Evidence map›Paper›PMID 30829941›Full record

ArticleInvestigative radiology2019

Automated Detection and Segmentation of Multiple Sclerosis Lesions Using Ultra-High-Field MP2RAGE.

Mário João Fartaria, Pascal Sati, Alexandra Todea, Ernst-Wilhelm Radue, Reza Rahmanzadeh, Kieran OʼBrien, Daniel S Reich, Meritxell Bach Cuadra, Tobias Kober, Cristina Granziera

Abstract read
In one paragraph

Article in Investigative radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  13. 7T MPFLAIR versus MP2RAGE for Quantifying Lesion Volume in Multiple Sclerosis.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mário João FartariaDepartment of Radiology, Centre Hospitalier Universitaire Vaudois.
Pascal SatiTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD.
Alexandra TodeaDepartment of Radiology, Pourtalès Hospital, Neuchâtel.
Ernst-Wilhelm RadueNeurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
Reza RahmanzadehNeurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
Kieran OʼBrienCentre for Advanced Imaging, University of Queensland.
Daniel S ReichTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD.
Meritxell Bach CuadraDepartment of Radiology, Centre Hospitalier Universitaire Vaudois.
Tobias KoberDepartment of Radiology, Centre Hospitalier Universitaire Vaudois.
Cristina GranzieraNeurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.

Funding

Multimodal MRI in Multiple SclerosisZIANS003119 · NINDS · NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE · PI REICH, DANIEL · 2010 to 2025
$53.4M
Intramural NIH HHS ZIA NS003119
6 · The paper itself

Abstract

objectivesThe aim of this study was to develop a new automated segmentation method of white matter (WM) and cortical multiple sclerosis (MS) lesions visible on magnetization-prepared 2 inversion-contrast rapid gradient echo (MP2RAGE) images acquired at 7 T MRI. MATERIALS AND

methodsThe proposed prototype (MSLAST [Multiple Sclerosis Lesion Analysis at Seven Tesla]) takes as input a single image contrast derived from the 7T MP2RAGE prototype sequence and is based on partial volume estimation and topological constraints. First, MSLAST performs a skull-strip of MP2RAGE images and computes tissue concentration maps for WM, gray matter (GM), and cerebrospinal fluid (CSF) using a partial volume model of tissues within each voxel. Second, MSLAST performs (1) connected-component analysis to GM and CSF concentration maps to classify small isolated components as MS lesions; (2) hole-filling in the WM concentration map to classify areas with low WM concentration surrounded by WM (ie, MS lesions); and (3) outlier rejection to the WM mask to improve the classification of small WM lesions. Third, MSLAST unifies the 3 maps obtained from 1, 2, and 3 processing steps to generate a global lesion mask.

resultsQuantitative and qualitative assessments were performed using MSLAST in 25 MS patients from 2 research centers. Overall, MSLAST detected a median of 71% of MS lesions, specifically 74% of WM and 58% of cortical lesions, when a minimum lesion size of 6 μL was considered. The median false-positive rate was 40%. When a 15 μL minimal lesions size was applied, which is the approximation of the minimal size recommended for 1.5/3 T images, the median detection rate was 80% for WM and 63% for cortical lesions, respectively, and the median false-positive rate was 33%. We observed high correlation between MSLAST and manual segmentations (Spearman rank correlation coefficient, ρ = 0.91), although MSLAST underestimated the total lesion volume (average difference of 1.1 mL), especially in patients with high lesion loads. MSLAST also showed good scan-rescan repeatability within the same session with an average absolute volume difference and F1 score of 0.38 ± 0.32 mL and 84%, respectively.

conclusionsWe propose a new methodology to facilitate the segmentation of WM and cortical MS lesions at 7 T MRI, our approach uses a single MP2RAGE scan and may be of special interest to clinicians and researchers.

Indexed as

AdolescentAdultFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedMultiple SclerosisWhite MatterYoung Adult

Identifiers

PMID30829941
PMCPMC6499666

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.