Longitudinal Assessment of Multiple Sclerosis Lesion Load With Synthetic Magnetic Resonance Imaging—A Multicenter Validation Study

Abstract

IntroductionDouble inversion recovery (DIR) has been validated as a sensitive magnetic resonance imaging (MRI) contrast in multiple sclerosis (MS). Deep learning techniques can use basic input data to generate synthetic DIR (synthDIR) images that are on par with their acquired counterparts. As assessment of longitudinal MRI data is paramount in MS diagnostics, our study's purpose is to evaluate the utility of synthDIR longitudinal subtraction imaging for detection of disease progression in a multicenter data set of MS patients.MethodsWe implemented a previously established generative adversarial network to synthesize DIR from input T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences for 214 MRI data sets from 74 patients and 5 different centers. One hundred and forty longitudinal subtraction maps of consecutive scans (follow-up scan—preceding scan) were generated for both acquired FLAIR and synthDIR. Two readers, blinded to the image origin, independently quantified newly formed lesions on the FLAIR and synthDIR subtraction maps, grouped into specific locations as outlined in the McDonald criteria.ResultsBoth readers detected significantly more newly formed MS-specific lesions in the longitudinal subtractions of synthDIR compared with acquired FLAIR (R1: 3.27 ± 0.60 vs 2.50 ± 0.69 [P = 0.0016]; R2: 3.31 ± 0.81 vs 2.53 ± 0.72 [P < 0.0001]). Relative gains in detectability were most pronounced in juxtacortical lesions (36% relative gain in lesion counts—pooled for both readers). In 5% of the scans, synthDIR subtraction maps helped to identify a disease progression missed on FLAIR subtraction maps.ConclusionsGenerative adversarial networks can generate high-contrast DIR images that may improve the longitudinal follow-up assessment in MS patients compared with standard sequences. By detecting more newly formed MS lesions and increasing the rates of detected disease activity, our methodology promises to improve clinical decision-making.

How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review

“…In six cases, the only input provided to the network were either T2-w images ( Abhale et al, 2022 , Yildirim and Dandil, 2021a ), MPRAGE (Magnetisation-prepared rapid gradient echo) ( Galimzianova et al, 2015 , Spies et al, 2013 ), MP2RAGE (Magnetisation-prepared 2 rapid gradient echo) ( Fartaria et al, 2019 ) or MR fingerprinting EPI (Echo-planar imaging) ( Hermann et al, 2021 ). Less common contrasts, such as diffusion basis spectrum imaging ( Ye et al, 2020 ), DIR ( Fartaria et al, 2015 , Schläger et al, 2022 , Bouman et al, 2023 ) and PSIR ( Bouman et al, 2023 ) were also adopted.…”

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

“…The results of subtraction images are compared with post-T1W images to assess the accuracy. However, they did not perform lesion segmentation [ 7 , 14 , 46 , 47 , 48 ]. Thus, it is very difficult to compare our results with these published ones.…”

7 T Lesion-Attenuated Magnetization-Prepared Gradient Echo Acquisition for Detection of Posterior Fossa Demyelinating Lesions in Multiple Sclerosis

“…Because of the higher signal-to-noise ratio (SNR), 7 T MRI allows for higher spatial resolutions and provides better image contrast; therefore, it has shown better detection rates of cortical and juxtacortical lesions, improved sensitivity for the phase-rim sign, and superior detection of the central vein sign in MS imaging. [7][8][9][10] Despite these advantages, 7 T presents greater challenges with imaging the posterior fossa versus its 1.5/3 T counterparts due to B1+ inhomogeneity, limited z axis coverage, larger B0 inhomogeneities, and greater sensitivity to dynamic field fluctuations. [11][12][13][14][15][16][17] These cumulative effects lead to poor image contrast, low SNR, and image artifacts that frequently plague the brainstem, cerebellum, and upper cervical spinal cord.…”