates with commissural and sensory projection pathways, which suggests a role for proprioception or involvement of the dentato-rubro-olivary circuit.

Functional assessment of the dural lymphatic vessels using dynamic contrast MRI in multiple sclerosis.

BACKGROUND AND PURPOSE: The discovery of glymphatic function in the human brain has generated interest in waste clearance mechanisms in neurological disorders such as multiple sclerosis (MS). However, noninvasive in vivo functional assessment is currently lacking. This work studies the feasibility of a novel intravenous dynamic contrast MRI method to assess the dural lymphatics, a purported pathway contributing to glymphatic clearance. METHODS: This prospective study included 20 patients with MS (17 women; age = 46.4 [27, 65] years; disease duration = 13.6 [2.1, 38.0] years, expanded disability status score (EDSS) = 2.0 [0, 6.5]). Patients were scanned on a 3.0T MRI system using intravenous contrast-enhanced fluid-attenuated inversion recovery MRI. Signal in the dural lymphatic vessel along the superior sagittal sinus was measured to calculate peak enhancement, time to maximum enhancement, wash-in and washout slopes, and the area under the time-intensity curve (AUC). Correlation analysis was performed to examine the relationship between the lymphatic dynamic parameters and the demographic and clinical characteristics, including the lesion load and the brain parenchymal fraction (BPF). RESULTS: Contrast enhancement was detected in the dural lymphatics in most patients 2-3 min after contrast administration. BPF had a significant correlation with AUC (p < .03), peak enhancement (p < .01), and wash-in slope (p = .01). Lymphatic dynamic parameters did not correlate with age, BMI, disease duration, EDSS, or lesion load. Moderate trends were observed for correlation between patient age and AUC (p = .062), BMI and peak enhancement (p = .059), and BMI and AUC (p = .093). CONCLUSION: Intravenous dynamic contrast MRI of the dural lymphatics is feasible and may be useful in characterizing its hydrodynamics in neurological diseases.

Cerebrovascular Effects of Lower Body Negative Pressure at 3T MRI: Implications for Long-Duration Space Travel.

BACKGROUND: Optic disc edema develops in most astronauts during long-duration spaceflight. It is hypothesized to result from weightlessness-induced venous congestion of the head and neck and is an unresolved health risk of space travel. PURPOSE: Determine if short-term application of lower body negative pressure (LBNP) could reduce internal jugular vein (IJV) expansion associated with the supine posture without negatively impacting cerebral perfusion or causing IJV flow stasis. STUDY TYPE: Prospective. SUBJECTS: Nine healthy volunteers (six women). FIELD STRENGTH/SEQUENCE: 3T/cine two-dimensional phase-contrast gradient echo; pseudo-continuous arterial spin labeling single-shot gradient echo echo-planar. ASSESSMENT: The study was performed with two sequential conditions in randomized order: supine posture and supine posture with 25 mmHg LBNP (LBNP25 ). LBNP was achieved by enclosing the lower extremities in a semi-airtight acrylic chamber connected to a vacuum. Heart rate, bulk cerebrovasculature flow, IJV cross-sectional area, fractional IJV outflow relative to arterial inflow, and cerebral perfusion were assessed in each condition. STATISTICAL TESTS: Paired t-tests were used to compare measurement means across conditions. Significance was defined as P < 0.05. RESULTS: LBNP25 significantly increased heart rate from 64 ± 9 to 71 ± 8 beats per minute and significantly decreased IJV cross-sectional area, IJV outflow fraction, cerebral arterial flow rate, and cerebral arterial stroke volume from 1.28 ± 0.64 to 0.56 ± 0.31 cm2 , 0.75 ± 0.20 to 0.66 ± 0.28, 780 ± 154 to 708 ± 137 mL/min and 12.2 ± 2.8 to 9.7 ± 1.7 mL/cycle, respectively. During LBNP25 , there was no significant change in gray or white matter cerebral perfusion (P = 0.26 and P = 0.24 respectively) and IJV absolute mean peak flow velocity remained ≥4 cm/sec in all subjects. DATA CONCLUSION: Short-term application of LBNP25 reduced IJV expansion without decreasing cerebral perfusion or inducing IJV flow stasis. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

Characterizing the time course of cerebrovascular reactivity in multiple sclerosis.

BACKGROUND AND PURPOSE: Changes in cerebral perfusion occur early in relapsing and progressive multiple sclerosis (MS) patients, though whether cerebral blood flow (CBF) can be altered by therapy is unknown. We sought to characterize the time course of change in CBF (cerebral vascular reactivity [CVR]), following intravenous (IV) acetazolamide (ACZ) in whole brain and within various gray and white matter brain regions in MS patients. METHODS: We enrolled five relapsing MS patients on injectable therapies. Participants received a 1000 mg IV bolus of ACZ and CBF was measured using pseudocontinuous arterial spin labeling MRI. To quantify differences in time course between patients, we calculated the numerical integration of CVR over time using the trapezoidal rule to estimate area under the curve (AUC(CVR) ). RESULTS: A change in whole brain CBF of 30%-65% was seen in all participants at 15 minutes after ACZ challenge. CBF increases >20% above baseline were sustained for 90 minutes within whole-brain, normal-appearing white matter and total T2-hyperintense lesioned tissue. AUC(CVR) values for both gray (cortical and deep gray matter) and white (normal-appearing and T2-lesioned) matter regions were similar between patients. CONCLUSION: Our findings show a prolonged time course in vascular reactivity after ACZ stimulus in MS patients with a similar time course for both gray and white matter brain regions, including in previously injured tissue. Our preliminary results suggest that blood flow can be augmented in the established MS lesion suggesting that even previously injured tissue might be responsive to treatment.

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.

Quantitative Assessment of In Vivo Human Anterior Cruciate Ligament Autograft Remodeling: A 3-Dimensional UTE-T2* Imaging Study.

BACKGROUND: The timing of return to play after anterior cruciate ligament (ACL) reconstruction is still controversial due to uncertainty of true ACL graft state at the time of RTP. Recent work utilizing ultra-short echo T2* (UTE-T2*) magnetic resonance imaging (MRI) as a scanner-independent method to objectively and non-invasively assess the status of in vivo ACL graft remodeling has produced promising results. PURPOSE/HYPOTHESIS: The purpose of this study was to prospectively and noninvasively investigate longitudinal changes in T2* within ACL autografts at incremental time points up to 12 months after primary ACL reconstruction in human patients. We hypothesized that (1) T2* would increase from baseline and initially exceed that of the intact contralateral ACL, followed by a gradual decline as the graft undergoes remodeling, and (2) remodeling would occur in a region-dependent manner. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Twelve patients (age range, 14-45 years) who underwent primary ACL reconstruction with semitendinosus tendon or bone-patellar tendon-bone autograft (with or without meniscal repair) were enrolled. Patients with a history of previous injury or surgery to either knee were excluded. Patients returned for UTE MRI at 1, 3, 6, 9, and 12 months after ACL reconstruction. Imaging at 1 month included the contralateral knee. MRI pulse sequences included high-resolution 3-dimensional gradient echo sequence and a 4-echo T2-UTE sequence (slice thickness, 1 mm; repetition time, 20 ms; echo time, 0.3, 3.3, 6.3, and 9.3 ms). All slices containing the intra-articular ACL were segmented from high-resolution sequences to generate volumetric regions of interest (ROIs). ROIs were divided into proximal/distal and core/peripheral sub-ROIs using standardized methods, followed by voxel-to-voxel registration to generate T2* maps at each time point. This process was repeated by a second reviewer for interobserver reliability. Statistical differences in mean T2* values and mean ratios of T2*inj/T2*intact (ie, injured knee to intact knee) among the ROIs and sub-ROIs were assessed using repeated measures and one-way analyses of variance. P < .05 represented statistical significance. RESULTS: Twelve patients enrolled in this prospective study, 2 withdrew, and ultimately 10 patients were included in the analysis (n = 7, semitendinosus tendon; n = 3, bone-patellar tendon-bone). Interobserver reliability for T2* values was good to excellent (intraclass correlation coefficient, 0.84; 95% CI, 0.59-0.94; P < .001). T2* values increased from 5.5 ± 2.1 ms (mean ± SD) at 1 month to 10.0 ± 2.9 ms at 6 months (P = .001), followed by a decline to 8.1 ± 2.0 ms at 12 months (P = .129, vs 1 month; P = .094, vs 6 months). Similarly, mean T2*inj/T2*intact ratios increased from 62.8% ± 22.9% at 1 month to 111.1% ± 23.9% at 6 months (P = .001), followed by a decline to 92.8% ± 29.8% at 12 months (P = .110, vs 1 month; P = .086, vs 6 months). Sub-ROIs exhibited similar increases in T2* until reaching a peak at 6 months, followed by a gradual decline until the 12-month time point. There were no statistically significant differences among the sub-ROIs (P > .05). CONCLUSION: In this preliminary study, T2* values for ACL autografts exhibited a statistically significant increase of 82% between 1 and 6 months, followed by an approximate 19% decline in T2* values between 6 and 12 months. In the future, UTE-T2* MRI may provide unique insights into the condition of remodeling ACL grafts and may improve our ability to noninvasively assess graft maturity before return to play.

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.

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.

Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning.

BACKGROUND: Magnetic resonance images with multiple contrasts or sequences are commonly used for segmenting brain tissues, including lesions, in multiple sclerosis (MS). However, acquisition of images with multiple contrasts increases the scan time and complexity of the analysis, possibly introducing factors that could compromise segmentation quality. OBJECTIVE: To investigate the effect of various combinations of multi-contrast images as input on the segmented volumes of gray (GM) and white matter (WM), cerebrospinal fluid (CSF), and lesions using a deep neural network. METHODS: U-net, a fully convolutional neural network was used to automatically segment GM, WM, CSF, and lesions in 1000 MS patients. The input to the network consisted of 15 combinations of FLAIR, T1-, T2-, and proton density-weighted images. The Dice similarity coefficient (DSC) was evaluated to assess the segmentation performance. For lesions, true positive rate (TPR) and false positive rate (FPR) were also evaluated. In addition, the effect of lesion size on lesion segmentation was investigated. RESULTS: Highest DSC was observed for all the tissue volumes, including lesions, when the input was combination of all four image contrasts. All other input combinations that included FLAIR also provided high DSC for all tissue classes. However, the quality of lesion segmentation showed strong dependence on the input images. The DSC and TPR values for inputs with the four contrast combination and FLAIR alone were very similar, but FLAIR showed a moderately higher FPR for lesion size <100 µl. For lesions smaller than 20 µl all image combinations resulted in poor performance. The segmentation quality improved with lesion size. CONCLUSIONS: Best performance for segmented tissue volumes was obtained with all four image contrasts as the input, and comparable performance was attainable with FLAIR only as the input, albeit with a moderate increase in FPR for small lesions. This implies that acquisition of only FLAIR images provides satisfactory tissue segmentation. Lesion segmentation was poor for very small lesions and improved rapidly with lesion size.

Serial Metabolic Evaluation of Perihematomal Tissues in the Intracerebral Hemorrhage Pig Model.

PURPOSE: Perihematomal edema (PHE) occurs in patients with intracerebral hemorrhage (ICH) and is often used as surrogate of secondary brain injury. PHE resolves over time, but little is known about the functional integrity of the tissues that recover from edema. In a pig ICH model, we aimed to assess metabolic integrity of perihematoma tissues by using non-invasive magnetic resonance spectroscopy (MRS). MATERIALS AND METHODS: Fourteen male Yorkshire pigs with an average age of 8 weeks were intracerebrally injected with autologous blood to produce ICH. Proton MRS data were obtained at 1, 7, and 14 days after ICH using a whole-body 3.0T MRI system. Point-resolved spectroscopy (PRESS)-localized 2D chemical shift imaging (CSI) was acquired. The concentration of N-Acetylaspartate (NAA), Choline (Cho), and Creatine (Cr) were measured within the area of PHE, tissues adjacent to the injury with no or negligible edema (ATNE), and contralesional brain tissue. A linear mixed model was used to analyze the evolution of metabolites in perihematomal tissues, with p-value < 0.05 indicating statistical significance. RESULTS: The perihematoma volume gradually decreased from 2.38 ± 1.23 ml to 0.41 ± 0.780 ml (p < 0.001) over 2 weeks. Significant (p < 0.001) reductions in NAA, Cr, and Cho concentrations were found in the PHE and ATNE regions compared to the contralesional hemisphere at day 1 and 7 after ICH. All three metabolites were significantly (p < 0.001) restored in the PHE tissue on day 14, but remained persistently low in the ATNE area, and unaltered in the contralesional voxel. CONCLUSION: This study highlights the potential of MRS to probe salvageable tissues within the perihematoma in the sub-acute phase of ICH. Altered metabolites within the PHE and ATNE regions in addition to edema and hematoma volumes were explored as possible markers for tissue recovery. Perihematomal tissue with PHE demonstrated a more reversible injury compared to the tissue adjacent to the injury without edema, suggesting a potentially beneficial role of edema.

MRI acoustic noise-modulated computer animations for patient distraction and entertainment with application in pediatric psychiatric patients.

PURPOSE: To reduce patient anxiety caused by the MRI scanner acoustic noise. MATERIAL AND METHODS: We developed a simple and low-cost system for patient distraction using visual computer animations that were synchronized to the MRI scanner's acoustic noise during the MRI exam. The system was implemented on a 3T MRI system and tested in 28 pediatric patients with bipolar disorder. The patients were randomized to receive noise-synchronized animations in the form of abstract animations in addition to music (n = 13, F/M = 6/7, age = 10.9 ±â€¯2.5 years) or, as a control, receive only music (n = 15, F/M = 7/8, age = 11.6 ±â€¯2.3 years). After completion of the scans, all subjects answered a questionnaire about their scan experience and the perceived scan duration. RESULTS: The scan duration with multisensory input (animations and music) was perceived to be ~15% shorter than in the control group (43 min vs. 50 min, P < 0.05). However, the overall scan experience was scored less favorably (3.9 vs. 4.6 in the control group, P < 0.04). CONCLUSIONS: This simple system provided patient distraction and entertainment leading to perceived shorter scan times, but the provided visualization with abstract animations was not favored by this patient cohort.

Serial Cerebral Metabolic Changes in Patients With Ischemic Stroke Treated With Autologous Bone Marrow Derived Mononuclear Cells.

Purpose: Cell-based therapy offers new opportunities for the development of novel treatments to promote tissue repair, functional restoration, and cerebral metabolic balance. N-acetylasperate (NAA), Choline (Cho), and Creatine (Cr) are three major metabolites seen on proton magnetic resonance spectroscopy (MRS) that play a vital role in balancing the biochemical processes and are suggested as markers of recovery. In this preliminary study, we serially monitored changes in these metabolites in ischemic stroke patients who were treated with autologous bone marrow-derived mononuclear cells (MNCs) using non-invasive MRS. Materials and Methods: A sub-group of nine patients (3 male, 6 female) participated in a serial MRS study, as part of a clinical trial on autologous bone marrow cell therapy in acute ischemic stroke. Seven to ten million mononuclear cells were isolated from the patient's bone marrow and administered intravenously within 72 h of onset of injury. MRS data were obtained at 1, 3, and 6 months using a whole-body 3.0T MRI. Single voxel point-resolved spectroscopy (PRESS) was obtained within the lesion and contralesional gray matter. Spectral analysis was done using TARQUIN software and absolute concentration of NAA, Cho, and Cr was determined. National Institute of Health Stroke Scale (NIHSS) was serially recoreded. Two-way analysis of variance was performed and p < 0.05 considered statistically significant. Results: All metabolites showed statistically significant or clear trends toward lower ipsilesional concentrations compared to the contralesional side at all time points. Statistically significant reductions were found in ipsilesional NAA at 1M and 3M, Cho at 6M, and Cr at 1M and 6M (p < 0.03), compared to the contralesional side. Temporally, ipsilesional NAA increased between 3M and 6M (p < 0.01). On the other hand, ipsilesional Cho showed continued decline till 6M (p < 0.01). Ipsilesional Cr was stable over time. Contralesional metabolites were relatively stable over time, with only Cr showing a reduction 3M (p < 0.02). There was a significant (p < 0.03) correlation between ipsilesional NAA and NIHSS at 3M follow-up. Conclusion: Serial changes in metabolites suggest that MRS can be applied to monitor therapeutic changes. Post-treatment increasing trends of NAA concentration and significant correlation with NIHSS support a potential therapeutic effect.

Ongoing Secondary Degeneration of the Limbic System in Patients With Ischemic Stroke: A Longitudinal MRI Study.

Purpose: Ongoing post-stroke structural degeneration and neuronal loss preceding neuropsychological symptoms such as cognitive decline and depression are poorly understood. Various substructures of the limbic system have been linked to cognitive impairment. In this longitudinal study, we investigated the post-stroke macro- and micro-structural integrity of the limbic system using structural and diffusion tensor magnetic resonance imaging. Materials and Methods: Nineteen ischemic stroke patients (11 men, 8 women, average age 53.4 ± 12.3, range 18-75 years), with lesions remote from the limbic system, were serially imaged three times over 1 year. Structural and diffusion-tensor images (DTI) were obtained on a 3.0 T MRI system. The cortical thickness, subcortical volume, mean diffusivity (MD), and fractional anisotropy (FA) were measured in eight different regions of the limbic system. The National Institutes of Health Stroke Scale (NIHSS) was used for clinical assessment. A mixed model for multiple factors was used for statistical analysis, and p-values <0.05 was considered significant. Results: All patients demonstrated improved NIHSS values over time. The ipsilesional subcortical volumes of the thalamus, hippocampus, and amygdala significantly decreased (p < 0.05) and MD significantly increased (p < 0.05). The ipsilesional cortical thickness of the entorhinal and perirhinal cortices was significantly smaller than the contralesional hemisphere at 12 months (p < 0.05). The cortical thickness of the cingulate gyrus at 12 months was significantly decreased at the caudal and isthmus regions as compared to the 1 month assessment (p < 0.05). The cingulum fibers had elevated MD at the ipsilesional caudal-anterior and posterior regions compared to the corresponding contralesional regions. Conclusion: Despite the decreasing NIHSS scores, we found ongoing unilateral neuronal loss/secondary degeneration in the limbic system, irrespective of the lesion location. These results suggest a possible anatomical basis for post stroke psychiatric complications.

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.