82 results found
Salvan P, Tournier JD, Batalle D, et al., 2017, Language ability in preterm children is associated with arcuate fasciculi microstructure at term, Human Brain Mapping, Vol: 38, Pages: 3836-3847, ISSN: 1065-9471
In the mature human brain, the arcuate fasciculus mediates verbal working memory, word learning, and sublexical speech repetition. However, its contribution to early language acquisition remains unclear. In this work, we aimed to evaluate the role of the direct segments of the arcuate fasciculi in the early acquisition of linguistic function. We imaged a cohort of 43 preterm born infants (median age at birth of 30 gestational weeks; median age at scan of 42 postmenstrual weeks) using high b value high-angular resolution diffusion-weighted neuroimaging and assessed their linguistic performance at 2 years of age. Using constrained spherical deconvolution tractography, we virtually dissected the arcuate fasciculi and measured fractional anisotropy (FA) as a metric of white matter development. We found that term equivalent FA of the left and right arcuate fasciculi was significantly associated with individual differences in linguistic and cognitive abilities in early childhood, independent of the degree of prematurity. These findings suggest that differences in arcuate fasciculi microstructure at the time of normal birth have a significant impact on language development and modulate the first stages of language learning.
Kersbergen KJ, Makropoulos A, Aljabar P, et al., 2016, Longitudinal Regional Brain Development and Clinical Risk Factors in Extremely Preterm Infants, JOURNAL OF PEDIATRICS, Vol: 178, Pages: 93-+, ISSN: 0022-3476
Makropoulos A, Aljabar P, Wright R, et al., 2015, Regional growth and atlasing of the developing human brain, Neuroimage, Vol: 125, Pages: 456-478, ISSN: 1095-9572
Detailed morphometric analysis of the neonatal brain is required to characterise brain development and define neuroimaging biomarkers related to impaired brain growth. Accurate automatic segmentation of neonatal brain MRI is a prerequisite to analyse large datasets. We have previously presented an accurate and robust automatic segmentation technique for parcellating the neonatal brain into multiple cortical and subcortical regions. In this study, we further extend our segmentation method to detect cortical sulci and provide a detailed delineation of the cortical ribbon. These detailed segmentations are used to build a 4-dimensional spatio-temporal structural atlas of the brain for 82 cortical and subcortical structures throughout this developmental period. We employ the algorithm to segment an extensive database of 420 MR images of the developing brain, from 27 to 45. weeks post-menstrual age at imaging. Regional volumetric and cortical surface measurements are derived and used to investigate brain growth and development during this critical period and to assess the impact of immaturity at birth. Whole brain volume, the absolute volume of all structures studied, cortical curvature and cortical surface area increased with increasing age at scan. Relative volumes of cortical grey matter, cerebellum and cerebrospinal fluid increased with age at scan, while relative volumes of white matter, ventricles, brainstem and basal ganglia and thalami decreased. Preterm infants at term had smaller whole brain volumes, reduced regional white matter and cortical and subcortical grey matter volumes, and reduced cortical surface area compared with term born controls, while ventricular volume was greater in the preterm group. Increasing prematurity at birth was associated with a reduction in total and regional white matter, cortical and subcortical grey matter volume, an increase in ventricular volume, and reduced cortical surface area.
Zimmer V, Glocker B, Aljabar P, et al., 2015, Learning and combining image similarities for neonatal brain population studies, International Workshop on Machine Learning in Medical Imaging (MLMI), Publisher: Springer International Publishing, Pages: 110-117, ISSN: 0302-9743
The characterization of neurodevelopment is challenging due to the complex structural changes of the brain in early childhood. To analyze the changes in a population across time and to relate them with clinical information, manifold learning techniques can be applied. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure in the embedding and highly application dependent. It has been shown that the combination of several notions of similarity and features can improve the new representation. However, how to combine and weight different similarites and features is non-trivial. In this work, we propose to learn the neighborhood structure and similarity measure used for manifold learning through Neighborhood Approximation Forests (NAFs). The recently proposed NAFs learn a neighborhood structure in a dataset based on a user-defined distance. A characterization of image similarity using NAFs enables us to construct manifold representations based on a previously defined criterion to improve predictions regarding structural and clinical information. In particular, NAFs can be used naturally to combine the affinities learned from multiple distances in a joint manifold towards a more meaningful representation and an improved characterization of the resulting embedding. We demonstrate the utility of NAFs in manifold learning on a population of preterm and in term neonates for classification regarding structural volume and clinical information.
Kainz B, Steinberger M, Wein W, et al., 2015, Fast volume reconstruction from motion corrupted stacks of 2D slices, IEEE Transactions on Medical Imaging, Vol: 34, Pages: 1901-1913, ISSN: 0278-0062
Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available.
Heckemann RA, Ledig C, Gray KR, et al., 2015, Brain Extraction Using Label Propagation and Group Agreement: Pincram, PLOS One, Vol: 10, ISSN: 1932-6203
Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisitefor many neuroimaging methods. Most existing methods exhibit disadvantages inthat they are laborious, yield inconsistent results, and/or require training data to closelymatch the data to be processed. Here, we present pincram, an automatic, versatile methodfor accurately labelling the adult brain on T1-weighted 3D MR head images. The methoduses an iterative refinement approach to propagate labels from multiple atlases to a giventarget image using image registration. At each refinement level, a consensus label is generated.At the subsequent level, the search for the brain boundary is constrained to the neighbourhoodof the boundary of this consensus label. The method achieves high accuracy(Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of >0.97) and performs better than many state-of-the-art methods as evidenced by independentevaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, theprogram generates the "success index," a scalar metadatum indicative of the accuracy ofthe output label. Pincram is available as open source software.
Rueckert D, Wright R, Makropoulos A, et al., 2015, Construction of a fetal spatio-temporal cortical surface atlas from in utero MRI: application of spectral surface matching, Neuroimage, Vol: 120, Pages: 467-480, ISSN: 1095-9572
In this study, we construct a spatio-temporal surface atlas of the developing cerebral cortex, which is an important tool for analysing and understanding normal and abnormal cortical development. In utero Magnetic Resonance Imaging (MRI) of 80 healthy foetuses was performed, with a gestational age range of 21.7 to 38.9 weeks. Topologically correct cortical surface models were extracted from reconstructed 3D MRI volumes. Accurate correspondences were obtained by applying a joint spectral analysis to cortices for sets of subjects close to a specific age. Sulcal alignment was found to be accurate in comparison to spherical demons, a state of the art registration technique for aligning 2D cortical representations (average Fréchet distance ≈ 0.4 mm at 30 weeks). We construct consistent, unbiased average cortical surface templates, for each week of gestation, from age-matched groups of surfaces by applying kernel regression in the spectral domain. These were found to accurately capture the average cortical shape of individuals within the cohort, suggesting a good alignment of cortical geometry. Each spectral embedding and its corresponding cortical surface template provide a dual reference space where cortical geometry is aligned and a vertex-wise morphometric analysis can be undertaken.
Schuh A, Murgasova M, Makropoulos A, et al., 2015, Construction of a 4D Brain Atlas and Growth Model Using Diffeomorphic Registration, 3rd International Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data (STIA), Publisher: SPRINGER-VERLAG BERLIN, Pages: 27-37, ISSN: 0302-9743
Eckersley RJ, Christensen-Jeffries K, Tang MX, et al., 2015, Super-resolution imaging of microbubble contrast agents, IEEE International Ultrasonics Symposium (IUS), Publisher: IEEE, ISSN: 1948-5719
Ferrazzi G, Murgasova MK, Arichi T, et al., 2014, Resting State fMRI in the moving fetus: A robust framework for motion, bias field and spin history correction, NEUROIMAGE, Vol: 101, Pages: 555-568, ISSN: 1053-8119
Makropoulos A, Gousias IS, Ledig C, et al., 2014, Automatic whole brain MRI segmentation of the developing neonatal brain, IEEE Transactions on Medical Imaging, Vol: 33, Pages: 1818-1831, ISSN: 0278-0062
Magnetic resonance (MR) imaging is increasingly being used to assess brain growth and development in infants. Such studies are often based on quantitative analysis of anatomical segmentations of brain MR images. However, the large changes in brain shape and appearance associated with development, the lower signal to noise ratio and partial volume effects in the neonatal brain present challenges for automatic segmentation of neonatal MR imaging data. In this study, we propose a framework for accurate intensity-based segmentation of the developing neonatal brain, from the early preterm period to term-equivalent age, into 50 brain regions. We present a novel segmentation algorithm that models the intensities across the whole brain by introducing a structural hierarchy and anatomical constraints. The proposed method is compared to standard atlas-based techniques and improves label overlaps with respect to manual reference segmentations. We demonstrate that the proposed technique achieves highly accurate results and is very robust across a wide range of gestational ages, from 24 weeks gestational age to term-equivalent age.
Boardman JP, Walley A, Ball G, et al., 2014, Common Genetic Variants and Risk of Brain Injury After Preterm Birth, PEDIATRICS, Vol: 133, Pages: E1655-E1663, ISSN: 0031-4005
Wright R, Kyriakopoulou V, Ledig C, et al., 2014, Automatic quantification of normal cortical folding patterns from fetal brain MRI, NEUROIMAGE, Vol: 91, Pages: 21-32, ISSN: 1053-8119
Ball G, Aljabar P, Zebari S, et al., 2014, Rich-club organization of the newborn human brain, Proceedings of the National Academy of Sciences, Vol: 111, Pages: 7456-7461
Pandit AS, Robinson E, Aljabar P, et al., 2014, Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth, Cerebral Cortex, Vol: 24, Pages: 2324-2333
Koch LM, Wright R, Vatansever D, et al., 2014, Graph-Based Label Propagation in Fetal Brain MR Images, 5th International Workshop on Machine Learning in Medical Imaging (MLMI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 9-16, ISSN: 0302-9743
Rueckert D, Wolz R, Aljabar P, 2014, Machine learning meets medical imaging: Learning and discovery of clinically useful information from images, 4th Eccomas Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE)
Ball G, Srinivasan L, Aljabar P, et al., 2013, Development of cortical microstructure in the preterm human brain, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 110, Pages: 9541-9546, ISSN: 0027-8424
Ball G, Boardman JP, Aljabar P, et al., 2013, The influence of preterm birth on the developing thalamocortical connectome, CORTEX, Vol: 49, Pages: 1711-1721, ISSN: 0010-9452
Gray KR, Aljabar P, Heckemann RA, et al., 2013, Random forest-based similarity measures for multi-modal classification of Alzheimer's disease, NEUROIMAGE, Vol: 65, Pages: 167-175, ISSN: 1053-8119
Schirmer M, Ball G, Counsell SJ, et al., 2013, Normalisation of Neonatal Brain Network Measures Using Stochastic Approaches, 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 574-581, ISSN: 0302-9743
Serag A, Aljabar P, Ball G, et al., 2012, Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (vol 59, pg 2255, 2012), NEUROIMAGE, Vol: 63, Pages: 998-998, ISSN: 1053-8119
Wolz R, Aljabar P, Hajnal JV, et al., 2012, Nonlinear dimensionality reduction combining MR imaging with non-imaging information, MEDICAL IMAGE ANALYSIS, Vol: 16, Pages: 819-830, ISSN: 1361-8415
Ball G, Boardman JP, Rueckert D, et al., 2012, The Effect of Preterm Birth on Thalamic and Cortical Development, CEREBRAL CORTEX, Vol: 22, Pages: 1016-1024, ISSN: 1047-3211
Keihaninejad S, Heckemann RA, Gousias IS, et al., 2012, Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation, PLOS ONE, Vol: 7, ISSN: 1932-6203
Gray KR, Wolz R, Heckemann RA, et al., 2012, Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease, NEUROIMAGE, Vol: 60, Pages: 221-229, ISSN: 1053-8119
Serag A, Aljabar P, Ball G, et al., 2012, Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression, NEUROIMAGE, Vol: 59, Pages: 2255-2265, ISSN: 1053-8119
Ledig C, Wolz R, Aljabar P, et al., 2012, MULTI-CLASS BRAIN SEGMENTATION USING ATLAS PROPAGATION AND EM-BASED REFINEMENT, 9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 896-899
Serag A, Aljabar P, Counsell S, et al., 2012, LISA: LONGITUDINAL IMAGE REGISTRATION VIA SPATIO-TEMPORAL ATLASES, 9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 334-337
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