The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.

BACKGROUND AND PURPOSE: Head motion causes image degradation in brain MR imaging examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MR imaging acquisitions. MATERIALS AND METHODS:We prospectively acquired brain MR imaging at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted TSE, and FLAIR in 32 unsedated children, including 7 with epilepsy (age range, 2-18 years). We implemented a novel motion correction technique through a modification of k-space data acquisition: Distributed and Incoherent Sample Orders for Reconstruction Deblurring by using Encoding Redundancy (DISORDER). For each participant and technique, we obtained 3 reconstructions as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: gradient entropy (GE) and MPRAGE white matter (WM) homogeneity. As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability.RESULTS: Both image quality metrics improved after motion correction for all modalities, and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z ¼ À3.164 for MPRAGE; z ¼ À2.066 for TSE; z ¼ À2.645 for FLAIR; all P , .05). CONCLUSIONS:Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least 1 sequence was improved by this approach, indicating the benefit of this technique in unsedated children for both clinical and research environments. ABBREVIATIONS: DISORDER ¼ Distributed and Incoherent Sample Orders for Reconstruction Deblurring by using Encoding Redundancy; Aq ¼ acquired; Di ¼ after DISORDER motion correction; DiOut ¼ Di with additional outlier rejection; GE ¼ gradient entropy H ead motion is a common cause of image degradation in brain MR imaging. Motion artifacts negatively impact MR image quality and therefore radiologists' capacity to read the images, ultimately affecting patient clinical care. 1 Motion artifacts are more common in noncompliant patients, 2 but even in compliant adults, intrascan movement is reported in at least 10% of cases. 3 For children who require high-resolution MR images, obtaining optimal image quality can be challenging, owing to the

Ultra-low field (ULF) point-of-care MRI systems allow image acquisition without interrupting medical provision, with neonatal clinical care being an important potential application. The ability to measure neonatal brain tissue T1 is a key enabling technology for subsequent structural image contrast optimisation, as well as being a potential biomarker for brain development. Here we describe an optimised strategy for neonatal T1 mapping at ULF. Methods:Examinations were performed on a 64mT portable MRI system. A phantom validation experiment was performed, and total of thirty-three in-vivo exams were acquired from twentyeight neonates with postmenstrual age ranging 31 +4 to 49 +0 weeks. Multiple inversion-recovery turbo spin echo sequences were acquired with differing inversion and repetition times. An analysis pipeline incorporating inter-sequence motion correction generated proton density and T1 maps. Regions of interest were placed in the cerebral deep grey matter, frontal white matter and cerebellum. Weighted linear regression was used to predict T1 as a function of postmenstrual age. Results:Reduction of T1 with postmenstrual age is observed in all measured brain tissue; the change in T1 per week and 95% confidence intervals is given by dT1=-21ms/week [-25, -16] (cerebellum), dT1=-14ms/week [-18, -10] (deep grey matter), and dT1=-35ms/week [-45, -25] (white matter). Conclusion:Neonatal T1 values at ULF are shorter than those previously described at standard clinical field strengths, but longer than those of adults at ULF. T1 reduces with postmenstrual age and is thus a candidate biomarker for perinatal brain development.

Background and Purpose: Head motion causes image degradation in brain MRI examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MRI acquisitions. Material and Methods: We prospectively acquired brain MRI at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted Turbo Spin Echo and FLAIR, in 32 unsedated children, including 7 with epilepsy (age range 2-18 years). We implemented a novel motion correction technique: Distributed and Incoherent Sample Orders for Reconstruction Deblurring using Encoding Redundancy (DISORDER). For each subject and modality, we obtained 3 reconstructions: as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: Gradient Entropy (GE) and MPRAGE White Matter Homogeneity (WM-H). As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability. Results: Both image quality metrics improved after motion correction for all modalities and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z=-3.164 MPRAGE, z=-2.066 TSE, z=-2.645 FLAIR, for all p<0.05). Conclusions: Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least one sequence was improved by this approach, indicating the benefit of this technique in un-sedated children for both clinical and research environments.

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