The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework — robust to variability in both image parameters and clinical condition — for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p≤0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of −15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsingremitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T 2-and T 2 *-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publiclyavailable analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P 5 0.001), which was further confirmed by absolute and normalized lesion volumes (P 5 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P 5 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P 5 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P 5 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.

BACKGROUND AND PURPOSE: Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversarial network training. MATERIALS AND METHODS:Forty patients with MS were prospectively included and scanned (3T) to acquire synthetic MR imaging and conventional FLAIR images. Synthetic FLAIR images were created with the SyMRI software. Acquired data were divided into 30 training and 10 test datasets. A conditional generative adversarial network was trained to generate improved FLAIR images from raw synthetic MR imaging data using conventional FLAIR images as targets. The peak signal-to-noise ratio, normalized root mean square error, and the Dice index of MS lesion maps were calculated for synthetic and deep learning FLAIR images against conventional FLAIR images, respectively. Lesion conspicuity and the existence of artifacts were visually assessed. RESULTS:The peak signal-to-noise ratio and normalized root mean square error were significantly higher and lower, respectively, in generated-versus-synthetic FLAIR images in aggregate intracranial tissues and all tissue segments (all P Ͻ .001). The Dice index of lesion maps and visual lesion conspicuity were comparable between generated and synthetic FLAIR images (P ϭ 1 and .59, respectively). Generated FLAIR images showed fewer granular artifacts (P ϭ .003) and swelling artifacts (in all cases) than synthetic FLAIR images. CONCLUSIONS:Using deep learning, we improved the synthetic FLAIR image quality by generating FLAIR images that have contrast closer to that of conventional FLAIR images and fewer granular and swelling artifacts, while preserving the lesion contrast.ABBREVIATIONS: cGAN ϭ conditional generative adversarial network; DL ϭ deep learning; GAN ϭ generative adversarial network; NRMSE ϭ normalized root mean square error; PSNR ϭ peak signal-to-noise ratio Indicates open access to non-subscribers at www.ajnr.org http://dx.

PurposeThis study was conducted in order to assess the intra- and interoperator reproducibility of shear-wave speed (SWS) measurement on elasticity phantoms and healthy volunteers using ultrasound-based point shear-wave elastography.Materials and methodsThis study was approved by the institutional review board. Two operators measured the SWS of five elasticity phantoms and seven organs (thyroid, lymph node, muscle, spleen, kidney, pancreas, and liver) of 30 healthy volunteers with 1.0–4.5 MHz convex (4C1) and 4.0–9.0 MHz linear (9L4) transducers. The phantom measurements were repeated ten times, while the volunteer measurements were performed five times each. Intra- and interoperator reproducibility was assessed. Interoperator reproducibility was also evaluated with the 95% Bland–Altman limits of agreement (LOA).ResultsIn phantoms, all intraclass correlation coefficients (ICCs) were above 0.90 and the 95% LOA between the two operators were less than ± 18%. In volunteers, intraoperator ICCs were > 0.75 for all regions except the pancreas. Interoperator ICC was above 0.75 for the right lobe of the liver (depth 4 cm) and the kidney, but the 95% LOA was less than ± 25% only for the liver.ConclusionAlthough excellent in phantoms, interoperator reproducibility was insufficient for all regions in the volunteers other than the right hepatic lobe at a depth of 4 cm. Clinicians should be aware of the 95% LOA when using SWS in patients.Key Points • Our phantom study indicated a high reproducibility for shear-wave speed (SWS) measurements with point shear-wave elastography (pSWE). • In volunteers, intraoperator reproducibility was generally high, but the interoperator reproducibility was not high enough except for the right hepatic lobe at 4 cm depth. • To evaluate interoperator reproducibility, the 95% limits of agreement (LOA) between operators should be considered in addition to the intraclass correlation coefficient (ICC). Electronic supplementary materialThe online version of this article (10.1007/s00330-019-06195-8) contains supplementary material, which is available to authorized users.

Despite important efforts to solve the clinico-radiological paradox, correlation between lesion load and physical disability in patients with multiple sclerosis remains modest. One hypothesis could be that lesion location in corticospinal tracts plays a key role in explaining motor impairment. In this study, we describe the distribution of lesions along the corticospinal tracts from the cortex to the cervical spinal cord in patients with various disease phenotypes and disability status. We also assess the link between lesion load and location within corticospinal tracts, and disability at baseline and 2-year follow-up. We retrospectively included 290 patients (22 clinically isolated syndrome, 198 relapsing remitting, 39 secondary progressive, 31 primary progressive multiple sclerosis) from eight sites. Lesions were segmented on both brain (T2-FLAIR or T2-weighted) and cervical (axial T2- or T2*-weighted) MRI scans. Data were processed using an automated and publicly available pipeline. Brain, brainstem and spinal cord portions of the corticospinal tracts were identified using probabilistic atlases to measure the lesion volume fraction. Lesion frequency maps were produced for each phenotype and disability scores assessed with Expanded Disability Status Scale score and pyramidal functional system score. Results show that lesions were not homogeneously distributed along the corticospinal tracts, with the highest lesion frequency in the corona radiata and between C2 and C4 vertebral levels. The lesion volume fraction in the corticospinal tracts was higher in secondary and primary progressive patients (mean = 3.6 ± 2.7% and 2.9 ± 2.4%), compared to relapsing-remitting patients (1.6 ± 2.1%, both P < 0.0001). Voxel-wise analyses confirmed that lesion frequency was higher in progressive compared to relapsing-remitting patients, with significant bilateral clusters in the spinal cord corticospinal tracts (P < 0.01). The baseline Expanded Disability Status Scale score was associated with lesion volume fraction within the brain (r = 0.31, P < 0.0001), brainstem (r = 0.45, P < 0.0001) and spinal cord (r = 0.57, P < 0.0001) corticospinal tracts. The spinal cord corticospinal tracts lesion volume fraction remained the strongest factor in the multiple linear regression model, independently from cord atrophy. Baseline spinal cord corticospinal tracts lesion volume fraction was also associated with disability progression at 2-year follow-up (P = 0.003). Our results suggest a cumulative effect of lesions within the corticospinal tracts along the brain, brainstem and spinal cord portions to explain physical disability in multiple sclerosis patients, with a predominant impact of intramedullary lesions.