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    Hampshire A, Daws RE, Das Neves I, Soreq E, Sandrone S, Violante IRet al., 2019,

    Probing cortical and sub-cortical contributions to instruction-based learning: Regional specialisation and global network dynamics

    , NEUROIMAGE, Vol: 192, Pages: 88-100, ISSN: 1053-8119
    Cox DJ, Bai W, Price AN, Edwards AD, Rueckert D, Groves AMet al., 2019,

    Ventricular remodeling in preterm infants: computational cardiac magnetic resonance atlasing shows significant early remodeling of the left ventricle.

    , Pediatr Res, Vol: 85, Pages: 807-815

    BACKGROUND: Premature birth is associated with ventricular remodeling, early heart failure, and altered left ventricular (LV) response to physiological stress. Using computational cardiac magnetic resonance (CMR) imaging, we aimed to quantify preterm ventricular remodeling in the neonatal period, and explore contributory clinical factors. METHODS: Seventy-three CMR scans (34 preterm infants, 10 term controls) were performed to assess in-utero development and preterm ex-utero growth. End-diastolic computational atlases were created for both cardiac ventricles; t statistics, linear regression modeling, and principal component analysis (PCA) were used to describe the impact of prematurity and perinatal factors on ventricular volumetrics, ventricular geometry, myocardial mass, and wall thickness. RESULTS: All preterm neonates demonstrated greater weight-indexed LV mass and higher weight-indexed end-diastolic volume at term-corrected age (P < 0.05 for all preterm gestations). Independent associations of increased term-corrected age LV myocardial wall thickness were (false discovery rate <0.05): degree of prematurity, antenatal glucocorticoid administration, and requirement for >48 h postnatal respiratory support. PCA of LV geometry showed statistical differences between all preterm infants at term-corrected age and term controls. CONCLUSIONS: Computational CMR demonstrates that significant LV remodeling occurs soon after preterm delivery and is associated with definable clinical situations. This suggests that neonatal interventions could reduce long-term cardiac dysfunction.

    Sandrone S, Moreno-Zambrano D, Kipnis J, van Gijn Jet al., 2019,

    A (delayed) history of the brain lymphatic system.

    , Nat Med, Vol: 25, Pages: 538-540
    Robinson R, Valindria VV, Bai W, Oktay O, Kainz B, Suzuki H, Sanghvi MM, Aung N, Paiva JÉM, Zemrak F, Fung K, Lukaschuk E, Lee AM, Carapella V, Kim YJ, Piechnik SK, Neubauer S, Petersen SE, Page C, Matthews PM, Rueckert D, Glocker Bet al., 2019,

    Automated quality control in image segmentation: application to the UK Biobank cardiac MR imaging study

    , Journal of Cardiovascular Magnetic Resonance, Vol: 21, ISSN: 1097-6647

    Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

    Liow N, Montaldo P, Lally PJ, Teiserskas J, Bassett P, Oliveira V, Mendoza J, Slater R, Shankaran S, Thayyil Set al., 2019,

    Preemptive Morphine During Therapeutic Hypothermia After Neonatal Encephalopathy: A Secondary Analysis.

    , Ther Hypothermia Temp Manag

    Although therapeutic hypothermia (TH) improves outcomes after neonatal encephalopathy (NE), the safety and efficacy of preemptive opioid sedation during cooling therapy is unclear. We performed a secondary analysis of the data from a large multicountry prospective observational study (Magnetic Resonance Biomarkers in Neonatal Encephalopathy [MARBLE]) to examine the association of preemptive morphine infusion during TH on brain injury and neurodevelopmental outcomes after NE. All recruited infants had 3.0 Tesla magnetic resonance imaging and spectroscopy at 1 week, and neurodevelopmental outcome assessments at 22 months. Of 223 babies recruited to the MARBLE study, the data on sedation were available from 169 babies with moderate (n = 150) or severe NE (n = 19). Although the baseline characteristics and admission status were similar, the babies who received morphine infusion (n = 141) were more hypotensive (49% vs. 25%, p = 0.02) and had a significantly longer hospital stay (12 days vs. 9 days, p = 0.009) than those who did not (n = 28). Basal ganglia/thalamic injury (score ≥1) and cortical injury (score ≥1) was seen in 34/141 (24%) and 37/141 (26%), respectively, of the morphine group and 4/28 (14%) and 3/28 (11%) of the nonmorphine group (p > 0.05). On regression modeling adjusted for potential confounders, preemptive morphine was not associated with mean (standard deviation [SD]) thalamic N-acetylaspartate (NAA) concentration (6.9 ± 0.9 vs. 6.5 ± 1.5; p = 0.97), and median (interquartile range) lactate/NAA peak area ratios (0.16 [0.12-0.21] vs. 0.13 [0.11-0.18]; p = 0.20) at 1 week, and mean (SD) Bayley-III composite motor (92 ± 23 vs. 94 ± 10; p = 0.98), language (89 ± 22 vs. 93 ± 8; p 

    Soreq E, Leech R, Hampshire A, 2019,

    Dynamic network coding of working-memory domains and working-memory processes

    , NATURE COMMUNICATIONS, Vol: 10, ISSN: 2041-1723
    Li LM, Violante IR, Leech R, Ross E, Hampshire A, Opitz A, Rothwell JC, Carmichael DW, Sharp DJet al., 2019,

    Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation

    , HUMAN BRAIN MAPPING, Vol: 40, Pages: 904-915, ISSN: 1065-9471
    Gilbert K, Bai W, Mauger C, Medrano-Gracia P, Suinesiaputra A, Lee AM, Sanghvi MM, Aung N, Piechnik SK, Neubauer S, Petersen SE, Rueckert D, Young AAet al., 2019,

    Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study

    , SCIENTIFIC REPORTS, Vol: 9, ISSN: 2045-2322
    Duan J, Bello G, Schlemper J, Bai W, Dawes TJW, Biffi C, de Marvao A, Doumou G, O'Regan DP, Rueckert Det al., 2019,

    Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach.

    , IEEE Trans Med Imaging

    Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the presence of artefacts in input CMR volumes.

    Sandrone S, Berthaud JV, Chuquilin M, Cios J, Ghosh P, Gottlieb-Smith RJ, Kushlaf H, Mantri S, Masangkay N, Menkes DL, Nevel KS, Sarva H, Schneider LDet al., 2019,

    Neurologic and neuroscience education Mitigating neurophobia to mentor health care providers

    , NEUROLOGY, Vol: 92, Pages: 174-179, ISSN: 0028-3878

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