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Automated Detection and Segmentation of Multiple Sclerosis Lesions Using Ultra-High-Field MP2RAGE.
Fartaria, Mário João; Sati, Pascal; Todea, Alexandra; Radue, Ernst-Wilhelm; Rahmanzadeh, Reza; OʼBrien, Kieran; Reich, Daniel S; Bach Cuadra, Meritxell; Kober, Tobias; Granziera, Cristina.
Afiliação
  • Fartaria MJ; Department of Radiology, Centre Hospitalier Universitaire Vaudois.
  • Sati P; Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne, Lausanne.
  • Todea A; Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD.
  • Radue EW; Department of Radiology, Pourtalès Hospital, Neuchâtel.
  • Rahmanzadeh R; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
  • OʼBrien K; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
  • Reich DS; Centre for Advanced Imaging, University of Queensland.
  • Bach Cuadra M; Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia.
  • Kober T; Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD.
  • Granziera C; Department of Radiology, Centre Hospitalier Universitaire Vaudois.
Invest Radiol ; 54(6): 356-364, 2019 06.
Article em En | MEDLINE | ID: mdl-30829941
ABSTRACT

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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador / Substância Branca / Esclerose Múltipla Tipo de estudo: Clinical_trials / Diagnostic_studies / Guideline / Prognostic_studies / Qualitative_research Limite: Adolescent / Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador / Substância Branca / Esclerose Múltipla Tipo de estudo: Clinical_trials / Diagnostic_studies / Guideline / Prognostic_studies / Qualitative_research Limite: Adolescent / Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2019 Tipo de documento: Article