serval:BIB_765C195C3334
RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis.
10.1016/j.nicl.2020.102412
000600619100055
32961401
Barquero
G.
author
La Rosa
F.
author
Kebiri
H.
author
Lu
P.J.
author
Rahmanzadeh
R.
author
Weigel
M.
author
Fartaria
M.J.
author
Kober
T.
author
Théaudin
M.
author
Du Pasquier
R.
author
Sati
P.
author
Reich
D.S.
author
Absinta
M.
author
Granziera
C.
author
Maggi
P.
author
Bach Cuadra
M.
author
article
2020
NeuroImage. Clinical
2213-1582
2213-1582
journal
28
102412
In multiple sclerosis (MS), the presence of a paramagnetic rim at the edge of non-gadolinium-enhancing lesions indicates perilesional chronic inflammation. Patients featuring a higher paramagnetic rim lesion burden tend to have more aggressive disease. The objective of this study was to develop and evaluate a convolutional neural network (CNN) architecture (RimNet) for automated detection of paramagnetic rim lesions in MS employing multiple magnetic resonance (MR) imaging contrasts.
Imaging data were acquired at 3 Tesla on three different scanners from two different centers, totaling 124 MS patients, and studied retrospectively. Paramagnetic rim lesion detection was independently assessed by two expert raters on T2*-phase images, yielding 462 rim-positive (rim+) and 4857 rim-negative (rim-) lesions. RimNet was designed using 3D patches centered on candidate lesions in 3D-EPI phase and 3D FLAIR as input to two network branches. The interconnection of branches at both the first network blocks and the last fully connected layers favors the extraction of low and high-level multimodal features, respectively. RimNet's performance was quantitatively evaluated against experts' evaluation from both lesion-wise and patient-wise perspectives. For the latter, patients were categorized based on a clinically relevant threshold of 4 rim+ lesions per patient. The individual prediction capabilities of the images were also explored and compared (DeLong test) by testing a CNN trained with one image as input (unimodal).
The unimodal exploration showed the superior performance of 3D-EPI phase and 3D-EPI magnitude images in the rim+/- classification task (AUC = 0.913 and 0.901), compared to the 3D FLAIR (AUC = 0.855, Ps < 0.0001). The proposed multimodal RimNet prototype clearly outperformed the best unimodal approach (AUC = 0.943, P < 0.0001). The sensitivity and specificity achieved by RimNet (70.6% and 94.9%, respectively) are comparable to those of experts at the lesion level. In the patient-wise analysis, RimNet performed with an accuracy of 89.5% and a Dice coefficient (or F1 score) of 83.5%.
The proposed prototype showed promising performance, supporting the usage of RimNet for speeding up and standardizing the paramagnetic rim lesions analysis in MS.
Cognitive Neuroscience
Radiology Nuclear Medicine and imaging
Neurology
Clinical Neurology
Deep learning
Multimodal network
Multiple sclerosis
Paramagnetic rim lesions
Supervised classification
Susceptibility-based MRI
eng
60_published
UNIL//
CHUV//
true
peer-reviewed
Publication types: Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Intramural
Publication Status: ppublish
University of Lausanne
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