Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical Image Datasets

Hierarchical Data Integration With Gaussian Processes: Application to the Characterization of Cardiac Ischemia-Reperfusion Patterns

Cardiac imaging protocols usually result in several types of acquisitions and descriptors extracted from the images. The statistical analysis of such data across a population may be challenging, and can be addressed by fusion techniques within a dimensionality reduction framework. However, directly combining different data types may lead to unfair comparisons (for heterogeneous descriptors) or over-exploitation of information (for strongly correlated modalities). In contrast, physicians progressively consider each type of data based on hierarchies derived from their experience or evidence-based recommendations, an inspiring approach for data fusion strategies. In this paper, we propose a novel methodology for hierarchical data fusion and unsupervised representation learning. It mimics the physicians' approach by progressively integrating different high-dimensional data descriptors according to a known hierarchy. We model this hierarchy with a Hierarchical Gaussian Process Latent Variable Model (GP-LVM), which links the estimated low-dimensional latent representation and high-dimensional observations at each level in the hierarchy, with additional links between consecutive levels of the hierarchy. We demonstrate the relevance of this approach on a dataset of 1726 magnetic resonance image slices from 123 patients revascularized after acute myocardial infarction (MI) (first level in the hierarchy), some of them undergoing reperfusion injury (microvascular obstruction (MVO), second level in the hierarchy). Our experiments demonstrate that our hierarchical model provides consistent data organization across levels of the hierarchy and according to physiological characteristics of the lesions. This allows more relevant statistical analysis of myocardial lesion patterns, and in particular subtle lesions such as MVO.

Embedding-Alignment Fusion-Based Graph Convolution Network With Mixed Learning Strategy for 4D Medical Image Reconstruction

In recent years, 4D medical image involving structural and motion information of tissue has attracted increasing attention. The key to the 4D image reconstruction is to stack the 2D slices based on matching the aligned motion states. In this study, the distribution of the 2D slices with the different motion states is modeled as a manifold graph, and the reconstruction is turned to be the graph alignment. An embedding-alignment fusion-based graph convolution network (GCN) with a mixed-learning strategy is proposed to align the graphs. Herein, the embedding and alignment processes of graphs interact with each other to realize a precise alignment with retaining the manifold distribution. The mixed strategy of self- and semi-supervised learning makes the alignment sparse to avoid the mismatching caused by outliers in the graph. In the experiment, the proposed 4D reconstruction approach is validated on the different modalities including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). We evaluate the reconstruction accuracy and compare it with those of state-of-the-art methods. The experiment results demonstrate that our approach can reconstruct a more accurate 4D image.

Manifold-based respiratory phase estimation enables motion and distortion correction of free-breathing cardiac diffusion tensor MRI

PURPOSE

For in vivo cardiac DTI, breathing motion and B₀ field inhomogeneities produce misalignment and geometric distortion in diffusion-weighted (DW) images acquired with conventional single-shot EPI. We propose using a dimensionality reduction method to retrospectively estimate the respiratory phase of DW images and facilitate both distortion correction (DisCo) and motion compensation.

METHODS

Free-breathing electrocardiogram-triggered whole left-ventricular cardiac DTI using a second-order motion-compensated spin echo EPI sequence and alternating directionality of phase encoding blips was performed on 11 healthy volunteers. The respiratory phase of each DW image was estimated after projecting the DW images into a 2D space with Laplacian eigenmaps. DisCo and motion compensation were applied to the respiratory sorted DW images. The results were compared against conventional breath-held T₂ half-Fourier single shot turbo spin echo. Cardiac DTI parameters including fractional anisotropy, mean diffusivity, and helix angle transmurality were compared with and without DisCo.

RESULTS

The left-ventricular geometries after DisCo and motion compensation resulted in significantly improved alignment of DW images with T₂ reference. DisCo reduced the distance between the left-ventricular contours by 13.2% ± 19.2%, P < .05 (2.0 ± 0.4 for DisCo and 2.4 ± 0.5 mm for uncorrected). DisCo DTI parameter maps yielded no significant differences (mean diffusivity: 1.55 ± 0.13 × 10^-3 mm² /s and 1.53 ± 0.13 × 10^-3 mm² /s, P = .09; fractional anisotropy: 0.375 ± 0.041 and 0.379 ± 0.045, P = .11; helix angle transmurality: 1.00% ± 0.10°/% and 0.99% ± 0.12°/%, P = .44), although the orientation of individual tensors differed.

CONCLUSION

Retrospective respiratory phase estimation with LE-based DisCo and motion compensation in free-breathing cardiac DTI resulting in significantly reduced geometric distortion and improved alignment within and across slices.

Characterizing interactions between cardiac shape and deformation by non-linear manifold learning

In clinical routine, high-dimensional descriptors of the cardiac function such as shape and deformation are reduced to scalars (e.g. volumes or ejection fraction), which limit the characterization of complex diseases. Besides, these descriptors undergo interactions depending on disease, which may bias their computational analysis. In this paper, we aim at characterizing such interactions by unsupervised manifold learning. We propose to use a sparsified version of Multiple Manifold Learning to align the latent spaces encoding each descriptor and weighting the strength of the alignment depending on each pair of samples. While this framework was up to now only applied to link different datasets from the same manifold, we demonstrate its relevance to characterize the interactions between different but partially related descriptors of the cardiac function (shape and deformation). We benchmark our approach against linear and non-linear embedding strategies, among which the fusion of manifolds by Multiple Kernel Learning, the independent embedding of each descriptor by Diffusion Maps, and a strict alignment based on pairwise correspondences. We first evaluated the methods on a synthetic dataset from a 0D cardiac model where the interactions between descriptors are fully controlled. Then, we transfered them to a population of right ventricular meshes from 310 subjects (100 healthy and 210 patients with right ventricular disease) obtained from 3D echocardiography, where the link between shape and deformation is key for disease understanding. Our experiments underline the relevance of jointly considering shape and deformation descriptors, and that manifold alignment is preferable over fusion for our application. They also confirm at a finer scale the characteristic traits of the right ventricular diseases in our population.

A minimally interactive method for labeling respiratory phases in free-breathing thoracic dynamic MRI for constructing 4D images

OBJECTIVE

Determination of end-expiration (EE) and end-inspiration (EI) time points in the respiratory cycle in free-breathing slice image acquisitions of the thorax is one key step needed for 4D image construction via dynamic magnetic resonance imaging. The purpose of this paper is to realize the automation of the labeling process.

METHODS

The diaphragm is used as a surrogate for tracking respiratory motion and determining the state of breathing. Regions of interest (ROIs) containing the hemi-diaphragms are set by human interaction to compute the optical flow matrix between two adjacent 2D time slices. Subsequently, our approach examines the diaphragm speed and direction and by considering the change in the optical flow matrix, the EE or EI points are detected.

RESULTS AND CONCLUSION

The labeling accuracy for the lateral aspect of the left lung and the lateral aspect of the right lung (0.630.71) is significantly lower (P < 0.05) than the accuracy for other positions (0.420.44), but the error in almost all scenarios is less than 1 time point. By comparing between automatic and manual labeling in 12 scenarios, we found out that 9 scenarios showed no significant difference (P > 0.05) between two methods. Overall, our method is found to be highly agreeable with manual labeling and greatly shortens the labeling time, requiring less than 8 minutes/ study compared to 4 hours/ study for manual labeling.

SIGNIFICANCE

Our method achieves automatic labeling of EE and EI points without the need for use of patient internal or external markers.

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.