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In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early or distinct disease processes. The role of MRI-observed DAWM is understudied due to a lack of automated assessment methods. Supervised deep learning (DL) methods are highly capable in this domain, but require large sets of labeled data. To overcome this challenge, a DL-based network (DAWM-Net) was trained using semi-supervised learning on a limited set of labeled data for segmentation of DAWM, focal lesions, and normal-appearing brain tissues on multiparametric MRI. DAWM-Net segmentation performance was compared to a previous intensity thresholding-based method on an independent test set from expert consensus (N = 25). Segmentation overlap by Dice Similarity Coefficient (DSC) and Spearman correlation of DAWM volumes were assessed. DAWM-Net showed DSC > 0.93 for normal-appearing brain tissues and DSC > 0.81 for focal lesions. For DAWM-Net, the DAWM DSC was 0.49 ± 0.12 with a moderate volume correlation (ρ = 0.52, p < 0.01). The previous method showed lower DAWM DSC of 0.26 ± 0.08 and lacked a significant volume correlation (ρ = 0.23, p = 0.27). These results demonstrate the feasibility of DL-based DAWM auto-segmentation with semi-supervised learning. This tool may facilitate future investigation of the role of DAWM in MS. [ABSTRACT FROM AUTHOR]

Gadolinium‐based contrast agents (CAs) were synthesized using faujasite zeolite (NaX) and zeolite beta (BEA) and their performances in vitro and in vivo were compared to the widely used commercial CA, gadoteric acid (Gd‐DOTA). Magnetic resonance imaging (MRI) relaxometry studies (considering longitudinal [T1] and transverse [T2] relaxation times) were performed using Gd‐DOTA and the zeolitic materials loaded with Gd3+. The Gd‐loaded NaX, which presented large pores and cavities (7.35 and 11.24 Å, respectively), exhibited relaxivity values of around 52 mM−1 s−1, while BEA, which presented smaller pore and cavity diameters (5.95 and 6.68 Å, respectively) showed lower relaxivity values of ~4.8 mM−1 s−1. The effect of the Gd‐loaded NaX as MRI CA was tested in vivo in Sprague–Dawley rats, employing a 7 T scanner, with comparison to Gd‐DOTA MRI angiography. The relaxivity measurements showed that the Gd‐loaded NaX (50 mM−1 s−1) provided better image contrast than Gd‐DOTA (5.1 mM−1 s−1). Clearance studies of the CAs using urine and blood showed that both Gd‐loaded NaX and Gd‐DOTA were eliminated from the body after 2 days, demonstrating the potential of Gd‐loaded NaX for use as an MRI CA. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients. Methods: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing–remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume. Results: The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size. Conclusion: Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets. [ABSTRACT FROM AUTHOR]

Tract-based spatial statistics (TBSS) of diffusion tensor imaging (DTI) studies have consistently shown diminished white matter (WM) integrity for individuals with cocaine use disorder (CUD). The present study used seed-based d mapping (SDM) to determine the extent to which a systematic difference in the WM integrity of cocaine users may exist (as compared with that of healthy controls). Articles from 2006 (when TBSS was first developed) to present were reviewed, with eight selected for inclusion. Meta-analysis found lower fractional anisotropy (FA) in the genu of the corpus callosum for cocaine users, with a small-to-moderate peak effect size (Hedge's g = -0.331). Sensitivity analyses mostly supported the robustness of the obtained difference. Differences detected at exploratory thresholds for significance suggested insult to WM integrity extending beyond the corpus callosum. The present results compliment a previous region-of-interest (ROI)-based meta-analysis of DTI studies in individuals with CUD. These findings have significant implications for the potential role of neuroprotective agents in the treatment of CUD and merit additional iteration as more studies accrue in the literature. [ABSTRACT FROM AUTHOR]