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In Multiple sclerosis (Houndmills, Basingstoke, England)

BACKGROUND : Cortical lesions are highly inconspicuous on magnetic resonance imaging (MRI). Double inversion recovery (DIR) has a higher sensitivity than conventional clinical sequences (i.e. T1, T2, FLAIR) but is difficult to acquire, leading to overseen cortical lesions in clinical care and clinical trials.

OBJECTIVE : To evaluate the usability of artificially generated DIR (aDIR) images for cortical lesion detection compared to conventionally acquired DIR (cDIR).

METHODS : The dataset consisted of 3D-T1 and 2D-proton density (PD) T2 images of 73 patients (49RR, 20SP, 4PP) at 1.5 T. Using a 4:1 train:test-ratio, a fully convolutional neural network was trained to predict 3D-aDIR from 3D-T1 and 2D-PD/T2 images. Randomized blind scoring of the test set was used to determine detection reliability, precision and recall.

RESULTS : A total of 626 vs 696 cortical lesions were detected on 15 aDIR vs cDIR images (intraclass correlation coefficient (ICC) = 0.92). Compared to cDIR, precision and recall were 0.84 ± 0.06 and 0.76 ± 0.09, respectively. The frontal and temporal lobes showed the largest differences in discernibility.

CONCLUSION : Cortical lesions can be detected with good reliability on artificial DIR. The technique has potential to broaden the availability of DIR in clinical care and provides the opportunity of ex post facto implementation of cortical lesions imaging in existing clinical trial data.

Bouman Piet M, Strijbis Victor Ij, Jonkman Laura E, Hulst Hanneke E, Geurts Jeroen Jg, Steenwijk Martijn D


artificial intelligence, clinical trial, cortical lesions, double inversion recovery, magnetic resonance imaging