BACKGROUND AND PURPOSE: Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learningbased reconstruction in pediatric neuroimaging and to investigate the impact of deep learning-based reconstruction on image quality and quantitative values in synthetic MR imaging. MATERIALS AND METHODS:This study included 47 children 2.3-14.7 years of age who underwent both standard and accelerated synthetic MR imaging at 3T. The accelerated synthetic MR imaging was reconstructed using a deep learning pipeline. The image quality, lesion detectability, tissue values, and brain volumetry were compared among accelerated deep learning and accelerated and standard synthetic data sets. RESULTS:The use of deep learning-based reconstruction in the accelerated synthetic scans significantly improved image quality for all contrast weightings (P , .001), resulting in image quality comparable with or superior to that of standard scans. There was no significant difference in lesion detectability between the accelerated deep learning and standard scans (P . .05). The tissue values and brain tissue volumes obtained with accelerated deep learning and the other 2 scans showed excellent agreement and a strong linear relationship (all, R 2 . 0.9). The difference in quantitative values of accelerated scans versus accelerated deep learning scans was very small (tissue values, ,0.5%; volumetry, À1.46%-0.83%). CONCLUSIONS:The use of deep learning-based reconstruction in synthetic MR imaging can reduce scan time by 42% while maintaining image quality and lesion detectability and providing consistent quantitative values. The accelerated deep learning synthetic MR imaging can replace standard synthetic MR imaging in both contrast-weighted and quantitative imaging.

Objective: Recent studies highlighted the triple-network model which illustrated the interactions among three large-scale networks including salience network (SN). The functional magnetic resonance imaging used in this study was designed to investigate the characteristics of three large-scale networks associated with the thought-action fusion (TAF) in patients with obsessive-compulsive disorder (OCD) using power spectral density (PSD) analysis. Methods: This study included 32 OCD patients and 38 age-matched healthy controls (HC). The TAF task was modified from the experiment of Rassin. PSD from time courses in large-scale networks of each subject was measured to compare between the groups for both TAF and resting state. Results: In SN, OCD reported lower power in the low-frequency domain of SN compared to HC using the two-sample t test during the TAF task (t = −2.395, p = 0.019) but not in the resting state. The PSD in the low-frequency domain of the SN had a significant negative correlation with state score in the guilty inventory (r = −0.361, p = 0.042) in OCD patients. Conclusion: This study suggests that OCD patients showed reduced SN power which can be prominent in a certain situation, such as TAF. In addition, the PSD alterations in SN cause difficulty in processing ambiguous emotional cues in social situations, and the difficulty can be connected with a negative feeling (e.g., guilt).

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Background IBD often affects women during their reproductive years; however, the effect of pregnancy on disease course remains poorly understood. We aimed to assess intestinal inflammation in IBD patients compared with controls as measured by faecal calprotectin (FC). We also investigated whether maternal IBD diagnosis was associated with altered FC in the offspring and if there were particular bacterial taxa that correlated with FC levels. Methods Pregnant women with or without IBD and their infants were prospectively enrolled in the MECONIUM study during 2015–2018 years. FC levels at each trimester of pregnancy and in babies throughout the first 3 years of life were measured using a quantitative enzyme immunoassay (CALPRO AS, Norway). Multivariate regression analysis was applied to investigate FC levels. Stool microbiota composition in the maternal and baby stool was assessed using 16s rRNA sequencing. Results 617 faecal samples from 342 mothers (91 IBD, 251 control) and 1005 faecal samples from 288 infants (born to 76 mothers with and 212 without IBD) were analysed. FC levels in pregnant women with IBD were significantly higher than in control pregnant women regardless of IBD type (Crohn’s disease vs. ulcerative colitis). In IBD mothers FC levels showed a decline during pregnancy (µg/g, median (interquartile range); first trimester 130.8 (102.3–147.3) vs. third trimester 85.9 (44.5–125.4), p = 0.02) (Figure 1). Patients with flare at stool collection had the highest, while those treated with anti-TNF plus thiopurine showed the lowest FC at each trimester. Babies born to mothers with IBD presented higher FC levels than those born to control mothers at multiple time points between 2 and 36 months of age (Figure 2A); the median FC levels were the highest after 1 year up to 3 years in those babies whose mothers presented active disease during pregnancy (Figure 2B). Microbial α-diversity showed an inverse relationship with FC in both mothers (r = −0.25, p = 0.032) and babies (r = −0.31, p = 0.0035). Blautia and Streptococcus abundance were positively correlated with FC in babies. Conclusion FC levels in IBD patients decreased throughout pregnancy. Maternal IBD, lower microbiome diversity and the abundance of certain microbial genera were associated with higher FC in the offspring up to 3 years of life. These findings suggest a potential favourable impact of pregnancy on IBD activity and highlight a possible effect of IBD during pregnancy on the intestinal inflammation in offspring, which could be mediated through altered microbiome.