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J Magn Reson Imaging. 2016 Jun;43(6):1445-54. doi: 10.1002/jmri.25095. Epub 2015 Nov 25.

Automated detection of white matter and cortical lesions in early stages of multiple sclerosis.

Author information

1
Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare AG, Lausanne, Switzerland.
2
Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
3
Laboratoire de Recherché en Neuroimagérie (LREN), Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
4
Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
5
Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
6
Neuroimmunology Unit, Neurology, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
7
Siemens Medical Solutions USA, Inc, Boston, MA, United States.
8
Signal Processing Core, Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland.
9
Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Chalestown, MA, United States.

Abstract

PURPOSE:

To develop a method to automatically detect multiple sclerosis (MS) lesions, located both in white matter (WM) and in the cortex, in patients with low disability and early disease stage.

MATERIALS AND METHODS:

We developed a lesion detection method, based on the k-nearest neighbor (k-NN) technique, to detect lesions as small as 0.0036 mL. This method uses the image intensity information from up to four different 3D magnetic resonance imaging (MRI) sequences (magnetization-prepared rapid gradient-echo, MPRAGE; magnetization-prepared two inversion-contrast rapid gradient-echo, MP2RAGE; 3D fluid-attenuated inversion recovery, FLAIR; and 3D double-inversion recovery, DIR), acquired on a 3T scanner. To these intensity features we added the information obtained by the spatial coordinates and tissue prior probabilities provided by the International Consortium for Brain Mapping (ICBM). Quantitative assessment was done in 39 early-stage MS patients with a "leave-one-out" cross-validation.

RESULTS:

The best lesion detection rate (DR) performance in WM was obtained using MP2RAGE, FLAIR, and DIR intensities (77% for lesions ≥0.0036 mL; 85% for lesions ≥0.005 mL). Similar results were obtained excluding the DIR intensity as well as when using only MPRAGE and FLAIR (DR = 75%, P = 0.5720). However, the combination of FLAIR with DIR and MP2RAGE appeared to be the best for detecting cortical lesions (DR = 62%), compared to the other combination of sequences (P < 0.001).

CONCLUSION:

For WM lesion detection, similar results were observed when only conventional clinical sequences (FLAIR, MPRAGE) were used compared to a combination of conventional and "advanced" sequences (MP2RAGE, DIR). Cortical lesion detection increased significantly when "advanced" sequences were used. J. Magn. Reson. Imaging 2015. J. Magn. Reson. Imaging 2016;43:1445-1454.

KEYWORDS:

cortical lesions; lesion detection; magnetic resonance imaging; multiple sclerosis

PMID:
26606758
DOI:
10.1002/jmri.25095
[Indexed for MEDLINE]

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