MRI MS DB Description: In the IMT-Segmentation folder there are 38 folders representing data for each patient 38patients). In each patient folder we have: 1) MRI TIFF Images from first and second examination (0 months, 6-12 months) 2) Lesion segmentations (*.plq files). The delineation/s can be loaded into matlab i.e load(file.plq, -.mat ); Then points can be drawn on the image. load( IM_00031_1.plq , -mat );

IntroductionMagnetic resonance imaging (MRI) is crucial for diagnosing and monitoring of multiple sclerosis (MS) as it is used to assess lesions in the brain and spinal cord. However, in real-world clinical settings, MRI scans are often acquired with thick slices, limiting their utility for automated quantitative analyses. This work presents a single-image super-resolution (SR) reconstruction framework that leverages SR convolutional neural networks (CNN) to enhance the through-plane resolution of structural MRI in people with MS (PwMS).MethodsOur strategy involves the supervised fine-tuning of CNN architectures, guided by a content loss function that promotes perceptual quality, as well as reconstruction accuracy, to recover high-level image features.ResultsExtensive evaluation with MRI data of PwMS shows that our SR strategy leads to more accurate MRI reconstructions than competing methods. Furthermore, it improves lesion segmentation on low-resolution MRI, approaching the performance achievable with high-resolution images.DiscussionResults demonstrate the potential of our SR framework to facilitate the use of low-resolution retrospective MRI from real-world clinical settings to investigate quantitative image-based biomarkers of MS.

Multiple Sclerosis Dataset, Brain MRI Object Detection & Segmentation Dataset

The dataset consists of .dcm files containing MRI scans of the brain of the person with a multiple sclerosis. The images are labeled by the doctors and accompanied by report in PDF-format. The dataset includes 13 studies, made from the different angles which provide a comprehensive understanding of a multiple sclerosis as a condition.

  MRI study angles in the dataset





  💴 For… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/multiple-sclerosis-dataset.

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21–49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10−6). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging (p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10−15) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.

This archive contains the part 1 of Shift Benchmark on Multiple Sclerosis lesion segmentation data. This dataset is provided by the Shifts Project to enable assessment of the robustness of models to distributional shift and the quality of their uncertainty estimates. This part is the MSSEG data collected in the digital repository of the OFSEP Cohort provided in the context of the MICCAI 2016 and 2021 challenges. A full description of the benchmark is available in https://arxiv.org/pdf/2206.15407. Part 2 of the data is available here. To find out more about the Shifts Project, please visit https://shifts.ai .

Abstract Background Multiple sclerosis (MS) is an inflammatory, degenerative, demyelinating disease that ranges from benign to rapidly progressive forms. A striking characteristic of the disease is the clinical-radiological paradox. Objectives The present study was conducted to determine whether, in our cohort, the clinical-radiological paradox exists and whether lesion location is related to clinical disability in patients with MS. Methods Retrospective data from 95 patients with MS (60 women and 35 men) treated at a single center were examined. One head-and-spine magnetic resonance imaging (MRI) examination from each patient was selected randomly, and two independent observers calculated lesion loads (LLs) on T2/fluid attenuation inversion recovery sequences manually, considering the whole brain and four separate regions (periventricular, juxtacortical, posterior fossa, and spinal cord). The LLs were compared with the degree of disability, measured by the Kurtzke Expanded Disability Status Scale (EDSS), at the time of MRI examination in the whole cohort and in patients with relapsing-remitting (RR), primarily progressive, and secondarily progressive MS. Results High LLs correlated with high EDSS scores in the whole cohort (r = 0.34; p< 0.01) and in the RRMS group (r = 0.27; p= 0.02). The EDSS score correlated with high regional LLs in the posterior fossa (r = 0.31; p= 0.002) and spinal cord (r = 0.35; p= 0.001). Conclusions Our results indicate that the clinical-radiological paradox is a myth and support the logical connection between lesion location and neurological repercussion.

Multiple Sclerosis (MS) is an idiopathic chronic inflammatory demyelinating disease of the central nervous system. Timely and accurate diagnosis via the McDonald criteria improves outcome but relies heavily on radiologist interpretation of MRI studies. A growing body of research is focused on identifying new MS lesions on MRI by comparing one time point with another but only limited longitudinal data are available.

We present a dataset of which contains 496 scans of 172 patients with MS each with at least 2 time points including T1, Flair and T2 sequences. There are 110 stable scans and 214 instances of change. We also include demographic information. Data has been anonymised, processed and segmented with three stages of expert opinion contribute to ground truth.

Future research using this dataset could include new lesion identification, radiomic characterisation of new lesions, relationship with disease activity and brain atrophy and time series applications including prediction of new lesions and active learning for efficient segmentation.

Objective: To assess the onset of ocrelizumab efficacy on brain magnetic resonance imaging (MRI) measures of disease activity in the Phase II study in relapsing-remitting multiple sclerosis (RRMS), and relapse rate in the pooled Phase III studies in relapsing multiple sclerosis (RMS).

Methods: Brain MRI activity was determined in the Phase II trial at monthly intervals in patients with RRMS receiving placebo, ocrelizumab (600 mg), or intramuscular interferon (IFN) β-1a (30 μg). Annualized relapse rate (ARR; over various epochs) and time to first relapse were analyzed in the pooled population of the Phase III OPERA I and OPERA II trials in patients with RMS receiving ocrelizumab (600 mg) or subcutaneous IFN β-1a (44 μg).

Results: In patients with RRMS, ocrelizumab reduced the number of new T1 gadolinium-enhancing lesions by Week 4 vs placebo (p=0.042) and by Week 8 vs intramuscular IFN β-1a (p<0.001). Ocrelizumab also reduced the number of new or enlarging T2 lesions appearing ...

IntroductionPatients with MS are MRI scanned continuously throughout their disease course resulting in a large manual workload for radiologists which includes lesion detection and size estimation. Though many models for automatic lesion segmentation have been published, few are used broadly in clinic today, as there is a lack of testing on clinical datasets. By collecting a large, heterogeneous training dataset directly from our MS clinic we aim to present a model which is robust to different scanner protocols and artefacts and which only uses MRI modalities present in routine clinical examinations.MethodsWe retrospectively included 746 patients from routine examinations at our MS clinic. The inclusion criteria included acquisition at one of seven different scanners and an MRI protocol including 2D or 3D T2-w FLAIR, T2-w and T1-w images. Reference lesion masks on the training (n = 571) and validation (n = 70) datasets were generated using a preliminary segmentation model and subsequent manual correction. The test dataset (n = 100) was manually delineated. Our segmentation model https://github.com/CAAI/AIMS/ was based on the popular nnU-Net, which has won several biomedical segmentation challenges. We tested our model against the published segmentation models HD-MS-Lesions, which is also based on nnU-Net, trained with a more homogenous patient cohort. We furthermore tested model robustness to data from unseen scanners by performing a leave-one-scanner-out experiment.ResultsWe found that our model was able to segment MS white matter lesions with a performance comparable to literature: DSC = 0.68, precision = 0.90, recall = 0.70, f1 = 0.78. Furthermore, the model outperformed HD-MS-Lesions in all metrics except precision = 0.96. In the leave-one-scanner-out experiment there was no significant change in performance (p < 0.05) between any of the models which were only trained on part of the dataset and the full segmentation model.ConclusionIn conclusion we have seen, that by including a large, heterogeneous dataset emulating clinical reality, we have trained a segmentation model which maintains a high segmentation performance while being robust to data from unseen scanners. This broadens the applicability of the model in clinic and paves the way for clinical implementation.

Database of human brain images derived from a realistic phantom and generated using a sophisticated MRI simulator. Custom simulations may be generated to match a user's selected parameters. The goal is to aid validation of computer-aided quantitative analysis of medical image data. The SBD contains a set of realistic MRI data volumes produced by an MRI simulator. These data can be used by the neuroimaging community to evaluate the performance of various image analysis methods in a setting where the truth is known. The SBD contains simulated brain MRI data based on two anatomical models: normal and multiple sclerosis (MS). For both of these, full 3-dimensional data volumes have been simulated using three sequences (T1-, T2-, and proton-density- (PD-) weighted) and a variety of slice thicknesses, noise levels, and levels of intensity non-uniformity. These data are available for viewing in three orthogonal views (transversal, sagittal, and coronal), and for downloading.

The primary objective of this study is to evaluate the effect of BG00012 (dimethyl fumarate) on brain magnetic resonance imaging (MRI) lesions in pediatric participants with relapsing-remitting multiple sclerosis (RRMS). The secondary objectives of this study are to characterize the pharmacokinetics of BG00012 in pediatric participants with RRMS and to evaluate the safety and tolerability of BG00012 in pediatric participants with RRMS.

CIS, clinical isolated syndrome; EDSS, Expanded Disability Status Scale; MRI, magnetic resonance imaging; MS, multiple sclerosis; PPMS, primary progressive multiple sclerosis; RRMS, relapsing-remitting multiple sclerosis; SD, standard deviation; SPMS, secondary progressive multiple sclerosis.

This is a Phase 3b, multicenter, international study conducted in 2 parts. Upon completion of the placebo-controlled period (Part 1), participants will have the option of enrolling in a 2-year open-label extension (Part 2).

Part 1: The primary objective of the study is to investigate whether treatment with natalizumab slows the accumulation of disability not related to relapses in participants with secondary progressive multiple sclerosis (SPMS).

The secondary objectives of Part 1 of this study are to determine the proportion of participants with consistent improvement in Timed 25-Foot Walk (T25FW), the change in participant-reported ambulatory status as measured by the 12-item MS Walking Scale (MSWS-12), the change in manual ability based on the ABILHAND Questionnaire, the impact of natalizumab on participant-reported quality of life using the Multiple Sclerosis Impact Scale-29 Physical (MSIS-29 Physical), the change in whole brain volume between the end of study and Week 24 using magnetic resonance imaging (MRI) and the proportion of participants experiencing progression of disability as measured by individual physical Expanded Disability Status Scale (EDSS) system scores.

Part 2: The primary objective of Part 2 of the study is to evaluate the safety profile of natalizumab in participants with SPMS.

The secondary objectives of Part 2 of the study are to investigate long-term disability (based on clinical or participant-reported assessments) in participants with SPMS receiving natalizumab treatment for approximately 4 years and to assess change in brain volume and T2 lesion volume.

Brain Cancer MRI Object Detection & Segmentation Dataset

The dataset consists of .dcm files containing MRI scans of the brain of the person with a cancer. The images are labeled by the doctors and accompanied by report in PDF-format.

The dataset includes 10 studies, made from the different angles which provide a comprehensive understanding of a brain tumor structure.

MRI study angles in the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F5939be1e93e8e0c9f1ff922f184f70fe%2FFrame%2079.png?generation=1707920286083259&alt=media" alt="">

💴 For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on TrainingData to buy the dataset

Types of diseases and conditions in the full dataset:

• Cancer
• Multiple sclerosis
• Metastatic lesion
• Arnold-Chiari malformation
• Focal gliosis of the brain
• AND MANY OTHER CONDITIONS

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F0f5a27b8872e85fe23bf742593dc4843%2F2.gif?generation=1707920414940375&alt=media" alt="">

The MRI scans provide high-resolution images of the anatomical structures, allowing medical professionals to visualize the tumor, its location, size, and surrounding tissues.

The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated tumor detection and segmentation, analyzing tumor response to different treatments, and studying the relationship between imaging features and clinical outcomes.

OTHER MEDICAL BRAIN MRI DATASETS:

• Brain MRI - Multiple Sclerosis Dataset
• Brain Anomaly Detection Dataset
• Normal Brain MRI Dataset
• Fazekas Detection - MRI Dataset

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

Content

The dataset includes:

• ST000001: includes subfolders with 10 studies. Each study includes MRI-scans in .dcm and .jpg formats,
• DICOMDIR: includes information about the patient's condition and links to access files,
• Brain_MRI_1.pdf: includes medical report, provided by the radiologist,
• .csv file: includes id of the studies and the number of files

Medical reports include the following data:

• Patient's demographic information,
• Description of the case,
• Preliminary diagnosis,
• Recommendations on the further actions

All patients consented to the publication of data

Medical data might be collected in accordance with your requirements.

TrainingData provides high-quality data annotation tailored to your needs

keywords: tumors, cloud, testing, glioma, related, pytorch, directories, science, improve, directory, malignant, classify, accuracy, level, classified, cancerous, magnetic, neural, resonance, mri brain scan, brain tumor, brain cancer, oncology, neuroimaging, radiology, brain metastasis, glioblastoma, meningioma, pituitary tumor, medulloblastoma, astrocytoma, oligodendroglioma, ependymoma, neuro-oncology, brain lesion, brain metastasis detection, brain tumor classification, brain tumor segmentation, brain tumor diagnosis, brain tumor prognosis, brain tumor treatment, brain tumor surgery, brain tumor radiation therapy, brain tumor chemotherapy, brain tumor clinical trials, brain tumor research, brain tumor awareness, brain tumor support, brain tumor survivor, neurosurgery, neurologist, neuroradiology, neuro-oncologist, neuroscientist, medical imaging, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, computer vision, deep learning

MSLesSeg is a publicly accessible MRI dataset aimed at advancing research in Multiple Sclerosis (MS) lesion segmentation. It includes 115 MRI scans from 75 patients, featuring T1, T2, and FLAIR sequences. The dataset is enriched with supplementary clinical data gathered from multiple sources. Expert-validated annotations provide high-quality lesion segmentation labels, offering a reliable, human-labeled benchmark for evaluating segmentation algorithms.

BackgroundThe monoclonal antibody natalizumab (NTZ) is a highly effective treatment for patients with multiple sclerosis (MS). However, this drug is associated with increased risk of developing Progressive Multifocal Leukoencephalopathy (PML), an opportunistic infection of central nervous system (CNS) caused by the John Cunningham polyomavirus (JCV).ObjectiveTo describe the 12-month clinical course of 39 patients with MS (28 women, 11 men) who developed NTZ-related PML after a mean exposure of 39 infusions.MethodsAn Italian independent collaborative repository initiative collected and analyzed socio-demographic, clinical, magnetic resonance imaging (MRI) data and number of JCV-DNA copies detected on cerebrospinal fluid (CSF) samples of patients diagnosed as affected by NTZ-related PML. The evolution of disability, measured by the Expanded Disability Status Scale, was assessed at NTZ start, at PML diagnosis and after 2, 6 and 12 months from PML diagnosis. The effect of clinical and paraclinical characteristics at PML diagnosis on the final outcome was also investigated.ResultsTen patients (25.6%) were diagnosed before 24 NTZ infusions. In six cases (15.4%) the PML suspect was made on the basis of highly suggestive MRI findings in absence of any detectable change of clinical conditions (asymptomatic PML). In patients with symptomatic PML, the diagnosis was quicker for those who presented with cognitive symptoms (n = 12) rather than for those with other neurological pictures (n = 21) (p = 0.003). Three patients (7.7%) died during the 12-month observation period, resulting in a survival rate of 92.3%. Asymptomatic PML, more localized brain involvement and gadolinium-enhancement detected at MRI, as well as lower viral load were associated with a better disability outcome (p-values

While inspecting the brain magnetic resonance imaging (MRI) scans from a sample of Multiple Sclerosis (MS) patients, blind to any clinical, cognitive and demographic information, it caught our attention the presence of ovoidal or circular, partially stellate, regions of signal intensities similar to that of the normal brain parenchyma in Fluid Attenuated Inversion Recovery (FLAIR) surrounded by hyperintensities in the periventricular region in a reasonable number of scans, seemingly corresponding in all cases to hypointense regions (i.e. with the same signal level of the cerebrospinal fluid) in T1-weighted. The ovoidal shape of these features, clearly distinctive due to their homogeneously lower signal with respect to their surroundings in the FLAIR sequence prompted us to refer them as FLAIR 'pseudocavities'. The idea that they could be differentially distinctive and indicative of an underlying process of different aetiology from their surroundings is not implausible. Inversion recovery imaging can potentially discriminate among tissues based on subtle differences in T1 characteristics. Specifically, the FLAIR sequence exploits the fact that many types of pathology have elevated T1 and T2 values resulting from increased free water content compared to background tissue. Higher specific absorption rate due to additional 180 degrees, together with the increased dynamic range, and the additive T1 and T2 contrast, make FLAIR highly susceptible to differentially reflect subtle pathological processes (Bydder & Young, 1985). We, hence, systematically reviewed the literature in the last 10 years (i.e. from March 1999 up to March 2019) to investigate the definitions of MS lesions used up to date and their characterisation, to establish if what we called 'FLAIR 'pseudocavities'' have been described previously. This dataset is conformed by an excel file (Microsoft excel 97-2003 (.xls)) with multiple worksheets which contain all the references found in the two databases explored (i.e. Medline and EMBASE), as well as the data extracted and the results of the analyses. Briefly, from just over a hundred studies that defined MRI lesions in MS, more than half characterised lesions based on the criteria that they were hyperintense on T2-weighted, FLAIR and PD-weighted series, and more than a quarter of the studies characterised lesions based on the criteria that they were hyperintense on T2-weighted, FLAIR and PD-weighted and that they were hypointense on T1-weighted series. The literature review confirmed that what we refer to as FLAIR 'pseudocavities' have not yet been acknowledged in the MS literature. Note: The dataset contains a master excel spreadsheet with multiple worksheets. The data from each worksheet in the excel file is also provided as a .csv file

An open relaxation-diffusion MRI dataset in neurosurgical studies

The MRI data was collected from 18 patients (including glioma, meningioma, diffuse large B-cell, multiple sclerosis, cortical cerebral infarction, and brain abscess) and two healthy individuals (11 females and 9 males; age range: 28.0 - 70.0 years; median age: 51.0 years; IQR: 21.5 years). The rdMRI data is acquired on a 3T Philips MRI scanner with 7 TEs (two healthy individuals has only 5 TEs, sub-05 and sub-15). All participants provided written informed consent before participation and signed informed consent regarding publishing their data. The Research Ethics Committee, Faculty of Medicine in Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, China approved the study protocols.

Contact information:

• Name: Ye Wu
• Email: dr.yewu@outlook.com

Of all subjects the following data were acquired:

• T1-weighted three-dimensional (3D) turbo field-echo (TFE) anatomical scan (anat)
• multi-echo multi-shell HARDI diffusion-weighted MRI (dwi, acq=PA)
  • ses-01: TE=75ms, Delta=35.9ms, delta=19.9ms
  • ses-02: TE=85ms, Delta=40.9ms, delta=24.9ms
  • ses-03: TE=95ms, Delta=45.9ms, delta=29.9ms
  • ses-04: TE=105ms, Delta=50.9ms, delta=34.9ms
  • ses-05: TE=115ms, Delta=55.9ms, delta=39.9ms
  • ses-06: TE=125ms, Delta=60.9ms, delta=44.9ms
  • ses-07: TE=135ms, Delta=65.9ms, delta=49.9ms
• demographic information

The "derivatives" folder contains:

• dwipreproc, preprocessed dwi dataset
• anatpreproc, preprocessed anat dataset, and tissue segmentation obtained with the SynthSeg toolbox described here https://doi.org/10.1016/j.media.2023.102789 and https://doi.org/10.1073/pnas.2216399120.
• freesurfer, cortical parcellation generated by Freesurfer v7.3.2
• dwiqc, quality assessment of dwi dataset, implemented with https://dsi-studio.labsolver.org/ and https://github.com/pnlbwh/SlicerDiffusionQC.
• anatqc, quality assessment of anat dataset, implemented with https://github.com/pnlbwh/structuralQC
• pathlogy, tumor pathology images

Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS). METHODS: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability. RESULTS: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate. The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression. CONCLUSION: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

Cohort 2 assays are reported in the current study MTBLS558. Cohort 1 assays are reported in MTBLS1464.

Objective: To assess treatment-related spatio-temporal dynamics of active MRI lesions in relapsing-remitting multiple sclerosis (RRMS) patients.Methods: We performed a post-hoc analysis of MRI data acquired at weeks 4, 8, 12, and 16, in RRMS patients from the multicenter randomized IMPROVE study, which compares patients treated with 44 mcg subcutaneous interferon β-1a three times weekly (n = 120) versus placebo (n = 60). We created lesion probability maps (LPMs) of the cumulative combined unique active (CUA) lesions in each patient group at each time point. Group differences were tested in terms of lesion spatial distribution and frequency of occurrence.Results: Spatial distribution of CUA lesions throughout the study was less widespread in the treated than placebo group, with a 50% lower lesion accrual (24 vs. 48 cm3/month). Similar results were obtained with the WM tract analysis, with a reduction ranging from −47 to −66% in the treated group (p < 0.001). On voxel-wise analysis, CUA lesion frequency was lower in the treated group than the placebo group at week 4 (p = 0.07, corrected), becoming particularly pronounced (p ≤ 0.03, corrected) from week 8 onwards in large clusters of WM tracts, with peaks along fronto-parietal parts of the corticospinal tract, thalamic radiation, and superior longitudinal fascicle.Conclusion: LPM showed, in the short term, a treatment-related reduction of MRI lesion activity in RRMS patients in specific, clinically relevant brain locations. Such a quantitative approach might be a promising additional endpoint in future MS studies alongside the number and volume of WM lesions.Clinical Trial Registration:ClinicalTrials.gov identifier NCT00441103.