722 results found
Pszczolkowski S, Pizarro L, O'Regan DP, et al., 2012, Gradient projection learning for parametric nonrigid registration, Pages: 226-233, ISSN: 0302-9743
A potentially large anatomical variability among subjects in a population makes nonrigid image registration techniques prone to inaccuracies and to high computational costs in their optimisation. In this paper, we propose a new learning-based approach to accelerate the convergence rate of any chosen parametric energy-based image registration method. From a set of training images and their corresponding deformations, our method learns offline a projection from the gradient space of the energy functional to the parameter space of the chosen registration method using partial least squares. Combined with a regularisation term, the learnt projection is subsequently used online to approximate the optimisation of the energy functional for unseen images. We employ the B-spline approach as underlying registration method, but other parametric methods can be used as well. We perform experiments on synthetic image data and MR cardiac sequences to show that our approach significantly accelerates the convergence -in number of iterations and total computational cost- of the chosen registration method, while achieving similar results in terms of accuracy. © 2012 Springer-Verlag.
Deligianni F, Varoquaux G, Thirion B, et al., 2012, Relating brain functional connectivity to anatomical connections: Model selection, Pages: 178-185, ISSN: 0302-9743
We aim to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity. Following , we formulate this problem as estimating a multivariate autoregressive (MAR) model with sparse linear regression. We introduce a model selection framework based on cross-validation. We select the appropriate sparsity of the connectivity matrices and demonstrate that choosing an ordering for the MAR that lends to sparser models is more appropriate than a random. Finally, we suggest randomized Least Absolute Shrinkage and Selective Operator (LASSO) in order to identify relevant anatomo-functional links with better recovery of ground truth. © 2012 Springer-Verlag.
Guerrero R, Donoghue CR, Pizarro L, et al., 2012, Learning correspondences in knee MR images from the osteoarthritis initiative, Pages: 218-225, ISSN: 0302-9743
Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration of images of the knee is challenging to achieve using intensity based registration algorithms. Problems arise due to large anatomical inter-subject differences which causes registrations to fail to converge to an accurate solution. In this work we propose learning correspondences in pairs of images to match self-similarity features, that describe images in terms of their local structure rather than their intensity. We use RANSAC as a robust model estimator. We show a substantial improvement in terms of mean error and standard deviation of 2.13mm and 2.47mm over intensity based registration methods, when comparing landmark alignment error. © 2012 Springer-Verlag.
Serag A, Gousias IS, Makropoulos A, et al., 2012, Unsupervised learning of shape complexity: Application to brain development, Pages: 88-99, ISSN: 0302-9743
This paper presents a framework for unsupervised learning of shape complexity in the developing brain. It learns the complexity in different brain structures by applying several shape complexity measures to each individual structure, and then using feature selection to select the measures that best describe the changes in complexity of each structure. Then, feature selection is applied again to assign a score to each structure, in order to find which structure can be a good biomarker of brain development. This study was carried out using T2-weighted MR images from 224 premature neonates (the age range at the time of scan was 26.7 to 44.86 weeks post-menstrual age). The advantage of the proposed framework is that one can extract as many ROIs as desired, and the framework automatically finds the ones which can be used as good biomarkers. However, the example application focuses on neonatal brain image data, the proposed framework for combining information from multiple measures may be applied more generally to other populations and other forms of imaging data. © 2012 Springer-Verlag.
van Rossum IA, Vos SJB, Burns L, et al., 2012, Injury markers predict time to dementia in subjects with MCI and amyloid pathology, NEUROLOGY, Vol: 79, Pages: 1809-1816, ISSN: 0028-3878
Vos S, van Rossum I, Burns L, et al., 2012, Test sequence of CSF and MRI biomarkers for prediction of AD in subjects with MCI, NEUROBIOLOGY OF AGING, Vol: 33, Pages: 2272-2281, ISSN: 0197-4580
Gousias IS, Edwards AD, Rutherford MA, et al., 2012, Magnetic resonance imaging of the newborn brain: Manual segmentation of labelled atlases in term-born and preterm infants, NEUROIMAGE, Vol: 62, Pages: 1499-1509, ISSN: 1053-8119
Donoghue CR, Rao A, Pizarro L, et al., 2012, Fast and accurate global geodesic registrations using knee MRI from the Osteoarthritis Initiative, Pages: 50-57, ISSN: 2160-7508
Registration is important for many applications in medical image analysis. Affine registration of knee MR images can suffer failures due to large anatomical and articulated pose variations. This work is demonstrated using 2743 MR images from the Osteoarthritis Initiative (OAI) public access dataset. With such large datasets any manual interventions to aid registration success are not feasible and so full automation with high accuracy is of paramount importance. Additionally, computing exhaustive pairwise registrations across the OAI dataset is very computationally expensive. We present a sparse geodesic registration method that increases accuracy of pairwise registration and also enables fast online computation of registration. We then propose two novel methods to reduce registration error over the graph. Firstly we use all precomputed transformations to infer transformation errors for each edge, through assuming global registration cycle consistency across a sparse graph. In conjunction with this, we suggest fusing multiple successful registrations as a strategy to mitigate small errors in each transformation in the graph. It is shown that, in combination, these techniques achieve more accurate pairwise registration results than both geodesic registration and direct pairwise registration. This paper addresses accuracy of registrations, speed of online computation and is demonstrated on a large scale dataset. © 2012 IEEE.
Frangi A, Santos A, Ober R, et al., 2012, Welcome
Donoghue CR, Rao A, Bull AMJ, et al., 2012, Robust global registration through geodesic paths on an empirical manifold with knee MRI from the Osteoarthritis Initiative (OAI), Pages: 1-10, ISSN: 0302-9743
Accurate affine registrations are crucial for many applications in medical image analysis. Within the Osteoarthritis Initiative (OAI) dataset we have observed a failure rate of approximately 4% for direct affine registrations of knee MRI without manual initialisation. Despite this, the problem of robust affine registration has not received much attention in recent years. With the increase in large medical image datasets, manual intervention is not a suitable solution to achieve successful affine registrations. We introduce a framework to improve the robustness of affine registrations without prior manual initialisations. We use 10,307 MR images from the large dataset available from the OAI to model the low dimensional manifold of the population of unregistered knee MRIs as a sparse k-nearest-neighbour graph. Affine registrations are computed in advance for nearest neighbours only. When a pairwise image registration is required the shortest path across the graph is extracted to find a geodesic path on the empirical manifold. The precomputed affine transformations on this path are composed to find an estimated transformation. Finally a refinement step is used to further improve registration accuracy. Failure rates of geodesic affine registrations reduce to 0.86% with the registration framework proposed. © 2012 Springer-Verlag.
Dawant BM, Christensen GE, Fitzpatrick JM, et al., 2012, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, ISBN: 9783642313394
O'Regan DP, Shi W, Ariff B, et al., 2012, Remodeling after acute myocardial infarction: mapping ventricular dilatation using three dimensional CMR image registration, Journal of Cardiovascular Magnetic Resonance, Vol: 14, ISSN: 1532-429X
Background: Progressive heart failure due to remodeling is a major cause of morbidity and mortality followingmyocardial infarction. Conventional clinical imaging measures global volume changes, and currently there is nomeans of assessing regional myocardial dilatation in relation to ischemic burden. Here we use 3D co-registration ofCardiovascular Magnetic Resonance (CMR) images to assess the long-term effects of ischemia-reperfusion injury onleft ventricular structure after acute ST-elevation myocardial infarction (STEMI).Methods: Forty six patients (age range 33–77 years) underwent CMR imaging within 7 days following primarypercutaneous coronary intervention (PPCI) for acute STEMI with follow-up at one year. Functional cine imaging andLate Gadolinium Enhancement (LGE) were segmented and co-registered. Local left ventricular wall dilatation wasassessed by using intensity-based similarities to track the structural changes in the heart between baseline andfollow-up. Results are expressed as means, standard errors and 95% confidence interval (CI) of the difference.Results: Local left ventricular remodeling within infarcted myocardium was greater than in non-infarctedmyocardium (1.6% ± 1.0 vs 0.3% ± 0.9, 95% CI: -2.4% – -0.2%, P = 0.02). One-way ANOVA revealed that transmuralinfarct thickness had a significant effect on the degree of local remodeling at one year (P < 0.0001) with greatestwall dilatation observed when infarct transmurality exceeded 50%. Infarct remodeling was more severe whenmicrovascular obstruction (MVO) was present (3.8% ± 1.3 vs −1.6% ± 1.4, 95% CI: -9.1% – -1.5%, P = 0.007) and whenend-diastolic volume had increased by >20% (4.8% ± 1.4 vs −0.15% ± 1.2, 95% CI: -8.9% – -0.9%, P = 0.017).Conclusions: The severity of ischemic injury has a significant effect on local ventricular wall remodeling with onlymodest dilatation observed within non-ischemic myocardium. Limitatio
Shi W, Zhuang X, Wang H, et al., 2012, A Comprehensive Cardiac Motion Estimation Framework Using Both Untagged and 3-D Tagged MR Images Based on Nonrigid Registration, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 31, Pages: 1263-1275, ISSN: 0278-0062
Duckett SG, Shi W, Zhuang X, et al., 2012, CARDIAC MRI: UNDERSTANDING MYOCARDIAL MOTION TO PREDICT REMODELLING PRE CARDIAC RESYNCHRONISATION THERAPY, Annual Conference of the British-Cardiovascular-Society (BCS)
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
Malcolme-Lawes L, Juli C, Karim R, et al., 2012, AUTOMATED ANALYSIS OF ATRIAL DELAYED ENHANCEMENT CARDIAC MRI CORRELATES WITH VOLTAGE, AF RECURRENCE POST-ABLATION, AND HIGH STROKE RISK, Annual Conference of the British-Cardiovascular-Society (BCS)
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
Hu M, Penney G, Figl M, et al., 2012, Reconstruction of a 3D surface from video that is robust to missing data and outliers: Application to minimally invasive surgery using stereo and mono endoscopes, MEDICAL IMAGE ANALYSIS, Vol: 16, Pages: 597-611, ISSN: 1361-8415
Dhutia NM, Cole GD, Willson K, et al., 2012, A new automated system to identify a consistent sampling position to make tissue Doppler and transmitral Doppler measurements of E, E ' and E/E ', INTERNATIONAL JOURNAL OF CARDIOLOGY, Vol: 155, Pages: 394-399, ISSN: 0167-5273
Wang H, Shi W, Zhuang X, et al., 2012, Automatic cardiac motion tracking using both untagged and 3D tagged MR images, Pages: 45-54, ISSN: 0302-9743
We present a fully automatic framework for cardiac motion tracking based on non-rigid image registration for the analysis of myocardial motion using both untagged and 3D tagged MR images. We detect and track anatomical landmarks in the heart and combine this with intensity-based motion tracking to allow accurately model cardiac motion while significantly reduce the computational complexity. A collaborative similarity measure simultaneously computed in three LA views is employed to register a sequence of images taken during the cardiac cycle to a reference image taken at end-diastole. We then integrate a valve plane tracker into the framework which uses short-axis and long-axis untagged MR images as well as 3D tagged images to estimate a fully four-dimensional motion field of the left ventricle. © 2012 Springer-Verlag.
Shi W, Zhuang X, Wolz R, et al., 2012, A multi-image graph cut approach for cardiac image segmentation and uncertainty estimation, Pages: 178-187, ISSN: 0302-9743
Registration and segmentation uncertainty may be important information to convey to a user when automatic image analysis is performed. Uncertainty information may be used to provide additional diagnostic information to traditional analysis of cardiac function. In this paper, we develop a framework for the automatic segmentation of the cardiac anatomy from multiple MR images. We also define the registration and segmentation uncertainty and explore its use for diagnostic purposes. Our framework uses cardiac MR image sequences that are widely available in clinical practice. We improve the performance of the cardiac segmentation algorithms by combining information from multiple MR images using a graph-cut based segmentation. We evaluate this framework on images from 32 subjects: 13 patients with ischemic cardiomyopathy, 14 patients with dilated cardiomyopathy and 5 normal volunteers. Our results indicate that the proposed method is capable of producing segmentation results with very high robustness and high accuracy with minimal user interaction across all subject groups. We also show that registration and segmentation uncertainties are good indicators for segmentation failures as well as good predictors for the functional abnormality of the subject. © 2012 Springer-Verlag.
Karim R, Arujuna A, Brazier A, et al., 2012, Validation of a novel method for the automatic segmentation of left atrial scar from delayed-enhancement magnetic resonance, Pages: 254-262, ISSN: 0302-9743
Delayed-enhancement magnetic resonance imaging is an effective technique for imaging left atrial (LA) scars both pre- and post- radio-frequency ablation for the treatment of atrial fibrillation. Existing techniques for LA scar segmentation require expert manual interaction, making them tedious and prone to high observer variability. In this paper, a novel automatic segmentation algorithm for segmenting LA scar was validated using digital phantoms and clinical data from 11 patients. The performance of the approach was compared to the two leading semi-automatic techniques and the ground truth of manual segmentations by 2 expert observers. The novel approach was shown to be accurate in terms of Dice coefficient, robust to typical image intensity variability, and much faster in terms of execution time. © 2012 Springer-Verlag.
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
Vounou M, Janousova E, Wolz R, et al., 2012, Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease, NEUROIMAGE, Vol: 60, Pages: 700-716, ISSN: 1053-8119
van der Lijn F, Verhaaren BFJ, Ikram MA, et al., 2012, Automated measurement of local white matter lesion volume, NEUROIMAGE, Vol: 59, Pages: 3901-3908, 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
Pszczolkowski S, Pizarro L, Guerrero R, et al., 2012, Nonrigid Free-Form Registration Using Landmark-Based Statistical Deformation Models, Conference on Medical Imaging - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Shi W, Zhuang X, Pizarro L, et al., 2012, Registration using sparse free-form deformations, Pages: 659-666, ISSN: 0302-9743
© Springer-Verlag Berlin Heidelberg 2012. Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.
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