Imperial College London

DrPaulAljabar

Faculty of EngineeringDepartment of Computing

Honorary Research Fellow
 
 
 
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Contact

 

paul.aljabar Website

 
 
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Location

 

344Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

82 results found

Salvan P, Tournier JD, Batalle D, Falconer S, Chew A, Kennea N, Aljabar P, Dehaene-Lambertz G, Arichi T, Edwards AD, Counsell SJet 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.

Journal article

Kersbergen KJ, Makropoulos A, Aljabar P, Groenendaal F, de Vries LS, Counsell SJ, Benders MJNLet al., 2016, Longitudinal Regional Brain Development and Clinical Risk Factors in Extremely Preterm Infants, JOURNAL OF PEDIATRICS, Vol: 178, Pages: 93-+, ISSN: 0022-3476

Journal article

Makropoulos A, Aljabar P, Wright R, H√ľning B, Merchant N, Arichi T, Tusor N, Hajnal JV, Edwards AD, Counsell SJ, Rueckert Det 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.

Journal article

Zimmer V, Glocker B, Aljabar P, Counsell S, Rutherford M, Edwards AD, Hajnal J, Gonzales Ballester MA, Rueckert D, Piella Get 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.

Conference paper

Heckemann RA, Ledig C, Gray KR, Aljabar P, Rueckert D, Hajnal JV, Hammers Aet 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.

Journal article

Rueckert D, Wright R, Makropoulos A, Kyriakopoulou V, Patkee P, Koch L, Rutherford M, Hajnal J, Aljabar Pet 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.

Journal article

Kainz B, Steinberger M, Wein W, Murgasova M, Malamateniou C, Keraudren K, Aljabar P, Rutherford M, Hajnal J, Rueckert Det al., 2015, Fast Volume Reconstruction from Motion Corrupted Stacks of 2D Slices, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

Journal article

Schuh A, Murgasova M, Makropoulos A, Ledig C, Counsell SJ, Hajnal JV, Aljabar P, Rueckert Det 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

Conference paper

Eckersley RJ, Christensen-Jeffries K, Tang MX, Hajnal JV, Aljabar P, Dunsby Cet al., 2015, Super-resolution imaging of microbubble contrast agents, IEEE International Ultrasonics Symposium (IUS), Publisher: IEEE, ISSN: 1948-5719

Conference paper

Ferrazzi G, Murgasova MK, Arichi T, Malamateniou C, Fox MJ, Makropoulos A, Allsop J, Rutherford M, Malik S, Aljabar P, Hajnal JVet 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

Journal article

Makropoulos A, Gousias IS, Ledig C, Aljabar P, Serag A, Hajnal JV, Edwards AD, Counsell SJ, Rueckert Det 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

Journal article

Boardman JP, Walley A, Ball G, Takousis P, Krishnan ML, Hughes-Carre L, Aljabar P, Serag A, King C, Merchant N, Srinivasan L, Froguel P, Hajnal J, Rueckert D, Counsell S, Edwards ADet al., 2014, Common Genetic Variants and Risk of Brain Injury After Preterm Birth, PEDIATRICS, Vol: 133, Pages: E1655-E1663, ISSN: 0031-4005

Journal article

Wright R, Kyriakopoulou V, Ledig C, Rutherford MA, Hajnal JV, Rueckert D, Aljabar Pet al., 2014, Automatic quantification of normal cortical folding patterns from fetal brain MRI, NEUROIMAGE, Vol: 91, Pages: 21-32, ISSN: 1053-8119

Journal article

Koch LM, Wright R, Vatansever D, Kyriakopoulou V, Malamateniou C, Patkee PA, Rutherford M, Hajnal JV, Aljabar P, Rueckert Det 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

Conference paper

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)

Poster

Ball G, Aljabar P, Zebari S, Tusor N, Arichi T, Merchant N, Robinson EC, Ogundipe E, Rueckert D, Edwards AD, otherset al., 2014, Rich-club organization of the newborn human brain, Proceedings of the National Academy of Sciences, Vol: 111, Pages: 7456-7461

Journal article

Pandit AS, Robinson E, Aljabar P, Ball G, Gousias IS, Wang Z, Hajnal JV, Rueckert D, Counsell SJ, Montana G, otherset 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

Journal article

Ball G, Srinivasan L, Aljabar P, Counsell SJ, Durighel G, Hajnal JV, Rutherford MA, Edwards ADet 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

Journal article

Ball G, Boardman JP, Aljabar P, Pandit A, Arichi T, Merchant N, Rueckert D, Edwards AD, Counsell SJet al., 2013, The influence of preterm birth on the developing thalamocortical connectome, CORTEX, Vol: 49, Pages: 1711-1721, ISSN: 0010-9452

Journal article

Shi W, Jantsch M, Aljabar P, Pizarro L, Bai W, Wang H, O'Regan D, Zhuang X, Rueckert Det al., 2013, Temporal sparse free-form deformations, Medical Image Analysis

Journal article

Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert Det al., 2013, Random forest-based similarity measures for multi-modal classification of Alzheimer's disease, NEUROIMAGE, Vol: 65, Pages: 167-175, ISSN: 1053-8119

Journal article

Schirmer M, Ball G, Counsell SJ, Edwards AD, Rueckert D, Hajnal JV, Aljabar Pet 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

Conference paper

Serag A, Aljabar P, Ball G, Counsell SJ, Boardman JP, Rutherford MA, Edwards AD, Hajnal JV, Rueckert Det 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

Journal article

Wolz R, Aljabar P, Hajnal JV, Lotjonen J, Rueckert Det al., 2012, Nonlinear dimensionality reduction combining MR imaging with non-imaging information, MEDICAL IMAGE ANALYSIS, Vol: 16, Pages: 819-830, ISSN: 1361-8415

Journal article

Ball G, Boardman JP, Rueckert D, Aljabar P, Arichi T, Merchant N, Gousias IS, Edwards AD, Counsell SJet al., 2012, The Effect of Preterm Birth on Thalamic and Cortical Development, CEREBRAL CORTEX, Vol: 22, Pages: 1016-1024, ISSN: 1047-3211

Journal article

Keihaninejad S, Heckemann RA, Gousias IS, Hajnal JV, Duncan JS, Aljabar P, Rueckert D, Hammers Aet 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

Journal article

Gray KR, Wolz R, Heckemann RA, Aljabar P, Hammers A, Rueckert Det 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

Journal article

Serag A, Aljabar P, Ball G, Counsel SJ, Boardman JP, Rutherford MA, Edwards AD, Hajnal JV, Rueckert Det 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

Journal article

Ledig C, Wolz R, Aljabar P, Lotjonen J, Heckemann RA, Hammers A, Rueckert Det 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

Conference paper

Serag A, Aljabar P, Counsell S, Boardman J, Hajnal JV, Rueckert Det 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

Conference paper

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