Multiple Sclerosis Imaging Research Articles

Pooled analysis of multiple sclerosis findings on multisite 7 Tesla MRI: Protocol and initial observations.

Although 7 T MRI research has contributed much to our understanding of multiple sclerosis (MS) pathology, most prior data has come from small, single-center studies with varying methods. In order to truly know if such findings have widespread applicability, multicenter methods and studies are needed. To address this, members of the North American Imaging in MS (NAIMS) Cooperative worked together to create a multicenter collaborative study of 7 T MRI in MS. In this manuscript, we describe the methods we have developed for the purpose of pooling together a large, retrospective dataset of 7 T MRIs acquired in multiple MS studies at five institutions. To date, this group has contributed five-hundred and twenty-eight 7 T MRI scans from 350 individuals with MS to a common data repository, with plans to continue to increase this sample size in the coming years. We have developed unified methods for image processing for data harmonization and lesion identification/segmentation. We report here our initial observations on intersite differences in acquisition, which includes site/device differences in brain coverage and image quality. We also report on the development of our methods and training of image evaluators, which resulted in median Dice Similarity Coefficients for trained raters' annotation of cortical and deep gray matter lesions, paramagnetic rim lesions, and meningeal enhancement between 0.73 and 0.82 compared to final consensus masks. We expect this publication to act as a resource for other investigators aiming to combine multicenter 7 T MRI datasets for the study of MS, in addition to providing a methodological reference for all future analysis projects to stem from the development of this dataset.

An Efficient Multiple Sclerosis Segmentation Framework using Hybrid Dilated Convolution –based Adaptive Mobilenet Mechanism.

Multiple Sclerosis (MS) is considered a very popular neurological condition in adults. Poor walking stability is considered the primary sign of MS. The Magnetic Resonance Imaging (MRI) is ensured to be a delicate approach for discovering disease progression. Severity quantification of the disease via MS lesion volume validation with MR imaging is significant for monitoring and learning the disorder and its treatment. Numerous techniques for MS lesions segmentation demand experts seed points as input, yet do not sufficiently permit the specialists to perform effectively or intuitively. Interestingly, various approaches also consider that the points are optimally determined. Hence, it is necessary to rectify some of the troubles that existed in classical MS classification mechanisms by developing a powerful process with deep learning in this work. In the beginning, the requisite MS images are fetched from the existing online source and forwarded to the module of image segmentation. In this module, an efficient image segmentation operation is carried out employing Transformer Unet++ with MobileNetv3 (TUnet++-MNetv3). Further, the segmented images are given to the module named MS classification, where the Hybrid Dilated Convolution based Adaptive MobileNet (HDC-AMNet) is recommended to classify the MS. Moreover, the Enhanced Single Candidate Optimizer (ESCO) is adopted to optimize the parameters that exist in the MobileNet. Thus, the designed MS classification task achieves improved performance rates by contrasting it with its baseline techniques employing numerous metrics.

Bibliometric analysis of myelin imaging studies of patients with multiple sclerosis (2000-2022).

Multiple sclerosis (MS) is a condition that can impact the central nervous system (CNS) and cause damage to the myelin, which is responsible for facilitating the normal transmission of electrical impulses along the nerves. We performed a bibliometric analysis of the scientific publications on myelin imaging in MS to reveal the development trends in this field and to evaluate research trends in myelin imaging in MS. The Web of Science Core Collection was searched for articles related to myelin imaging in MS published between January 2000 and December 2022. CiteSpace, VOSviewer, and R language were used to evaluate and visualize contributions by and co-occurrence relationships among countries and institutions, authors, journals, citations, keywords, and so on. A total of 1,639 articles addressed the topic of myelin imaging in MS. The United States had the largest number of annual publications. The University of London was the institution with the highest number of publications (n=118) and citations (n=9,885). The top 3 productive authors were all from the University of British Columbia in Canada. An article published by Mackay et al. in 1994 had the most citations (n=272). Neuroimage [impact factor (IF) =7.40, Journal Citation Reports quartile 1 (Q1)] was the most productive journal in terms of the number of articles relating to myelin imaging in MS (n=149). In recent years, myelin water imaging, synthetic magnetic resonance imaging (SyMRI), inhomogeneous magnetization, positron emission tomography (PET) imaging, and aquaporin-4 (AQP4) have been researched hotspots of myelin imaging in MS. With advancements in the pathophysiological research on myelin changes in MS, myelin imaging is playing an important role in the diagnosis and treatment of MS. In addition, the use of new sequences of myelin imaging to distinguish MS from other inflammatory demyelinating diseases is a future development trend in this field.

Determination of Efficiency of 3D Fluid-Attenuated Inversion Recovery (FLAIR) in the Imaging of Multiple Sclerosis in Comparison With 2D FLAIR at 3-Tesla MRI.

A fluid-attenuated inversion recovery (FLAIR) method eliminates the cerebrospinal fluid (CSF) signal, enhancing white matter lesion detection by enhancing the contrast between the lesion and CSF. Three-dimensional (3D) volume acquisition has the advantage of multiplanar reformation of contiguous slices yielding improved signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs). To our knowledge, there are only three studies comparing 3D- and 2D-FLAIR sequences with respect to multiple sclerosis (MS) lesions at 3 tesla. This study aimed to determine the efficiency of 3D-FLAIR in the detection of lesions of multiple sclerosis in terms of spatial and contrast resolutions in comparison with 2D-FLAIR sequences. A total of 75 patients with MS undergoing magnetic resonance imaging (MRI) brain at the Department of Radiology, Krishna Institute of Medical Sciences (KIMS), Secunderabad, Telangana, India. This is an observational comparative study. Independent-samples t-tests were performed in the present study to compare the number of lesions detected. The measured CNR and SNR values were subjected to Mann-Whitney U test. As a result of the 3D-FLAIR, more lesions were found as compared to 2D-FLAIR (p = 0.001). There was a greater CNRs for 3D-FLAIR images than for 2D-FLAIR images (p = 0.001). Lesions, CSF, white matter, and gray matter showed significantly higher SNRs with 3D-FLAIR (p = 0.001). 3D-FLAIR has exhibited greater sensitivity in detecting lesions associated with MSwhen contrasted with the 2D-FLAIR sequence. Significantly more lesions and higher SNRs and CNRs were detected with 3D-FLAIR in contrast to 2D-FLAIR. 3D-FLAIR may be considered the sequence of choice for MS imaging in the future.

Ultrahigh-field MRI: where it really makes adifference.

Currently, two major magnetic resonance (MR) vendors provide commercial 7‑T scanners that are approved by the Food and Drug Administration (FDA) for clinical application. There is growing interest in ultrahigh-field MRI because of the improved clinical results in terms of morphological detail, as well as functional and metabolic imaging capabilities. The 7‑T systems benefit from ahigher signal-to-noise ratio, which scales supralinearly with field strength, asupralinear increase in the blood oxygenation level dependent (BOLD) contrast for functional MRI and susceptibility weighted imaging (SWI), and the chemical shift increases linearly with field strength with consequently higher spectral resolution. In multiple sclerosis (MS), 7‑T imaging enables visualization of cortical lesions, the central vein sign, and paramagnetic rim lesions, which may be beneficial for the differential diagnosis between MS and other neuroinflammatory diseases in challenging and inconclusive clinical presentations and are seen as promising biomarkers for prognosis and treatment monitoring. The recent development of high-resolution proton MR spectroscopic imaging in clinically reasonable scan times has provided new insights into tumor metabolism and tumor grading as well as into early metabolic changes that may precede inflammatory processes in MS. This technique also improves the detection of epileptogenic foci in the brain. Multi-nuclear clinical applications, such as sodium imaging, have shown great potential for the evaluation of repair tissue quality after cartilage transplantation and in the monitoring of newly developed cartilage regenerative drugs for osteoarthritis. For special clinical applications, such as SWI in MS, MR spectroscopic imaging in tumors, MS and epilepsy, and sodium imaging in cartilage repair, 7T may become anew standard.

Multiple sclerosis imaging in clinical practice: a European-wide survey of 428 centers and conclusions by the ESNR Working Group.

To evaluate compliance with the available recommendations, we assessed the current clinical practice of imaging in the evaluation of multiple sclerosis (MS). An online questionnaire was emailed to all members and affiliates. Information was gathered on applied MR imaging protocols, gadolinium-based contrast agents (GBCA) use and image analysis. We compared the survey results with the Magnetic Resonance Imaging in MS (MAGNIMS) recommendations considered as the reference standard. A total of 428 entries were received from 44 countries. Of these, 82% of responders were neuroradiologists. 55% performed more than ten scans per week for MS imaging. The systematic use of 3T is rare (18%). Over 90% follow specific protocol recommendations with 3D FLAIR, T2-weighted and DWI being the most frequently used sequences. Over 50% use SWI at initial diagnosis and 3D gradient-echo T1-weighted imaging is the most used MRI sequence for pre- and post-contrast imaging. Mismatches with recommendations were identified including the use of only one sagittal T2-weighted sequence for spinal cord imaging, the systematic use of GBCA at follow-up (over 30% of institutions), a delay time shorter than 5min after GBCA administration (25%) and an inadequate follow-up duration in pediatric acute disseminated encephalomyelitis (80%). There is scarce use of automated software to compare images or to assess atrophy (13% and 7%). The proportions do not differ significantly between academic and non-academic institutions. While current practice in MS imaging is rather homogeneous across Europe, our survey suggests that recommendations are only partially followed. Hurdles were identified, mainly in the areas of GBCA use, spinal cord imaging, underuse of specific MRI sequences and monitoring strategies. This work will help radiologists to identify the mismatches between their own practices and the recommendations and act upon them. • While current practice in MS imaging is rather homogeneous across Europe, our survey suggests that available recommendations are only partially followed. • Several hurdles have been identified through the survey that mainly lies in the areas of GBCA use, spinal cord imaging, underuse of specific MRI sequences and monitoring strategies.

Quantitative MRI identifies lesional and non-lesional abnormalities in MOGAD.

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a distinct central nervous system (CNS) disorder that shares features with multiple sclerosis (MS) and may be misdiagnosed as MS. MOGAD and MS share a frequently relapsing clinical course and lesions with inflammatory demyelinating pathology. One key feature of MS pathology is tissue damage in normal-appearing white matter (NAWM) outside of discrete lesions, whereas the extent to which similar non-lesional damage occurs in MOGAD is not known and could be assessed using qGRE. The goal of this study was to examine the brains of people with MOGAD using quantitative gradient-recalled echo (qGRE) magnetic resonance imaging and to compare tissue damage with MS patients matched for disability. MOGAD and MS patients were recruited to match in terms of age and disability. Similarly aged healthy control (HC) data were drawn from existing studies. qGRE brain imaging of HC (N=15), MOGAD (N=17), and MS (N=15) patients was used to examine the severity and extent of tissue damage within and outside of discrete lesions. The qGRE metric R2t* is sensitive to changes in tissue microstructure and was measured in white matter lesions (WMLs), NAWM, cortical (CGM) and deep gray matter (DGM). Statistical inference was performed with linear models. R2t* was reduced in CGM (p=0.00047), DGM (p=0.0055) and NAWM (p=0.0019) in MOGAD and MS compared to similar regions in age-matched HCs. However, the degree of R2t* reduction in all these regions was less in the MOGAD patients compared with MS. WMLs in MOGAD demonstrated reduced R2t* compared to NAWM but this reduction was modest compared to changes associated with WMLs in MS (p=0.026). These results demonstrate abnormalities in lesional and non-lesional CNS tissues in MOGAD that are not detectable on standard MRI. The abnormalities seen in NAWM, CGM, and DGM were less severe in MOGAD compared to MS. MOGAD-related WMLs showed reduced R2t*, but were less abnormal than WMLs in MS. These data reveal damage to non-lesional tissues in two different demyelinating diseases, suggesting that damage outside of WMLs may be a common feature of demyelinating diseases. The lesser degree of R2t* abnormality in MOGAD tissues compared to MS suggests less underlying tissue damage and may underlie the greater propensity for recovery in MOGAD.

Psychometrics of the Multiple Sclerosis and Magnetic Resonance Imaging Knowledge Patient Questionnaire: A Pilot Study.

BACKGROUND: In the last few decades, the revised diagnostic criteria for multiple sclerosis (MS) and the development of numerous new MS therapies have created the possibility of early diagnosis and opportune MS management. However, these changes, and the mounting emphasis on patient choice and shared decision making, have not been accompanied by improvements in information provided to patients with MS (PwMS). Information provision for PwMS increases disease-related knowledge that may assist them in decision making and quality of life. The purpose of this study was to validate the Multiple Sclerosis and Magnetic Resonance Imaging Knowledge Questionnaire (MSMRIKQ) to improve patients' shared decision-making capabilities. METHODS: In this methodological study, the instrument development process was completed in 4 stages using principles of measurement theory: (1) establishing a pool of items after patient and clinician panels' review, (2) evaluating the validity of the scale-both face and content validity, (3) pilot testing the scale, and (4) post-pilot testing statistical analysis of items and scale reliability and validity. RESULTS: The 20-item instrument was finalized based on a panel of MS experts' and patients' comments. The final version of the MSMRIKQ was pilot tested with a sample of 46 PwMS. Face validity was established on all MSMRIKQ items. Individual item-level and scale-level content validity indices for universal agreement estimates were acceptable at item-level content validity index = 1 and scale-level content validity index = 1 for the 20 items. The Kuder-Richardson 20 reliability estimate for the entire scale was 0.58; the Kuder-Richardson 20 estimates for the subscales of MS and magnetic resonance imaging knowledge were 0.35 and 0.51, respectively. The split-half reliability with Spearman-Brown correction for the total scale was 0.60. The Poisson regression model was significant for predicting MS knowledge within this population. CONCLUSION: The MSMRIKQ is a basic knowledge instrument for clinical and research use.

Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study.

Generative adversarial networks (GANs) can synthesize high-contrast MRI from lower-contrast input. Targeted translation of parenchymal lesions in multiple sclerosis (MS), as well as visualization of model confidence further augment their utility, provided that the GAN generalizes reliably across different scanners. We here investigate the generalizability of a refined GAN for synthesizing high-contrast double inversion recovery (DIR) images and propose the use of uncertainty maps to further enhance its clinical utility and trustworthiness. A GAN was trained to synthesize DIR from input fluid-attenuated inversion recovery (FLAIR) and T1w of 50 MS patients (training data). In another 50 patients (test data), two blinded readers (R1 and R2) independently quantified lesions in synthetic DIR (synthDIR), acquired DIR (trueDIR) and FLAIR. Of the 50 test patients, 20 were acquired on the same scanner as training data (internal data), while 30 were scanned at different scanners with heterogeneous field strengths and protocols (external data). Lesion-to-Background ratios (LBR) for MS-lesions vs. normal appearing white matter, as well as image quality parameters were calculated. Uncertainty maps were generated to visualize model confidence. Significantly more MS-specific lesions were found in synthDIR compared to FLAIR (R1: 26.7 ± 2.6 vs. 22.5 ± 2.2 p < 0.0001; R2: 22.8 ± 2.2 vs. 19.9 ± 2.0, p = 0.0005). While trueDIR remained superior to synthDIR in R1 [28.6 ± 2.9 vs. 26.7 ± 2.6 (p = 0.0021)], both sequences showed comparable lesion conspicuity in R2 [23.3 ± 2.4 vs. 22.8 ± 2.2 (p = 0.98)]. Importantly, improvements in lesion counts were similar in internal and external data. Measurements of LBR confirmed that lesion-focused GAN training significantly improved lesion conspicuity. The use of uncertainty maps furthermore helped discriminate between MS lesions and artifacts. In conclusion, this multicentric study confirms the external validity of a lesion-focused Deep-Learning tool aimed at MS imaging. When implemented, uncertainty maps are promising to increase the trustworthiness of synthetic MRI.

Radiologically Isolated Syndrome Conversion to Multiple Sclerosis

Introduction: Radiologically isolated syndrome (RIS) is currently not a subtype of multiple sclerosis (MS) however, in the individuals with normal neurological examination and no clinical MS signs whose magnetic resonance imaging (MRI) data fulfill MS imaging criteria, RIS is concluded. We aimed to investigate and descriptively report the demographic, clinical and laboratory findings in 41 RIS subjects.

Automatic deep learning multicontrast corpus callosum segmentation in multiple sclerosis.

Background and PurposeCorpus callosum (CC) atrophy is predictive of future disability in multiple sclerosis (MS). However, current segmentation methods are either labor‐ or computationally intensive. We therefore developed an automated deep learning‐based CC segmentation tool and hypothesized that its output would correlate with disability.MethodsA cohort of 631 MS patients (449 females, baseline age 41 ± 11 years) with both 3‐dimensional T1‐weighted and T2‐weighted fluid‐attenuated inversion recovery (FLAIR) MRI was used for the development. Data from 204 patients were manually segmented to train convolutional neural networks in extracting the midsagittal intracranial and CC areas. Remaining data were used to compare segmentations with FreeSurfer and benchmark the outputs with regard to clinical correlations. A 1.5 and 3 Tesla reproducibility cohort of 9 MS patients evaluated the segmentation robustness.ResultsThe deep learning‐based tool was accurate in selecting the appropriate slice for segmentation (98% accuracy within 3 mm of the manual ground truth) and segmenting the CC (Dice coefficient .88‐.91) and intracranial areas (.97‐.98). The accuracy was lower with higher atrophy. Reproducibility was excellent (intraclass correlation coefficient > .90) for T1‐weighted scans and moderate‐good for FLAIR (.74‐.75). Segmentations were associated with baseline and future (average follow‐up time 6‐7 years) Expanded Disability Status Scale (ρ = –.13 to –.24) and Symbol Digit Modalities Test (r = .18‐.29) scores.ConclusionsWe present a fully automatic deep learning‐based CC segmentation tool optimized to modern imaging in MS with clinical correlations on par with computationally expensive alternatives.

BMAT: An open-source BIDS managing and analysis tool

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) has been recently proposed. The BIDS standard eases data sharing and processing within or between centers by providing guidelines for their description and organization. However, the transformation from the complex unstructured non-open file data formats coming directly from the MRI scanner to a correct BIDS structure can be cumbersome and time consuming. This hinders a wider adoption of the BIDS format across different study centers. To solve this problem and ease the day-to-day use of BIDS for the neuroimaging scientific community, we present the BIDS Managing and Analysis Tool (BMAT). The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies. BMAT provides the possibility to translate data from MRI scanners to the BIDS structure, create and manage BIDS datasets as well as develop and run automated processing pipelines, and is faster than its competitor. BMAT software propose the possibility to download useful analysis apps, especially applied to MS research with lesion segmentation and processing of imaging contrasts for novel disease biomarkers such as the central vein sign and the paramagnetic rim lesions.

Central vein sign: A putative diagnostic marker for multiple sclerosis.

The presence of a "central vein sign" (CVS) has been introduced as a biomarker for the diagnosis of multiple sclerosis (MS) and shown to have the ability to accurately differentiate MS from other white matter diseases (MS mimics). Following the development of susceptibility-based magnetic resonance venography that allowed the in vivo detection of CVS, a standard CVS definition was established by introducing the "40% rule" that assesses the number of MS lesions with CVS as a fraction of the total number of lesions to differentiate MS lesions from other types of lesions. The "50% rule," the "three-lesion criteria," and the "six-lesion criteria" were later introduced and defined. Each of these rules had high levels of sensitivity, specificity, and accuracy in differentiating MS from other diseases, which has been recognized by the Magnetic Resonance Imaging in MS (MAGNIMS) group and the Consortium of MS Centers task force. The North American Imaging in Multiple Sclerosis Cooperative even provided statements and recommendations aiming to refine, standardize and evaluate the CVS in MS. Herein, we review the existing literature on CVS and evaluate its added value in the diagnosis of MS and usefulness in differentiating it from other vasculopathies. We also review the histopathology of CVS and identify available automated CVS assessment methods as well as define the role of vascular comorbidities in the diagnosis of MS.

Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.

Walk Your Talk: Real-World Adherence to Guidelines on the Use of MRI in Multiple Sclerosis.

(1) Although guidelines about the use of MRI sequences for Multiple Sclerosis (MS) diagnosis and follow-up are available, variability in acquisition protocols is not uncommon in everyday clinical practice. The aim of this study was to evaluate the real-world application of MS imaging guidelines in different settings to clarify the level of adherence to these guidelines. (2) Via an on-line anonymous survey, neuroradiologists (NR) were asked about MRI protocols and parameters routinely acquired when MS patients are evaluated in their center, both at diagnosis and follow-up. Furthermore, data about report content and personal opinions about emerging neuroimaging markers were also retrieved. (3) A total of 46 participants were included, mostly working in a hospital or university hospital (80.4%) and with more than 10 years of experience (47.9%). We found a relatively good adherence to the suggested MRI protocols regarding the use of T2-weighted sequences, although almost 10% of the participants routinely acquired 2D sequences with a slice thickness superior to 3 mm. On the other hand, a wider degree of heterogeneity was found regarding gadolinium administration, almost routinely performed at follow-up examination (87.0% of cases) in contrast with the current guidelines, as well as a low use of a standardized reporting system (17.4% of cases). (4) Although the MS community is getting closer to a standardization of MRI protocols, there is still a relatively wide heterogeneity among NR, with particular reference to contrast administration, which must be overcome to guarantee an adequate quality of patients’ care in MS.

Choroid Plexus Enlargement in Inflammatory Multiple Sclerosis: 3.0-T MRI and Translocator Protein PET Evaluation.

Background Choroid plexuses (CPs) have been suggested as a key gateway for inflammation in experimental autoimmune encephalitis, but in vivo evidence of their involvement in multiple sclerosis (MS) is lacking. Purpose To assess CP volumetric and inflammatory changes in patients with MS versus healthy control participants. Materials and Methods This was a secondary analysis of 97 patients (61 with relapsing-remitting MS [RRMS] and 36 with progressive MS) and 44 healthy control participants who participated in three prospective 3.0-T brain MRI studies between May 2009 and September 2017. A subgroup of 37 patients and 19 healthy control participants also underwent translocator protein fluorine 18 (18F)-DPA-714 PET for neuroinflammation. Relapses and disability scores were collected at baseline and over 2 years. CPs were manually segmented on three-dimensional T1-weighted images; other brain volumes were additionally segmented. Volumes were expressed as a ratio of intracranial volume. The 18F-DPA-714 distribution volume ratio was quantified in parenchymal regions, whereas standardized uptake value was used for CP inflammation. Multivariable linear regression analyses were performed to assess CP volumetric and inflammatory differences between patients with MS and healthy control participants and correlations between CP volume and lesion load, brain volumes, 18F-DPA-714 uptake, and annualized relapse rate. Results Ninety-seven patients with MS (mean age, 42 years ± 12 [standard deviation]; 49 women) and 44 healthy control participants (mean age, 39 years ± 14; 23 women) underwent MRI. Thirty-seven patients with MS and 19 healthy control participants underwent PET. CPs were 35% larger in patients with MS (mean value, 15.9 × 10-4 ± 4.5) than in healthy control participants (mean value, 11.8 × 10-4 ± 3.8; P = .004). Subgroup analysis confirmed greater CP volume in patients with RRMS (mean value, 15.5 × 10-4 ± 4.6; P = .008) than in healthy control participants. CP enlargement was greater in patients with active lesions at MRI (mean volume, 18.2 × 10-4 ± 4.9 in patients with lesions that enhanced with gadolinium vs 14.9 × 10-4 ± 4 in patients with lesions that did not enhance with gadolinium; P < .001) and correlated with white matter lesion load (r = 0.39; 95% CI: 0.20, 0.55; P < .001) and 18F-DPA-714 binding in the thalami (r = 0.44; 95% CI: 0.22, 0.72; P = .04) and normal-appearing white matter (r = 0.5; 95% CI: 0.20, 0.71; P = .005). Moreover, it correlated with annualized relapse rate in patients with RRMS (r = 0.37; 95% CI: 0.1, 0.55; P = .005). Finally, patients with MS showed 18.5% higher CP 18F-DPA-714 uptake than control participants (mean value, 0.778 ± 0.23 vs 0.635 ± 0.15, respectively; P = .01). CP volume in patients with RRMS (r = 0.57; 95% CI: 0.37, 0.73; P = .009) correlated with higher 18F-DPA-714 uptake. Conclusion Choroid plexuses (CPs) are enlarged and inflamed in patients with multiple sclerosis (MS), particularly in those with relapsing-remitting MS with inflammatory profiles; CP volumetric analysis could represent an MS imaging marker. © RSNA, 2021 EudraCT no. 2008-004174-40; clinical trial registration nos. NCT02305264 and NCT01651520 Online supplemental material is available for this article.

New Prospects for Ultra-High-Field Magnetic Resonance Imaging in Multiple Sclerosis.

There is growing interest in imaging multiple sclerosis (MS) through the ultra-high-field (UHF) lens, which currently means a static magnetic field strength of 7 T or higher. Because of higher signal-to-noise ratio and enhanced susceptibility effects, UHF magnetic resonance imaging improves conspicuity of MS pathological hallmarks, among them cortical demyelination and the central vein sign. This could, in turn, improve confidence in MS diagnosis and might also facilitate therapeutic monitoring of MS patients. Furthermore, UHF imaging offers unique insight into iron-related pathology, leptomeningeal inflammation, and spinal cord pathologies in neuroinflammation. Yet, limitations such as the longer scanning times to achieve improved resolution and incipient safety data on implanted medical devices need to be considered. In this review, we discuss applications of UHF imaging in MS, its advantages and limitations, and practical aspects of UHF in the clinical setting.

Resonancia Magnética en Esclerosis Múltiple: un repaso de los principios básicos de imagenología y guías prácticas de uso

Resumen La imagenología por resonancia magnética (IRM), representa una ventana in vivo de la fisiopatología de la esclerosis múltiple (EM) y nos ha llevado a considerarla como una herramienta clave para el diagnóstico, manejo y toma de decisiones al iniciar o cambiar fármacos modificadores de la enfermedad (FARME). Resulta esencial estar familiarizado con las técnicas de imagen y los protocolos recomendados que pueden mejorar la detección de la actividad para poder proveer del mejor manejo posible1-2. Muchas veces existe un desacoplamiento entre clínicos y radiólogos en cuanto a los requisitos solicitados al ordenar un estudio de imagen, lo cual se puede traducir en un subóptimo manejo. Esto puede suceder por mala comunicación, desconocimiento de técnicas imagenológicas por parte de los clínicos o, por otra parte, porque los radiólogos muchas veces no son conscientes de la importancia que pueden tener las características específicas de IRM en el proceso de toma de decisiones del neurólogo tratante. De ahí la importancia de los protocolos estandarizados de resonancia magnética para que los médicos y radiólogos se familiaricen con ellos, a fin de mejorar la calidad de la imagen, la detección de lesiones y el informe radiológico. Esta revisión resume los principios básicos de la IRM en EM y compara las recomendaciones relevantes del Consorcio de Centros de EM (CMSC) y el consenso europeo de imagenología por resonancia magnética (MAGNIMS); presentando la información de una manera simple y profesional, fácil de seguir por expertos y no expertos en el campo. Palabras clave: imágenes de resonancia magnética en la red de esclerosis múltiple, imágenes de resonancia magnética, esclerosis múltiple

DeepGRAI (Deep Gray Rating via Artificial Intelligence): Fast, feasible, and clinically relevant thalamic atrophy measurement on clinical quality T2-FLAIR MRI in multiple sclerosis

BackgroundThalamic volume loss is a key marker of neurodegeneration in multiple sclerosis (MS). T2-FLAIR MRI is a common denominator in clinical routine MS imaging, but current methods for thalamic volumetry are not applicable to it. ObjectiveTo develop and validate a robust algorithm to measure thalamic volume using clinical routine T2-FLAIR MRI. MethodsA dual-stage deep learning approach based on 3D U-net (DeepGRAI – Deep Gray Rating via Artificial Intelligence) was created and trained/validated/tested on 4,590 MRI exams (4288 2D-FLAIR, 302 3D-FLAIR) from 59 centers (80/10/10 train/validation/test split). As training/test targets, FIRST was used to generate thalamic masks from 3D T1 images. Masks were reviewed, corrected, and aligned into T2-FLAIR space. Additional validation was performed to assess inter-scanner reliability (177 subjects at 1.5 T and 3 T within one week) and scan-rescan-reliability (5 subjects scanned, repositioned, and then re-scanned). A longitudinal dataset including assessment of disability and cognition was used to evaluate the predictive value of the approach. ResultsDeepGRAI automatically quantified thalamic volume in approximately 7 s per case, and has been made publicly available. Accuracy on T2-FLAIR relative to 3D T1 FIRST was 99.4% (r = 0.94, p < 0.001,TPR = 93.0%, FPR = 0.3%). Inter-scanner error was 3.21%. Scan-rescan error with repositioning was 0.43%. DeepGRAI-derived thalamic volume was associated with disability (r = -0.427,p < 0.001) and cognition (r = -0.537,p < 0.001), and was a significant predictor of longitudinal cognitive decline (R2 = 0.081, p = 0.024; comparatively, FIRST-derived volume was R2 = 0.080, p = 0.025). ConclusionsDeepGRAI provides fast, reliable, and clinically relevant thalamic volume measurement on multicenter clinical-quality T2-FLAIR images. This indicates potential for real-world thalamic volumetry, as well as quantification on legacy datasets without 3D T1 imaging.

P.285 Association of whole-brain perfusion and major depressive disorder diagnosis and severity measured by arterial spin labelling

Multiple sclerosis (MS) is a central nervous system disorder that may eventually affect its function. The clinical standard for MS severity is based on a clinical scale, which lacks lesion specific information. Magnetic resonance imaging of MS faces the challenge of myelin specificity, and in this work a new method inhomogeneous magnetization transfer (ihMT) is investigated as new biomarker of demyelination in MS.Local ethics committee approved this study and written informed consents were obtained. Between Oct 2017 to May 2018, eighteen patients with relapsing-remitting MS (RRMS) (6 males, 12 females, mean age 31.2) and sixteen healthy volunteers (6 males, 10 females, mean age 30.4 years) were enrolled in this prospective study. All subjects underwent MRI exams including MT and ihMT imaging as well as the Expanded Disability Status Scale (EDSS) assessments. Independent sample t-test were used to compare the difference of ihMT parameters between healthy white matter (HWM) and normal appearing white matter (NAWM) and between HWM and MS lesions, respectively. Spearman correlation were used to analyze the correlation between ihMT parameters of MS lesions and EDSS score.The ihMTR and qihMT demonstrate significant differences between WHM and NAWM groups, while no significant differences are observed for MTR and qMT. All parameters show significant differences between HWM and MS groups (p < 0.05). There was moderate negative correlation between MTR, qMT and EDSS score (−0.440 and −0.572), while there was a strong negative correlation between ihMTR and qihMT and EDSS score (−0.704 and −0.739).Based on whole brain analysis at 3.0 T, ihMT showed better correlation with EDSS compared to magnetization transfer imaging, and may be a potentially valuable biomarker for demyelination in MS.