Fast Predictive Image Registration

We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a 1500x/66x speed up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D

Dealing with blast loading on brickwork

Much effort is being deployed in maintaining electricity generation from the UK's fleet of nuclear power stations with safety being a prime consideration. Up-to-date safety standards demand resistance to hazards which normally include seismic and blast loading, yet many stations were built before such considerations became mandatory. To extend the generating life of older nuclear power stations, a demonstration of conformity to modern safety standards is now required. This paper will discuss the structural problems associated with assessing and strengthening masonry panels subjected to blast loadings as generated by a postulated accident of hot gas release from fracture of a reactor pressure vessels cooling circuit. The paper will provide background on masonry strength and assessment techniques, including the use of dynamic amplification which permits static design principles to be used under dynamic loading conditions. An account of the strengthening options and the problems associated with their implementation will be presented

Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms

We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context. For simplicity, we choose the kernel Fischer Linear Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters in an Expectation-Maximization framework, we define model fidelity via the hinge loss of the decision function. The resulting algorithm optimizes the parameters of the LDDMM norm-inducing differential operator as a solution to a group-wise registration and classification problem. In practice, this may lead to a biology-aware registration, focusing its attention on the predictive task at hand such as identifying the effects of disease. We first tested our algorithm on a synthetic dataset, showing that our parameter selection improves registration quality and classification accuracy. We then tested the algorithm on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our Schizophrenia-Control predictive model showed significant improvement in ROC AUC compared to baseline parameters

Unsupervised Deformable Image Registration Using Cycle-Consistent CNN

Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to the classical approaches. In this paper, we present a novel unsupervised medical image registration method that trains deep neural network for deformable registration of 3D volumes using a cycle-consistency. Thanks to the cycle consistency, the proposed deep neural networks can take diverse pair of image data with severe deformation for accurate registration. Experimental results using multiphase liver CT images demonstrate that our method provides very precise 3D image registration within a few seconds, resulting in more accurate cancer size estimation.Comment: accepted for MICCAI 201

Comparison of diffusion tensor imaging by cardiovascular magnetic resonance and gadolinium enhanced 3D image intensity approaches to investigation of structural anisotropy in explanted rat hearts

Background: Cardiovascular magnetic resonance (CMR) can through the two methods 3D FLASH and diffusion tensor imaging (DTI) give complementary information on the local orientations of cardiomyocytes and their laminar arrays. Methods: Eight explanted rat hearts were perfused with Gd-DTPA contrast agent and fixative and imaged in a 9.4T magnet by two types of acquisition: 3D fast low angle shot (FLASH) imaging, voxels 50 × 50 × 50 μm, and 3D spin echo DTI with monopolar diffusion gradients of 3.6 ms duration at 11.5 ms separation, voxels 200 × 200 × 200 μm. The sensitivity of each approach to imaging parameters was explored. Results:The FLASH data showed laminar alignments of voxels with high signal, in keeping with the presumed predominance of contrast in the interstices between sheetlets. It was analysed, using structure-tensor (ST) analysis, to determine the most (v 1 ST ), intermediate (v 2 ST ) and least (v 3 ST ) extended orthogonal directions of signal continuity. The DTI data was analysed to determine the most (e 1 DTI ), intermediate (e 2 DTI ) and least (e 3 DTI ) orthogonal eigenvectors of extent of diffusion. The correspondence between the FLASH and DTI methods was measured and appraised. The most extended direction of FLASH signal (v 1 ST ) agreed well with that of diffusion (e 1 DTI ) throughout the left ventricle (representative discrepancy in the septum of 13.3 ± 6.7°: median ± absolute deviation) and both were in keeping with the expected local orientations of the long-axis of cardiomyocytes. However, the orientation of the least directions of FLASH signal continuity (v 3 ST ) and diffusion (e 3 ST ) showed greater discrepancies of up to 27.9 ± 17.4°. Both FLASH (v 3 ST ) and DTI (e 3 DTI ) where compared to directly measured laminar arrays in the FLASH images. For FLASH the discrepancy between the structure-tensor calculated v 3 ST and the directly measured FLASH laminar array normal was of 9 ± 7° for the lateral wall and 7 ± 9° for the septum (median ± inter quartile range), and for DTI the discrepancy between the calculated v 3 DTI and the directly measured FLASH laminar array normal was 22 ± 14° and 61 ± 53.4°. DTI was relatively insensitive to the number of diffusion directions and to time up to 72 hours post fixation, but was moderately affected by b-value (which was scaled by modifying diffusion gradient pulse strength with fixed gradient pulse separation). Optimal DTI parameters were b = 1000 mm/s2 and 12 diffusion directions. FLASH acquisitions were relatively insensitive to the image processing parameters explored. Conclusions: We show that ST analysis of FLASH is a useful and accurate tool in the measurement of cardiac microstructure. While both FLASH and the DTI approaches appear promising for mapping of the alignments of myocytes throughout myocardium, marked discrepancies between the cross myocyte anisotropies deduced from each method call for consideration of their respective limitations

Enhanced expressions of microvascular smooth muscle receptors after focal cerebral ischemia occur via the MAPK MEK/ERK pathway

<p>Abstract</p> <p>Background</p> <p>MEK1/2 is a serine/threonine protein that phosphorylates extracellular signal-regulated kinase (ERK1/2). Cerebral ischemia results in enhanced expression of cerebrovascular contractile receptors in the middle cerebral artery (MCA) leading to the ischemic region. Here we explored the role of the MEK/ERK pathway in receptor expression following ischemic brain injury using the specific MEK1 inhibitor U0126.</p> <p>Methods and result</p> <p>Rats were subjected to a 2-h middle cerebral artery occlusion (MCAO) followed by reperfusion for 48-h and the ischemic area was calculated. The expression of phosphorylated ERK1/2 and Elk-1, and of endothelin ET_Aand ET_B, angiotensin AT₁, and 5-hydroxytryptamine 5-HT_1Breceptors were analyzed with immunohistochemistry using confocal microscopy in cerebral arteries, microvessels and in brain tissue. The expression of endothelin ET_Breceptor was analyzed by quantitative Western blot. We demonstrate that there is an increase in the number of contractile smooth muscle receptors in the MCA and in micro- vessels within the ischemic region. The enhanced expression occurs in the smooth muscle cells as verified by co-localization studies. This receptor upregulation is furthermore associated with enhanced expression of pERK1/2 and of transcription factor pElk-1 in the vascular smooth muscle cells. Blockade of transcription with the MEK1 inhibitor U0126, given at the onset of reperfusion or as late as 6 hours after the insult, reduced transcription (pERK1/2 and pElk-1), the enhanced vascular receptor expression, and attenuated the cerebral infarct and improved neurology score.</p> <p>Conclusion</p> <p>Our results show that MCAO results in upregulation of cerebrovascular ET_B, AT₁and 5-HT_1Breceptors. Blockade of this event with a MEK1 inhibitor as late as 6 h after the insult reduced the enhanced vascular receptor expression and the associated cerebral infarction.</p

Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation

Brain volume measurements extracted from structural MRI data sets are a widely accepted neuroimaging biomarker to study mouse models of neurodegeneration. Whether to acquire and analyze data in vivo or ex vivo is a crucial decision during the phase of experimental designs, as well as data analysis. In this work, we extracted the brain structures for both longitudinal in vivo and single-time-point ex vivo MRI acquired from the same animals using accurate automatic multi-atlas structural parcellation, and compared the corresponding statistical and classification analysis. We found that most gray matter structures volumes decrease from in vivo to ex vivo, while most white matter structures volume increase. The level of structural volume change also varies between different genetic strains and treatment. In addition, we showed superior statistical and classification power of ex vivo data compared to the in vivo data, even after resampled to the same level of resolution. We further demonstrated that the classification power of the in vivo data can be improved by incorporating longitudinal information, which is not possible for ex vivo data. In conclusion, this paper demonstrates the tissue-specific changes, as well as the difference in statistical and classification power, between the volumetric analysis based on the in vivo and ex vivo structural MRI data. Our results emphasize the importance of longitudinal analysis for in vivo data analysis

SVF-Net: Learning Deformable Image Registration Using Shape Matching

International audienceIn this paper, we propose an innovative approach for registration based on the deterministic prediction of the parameters from both images instead of the optimization of a energy criteria. The method relies on a fully convolutional network whose architecture consists of contracting layers to detect relevant features and a symmetric expanding path that matches them together and outputs the transformation parametriza-tion. Whereas convolutional networks have seen a widespread expansion and have been already applied to many medical imaging problems such as segmentation and classification, its application to registration has so far faced the challenge of defining ground truth data on which to train the algorithm. Here, we present a novel training strategy to build reference deformations which relies on the registration of segmented regions of interest. We apply this methodology to the problem of inter-patient heart registration and show an important improvement over a state of the art optimization based algorithm. Not only our method is more accurate but it is also faster-registration of two 3D-images taking less than 30ms second on a GPU-and more robust to outliers

Self-Supervised Discovery of Anatomical Shape Landmarks

Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discovers the landmarks corresponding to anatomical shape features that promote good image registration in the context of a particular class of transformations. In addition, we also propose a regularization for the proposed network which allows for a uniform distribution of these discovered landmarks. In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis. We evaluate the performance on a phantom dataset as well as 2D and 3D images.Comment: Early accept at MICCAI 202

Deep Group-wise Variational Diffeomorphic Image Registration

Deep neural networks are increasingly used for pair-wise image registration. We propose to extend current learning-based image registration to allow simultaneous registration of multiple images. To achieve this, we build upon the pair-wise variational and diffeomorphic VoxelMorph approach and present a general mathematical framework that enables both registration of multiple images to their geodesic average and registration in which any of the available images can be used as a fixed image. In addition, we provide a likelihood based on normalized mutual information, a well-known image similarity metric in registration, between multiple images, and a prior that allows for explicit control over the viscous fluid energy to effectively regularize deformations. We trained and evaluated our approach using intra-patient registration of breast MRI and Thoracic 4DCT exams acquired over multiple time points. Comparison with Elastix and VoxelMorph demonstrates competitive quantitative performance of the proposed method in terms of image similarity and reference landmark distances at significantly faster registration