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

Fartaria, Mário João, PhD*,†,‡; Sati, Pascal, PhD§; Todea, Alexandra, MD; Radue, Ernst-Wilhelm, MD; Rahmanzadeh, Reza, MD; O'Brien, Kieran, PhD#,**; Reich, Daniel S., MD, PhD§; Bach Cuadra, Meritxell, PhD†,‡,††; Kober, Tobias, PhD*,†,‡; Granziera, Cristina, MD, PhD¶,‡‡,§§

doi: 10.1097/RLI.0000000000000551
Original Articles
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Objectives The 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 Methods The 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.

Results Quantitative 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.

Conclusions We 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.

From the *Advanced Clinical Imaging Technology, Siemens Healthcare AG;

Department of Radiology, Centre Hospitalier Universitaire Vaudois;

Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne, Lausanne;

§Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD;

Department of Radiology, Pourtalès Hospital, Neuchâtel;

Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland;

#Centre for Advanced Imaging, University of Queensland;

**Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia;

††Medical Image Analysis Laboratory, Centre d'Imagerie BioMédicale, Lausanne;

‡‡Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel; and

§§Department of Biomedical Engineering, University of Basel, Basel, Switzerland.

Received for publication November 2, 2018; and accepted for publication, after revision, December 19, 2018.

Conflicts of interest and sources of funding: This study was supported financially by the Intramural Research Program of the National Institute of Neurological Disorders and Stroke.

Correspondence to: Mário João Fartaria, PhD, EPFL QI-E 4 126, 1015 Lausanne, Switzerland. E-mail: mario.fartaria_de_oliveira@siemens-healthineers.com.

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