BIVENTRICULAR FINITE ELEMENT MODELING AND QUANTIFICATION OF 3D LANGRAGIAN STRAINS AND TORSION USING DENSE MRI

Statistical data suggests that increased use of evidence-based medical therapies has largely contributed to the decrease in American death rate caused by heart disease. And my studies are about two applications of magnetic resonance imaging (MRI) as a non-invasive approach in evidence-based health care research. In my first study, the achievement of a pulmonary valve replacement surgery was assessed on a patient with tetralogy of Fallot (TOF). In order to evaluate the remodeling of right ventricle, two biventricular finite element models were built up for pre-surgical images and post-surgical images. In my second study, 3D Lagrangian strains and torsion in the left ventricle of ten rats were investigated using Displacement ENcoding with Stimulated Echoes (DENSE) cardiac magnetic resonance (CMR) images. Tools written in MATLAB were developed for 2D contouring, 3D modeling, strain and torsion computations, and statistical comparison across subjects

Stratified decision forests for accurate anatomical landmark localization in cardiac images

Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy

Automatic initialization and quality control of large-scale cardiac MRI segmentations

Continuous advances in imaging technologies enable ever more comprehensive phenotyping of human anatomy and physiology. Concomitant reduction of imaging costs has resulted in widespread use of imaging in large clinical trials and population imaging studies. Magnetic Resonance Imaging (MRI), in particular, offers one-stop-shop multidimensional biomarkers of cardiovascular physiology and pathology. A wide range of analysis methods offer sophisticated cardiac image assessment and quantification for clinical and research studies. However, most methods have only been evaluated on relatively small databases often not accessible for open and fair benchmarking. Consequently, published performance indices are not directly comparable across studies and their translation and scalability to large clinical trials or population imaging cohorts is uncertain. Most existing techniques still rely on considerable manual intervention for the initialization and quality control of the segmentation process, becoming prohibitive when dealing with thousands of images. The contributions of this paper are three-fold. First, we propose a fully automatic method for initializing cardiac MRI segmentation, by using image features and random forests regression to predict an initial position of the heart and key anatomical landmarks in an MRI volume. In processing a full imaging database, the technique predicts the optimal corrective displacements and positions in relation to the initial rough intersections of the long and short axis images. Second, we introduce for the first time a quality control measure capable of identifying incorrect cardiac segmentations with no visual assessment. The method uses statistical, pattern and fractal descriptors in a random forest classifier to detect failures to be corrected or removed from subsequent statistical analysis. Finally, we validate these new techniques within a full pipeline for cardiac segmentation applicable to large-scale cardiac MRI databases. The results obtained based on over 1200 cases from the Cardiac Atlas Project show the promise of fully automatic initialization and quality control for population studies

Automatic segmentation of wall structures from cardiac images

One important topic in medical image analysis is segmenting wall structures from different cardiac medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). This task is typically done by radiologists either manually or semi-automatically, which is a very time-consuming process. To reduce the laborious human efforts, automatic methods have become popular in this research. In this thesis, features insensitive to data variations are explored to segment the ventricles from CT images and extract the left atrium from MR images. As applications, the segmentation results are used to facilitate cardiac disease analysis. Specifically, 1. An automatic method is proposed to extract the ventricles from CT images by integrating surface decomposition with contour evolution techniques. In particular, the ventricles are first identified on a surface extracted from patient-specific image data. Then, the contour evolution is employed to refine the identified ventricles. The proposed method is robust to variations of ventricle shapes, volume coverages, and image quality. 2. A variational region-growing method is proposed to segment the left atrium from MR images. Because of the localized property of this formulation, the proposed method is insensitive to data variabilities that are hard to handle by globalized methods. 3. In applications, a geometrical computational framework is proposed to estimate the myocardial mass at risk caused by stenoses. In addition, the segmentation of the left atrium is used to identify scars for MR images of post-ablation.PhDCommittee Chair: Yezzi, Anthony; Committee Co-Chair: Tannenbaum, Allen; Committee Member: Egerstedt, Magnus ; Committee Member: Fedele, Francesco ; Committee Member: Stillman, Arthur; Committee Member: Vela,Patrici

Investigation and Validation of Imaging Techniques for Mitral Valve Disease Diagnosis and Intervention

Mitral Valve Disease (MVD) describes a variety of pathologies that result in regurgitation of blood during the systolic phase of the cardiac cycle. Decisions in valvular disease management rely heavily on non-invasive imaging. Transesophageal echocardiography (TEE) is widely recognized as the key evaluation technique where backflow of high velocity blood can be visualized under Doppler. In most cases, TEE imaging is adequate for identifying mitral valve pathology, though the modality is often limited from signal dropout, artifacts and a restricted field of view. Quantitative analysis is an integral part of the overall assessment of valve morphology and gives objective evidence for both classification and guiding intervention of regurgitation. In addition, patient-specific models derived from diagnostic TEE images allow clinicians to gain insight into uniquely intricate anatomy prior to surgery. However, the heavy reliance on TEE segmentation for diagnosis and modelling has necessitated an evaluation of the accuracy of the oft-used mitral valve imaging modality. Dynamic cardiac 4D-Computed Tomography (4D-CT) is emerging as a valuable tool for diagnosis, quantification and assessment of cardiac diseases. This modality has the potential to provide a high quality rendering of the mitral valve and subvalvular apparatus, to provide a more complete picture of the underlying morphology. However, application of dynamic CT to mitral valve imaging is especially challenging due to the large and rapid motion of the valve leaflets. It is therefore necessary to investigate the accuracy and level of precision by which dynamic CT captures mitral valve motion throughout the cardiac cycle. To do this, we design and construct a silicone and bovine quasi-static mitral valve phantom which can simulate a range of ECG-gated heart rates and reproduce physiologic valve motion over the cardiac cycle. In this study, we discovered that the dynamic CT accurately captures the underlying valve movement, but with a higher prevalence of image artifacts as leaflet and chordae motion increases due to elevated heart rates. In a subsequent study, we acquire simultaneous CT and TEE images of both a silicone mitral valve phantom and an iodine-stained bovine mitral valve. We propose a pipeline to use CT as the ground truth to study the relationship between TEE intensities and the underlying valve morphology. Preliminary results demonstrate that with an optimized threshold selection based solely on TEE pixel intensities, only 40\% of pixels are correctly classified as part of the valve. In addition, we have shown that emphasizing the centre-line rather than the boundaries of high intensity TEE image regions provides a better representation and segmentation of the valve morphology. This work has the potential to inform and augment the use of TEE for diagnosis and modelling of the mitral valve in the clinical workflow for MVD

Dehazing Ultrasound using Diffusion Models

Echocardiography has been a prominent tool for the diagnosis of cardiac disease. However, these diagnoses can be heavily impeded by poor image quality. Acoustic clutter emerges due to multipath reflections imposed by layers of skin, subcutaneous fat, and intercostal muscle between the transducer and heart. As a result, haze and other noise artifacts pose a real challenge to cardiac ultrasound imaging. In many cases, especially with difficult-to-image patients such as patients with obesity, a diagnosis from B-Mode ultrasound imaging is effectively rendered unusable, forcing sonographers to resort to contrast-enhanced ultrasound examinations or refer patients to other imaging modalities. Tissue harmonic imaging has been a popular approach to combat haze, but in severe cases is still heavily impacted by haze. Alternatively, denoising algorithms are typically unable to remove highly structured and correlated noise, such as haze. It remains a challenge to accurately describe the statistical properties of structured haze, and develop an inference method to subsequently remove it. Diffusion models have emerged as powerful generative models and have shown their effectiveness in a variety of inverse problems. In this work, we present a joint posterior sampling framework that combines two separate diffusion models to model the distribution of both clean ultrasound and haze in an unsupervised manner. Furthermore, we demonstrate techniques for effectively training diffusion models on radio-frequency ultrasound data and highlight the advantages over image data. Experiments on both \emph{in-vitro} and \emph{in-vivo} cardiac datasets show that the proposed dehazing method effectively removes haze while preserving signals from weakly reflected tissue.Comment: 10 pages, 11 figures, preprint IEEE submissio

S-Net: a multiple cross aggregation convolutional architecture for automatic segmentation of small/thin structures for cardiovascular applications

With the success of U-Net or its variants in automatic medical image segmentation, building a fully convolutional network (FCN) based on an encoder-decoder structure has become an effective end-to-end learning approach. However, the intrinsic property of FCNs is that as the encoder deepens, higher-level features are learned, and the receptive field size of the network increases, which results in unsatisfactory performance for detecting low-level small/thin structures such as atrial walls and small arteries. To address this issue, we propose to keep the different encoding layer features at their original sizes to constrain the receptive field from increasing as the network goes deeper. Accordingly, we develop a novel S-shaped multiple cross-aggregation segmentation architecture named S-Net, which has two branches in the encoding stage, i.e., a resampling branch to capture low-level fine-grained details and thin/small structures and a downsampling branch to learn high-level discriminative knowledge. In particular, these two branches learn complementary features by residual cross-aggregation; the fusion of the complementary features from different decoding layers can be effectively accomplished through lateral connections. Meanwhile, we perform supervised prediction at all decoding layers to incorporate coarse-level features with high semantic meaning and fine-level features with high localization capability to detect multi-scale structures, especially for small/thin volumes fully. To validate the effectiveness of our S-Net, we conducted extensive experiments on the segmentation of cardiac wall and intracranial aneurysm (IA) vasculature, and quantitative and qualitative evaluations demonstrated the superior performance of our method for predicting small/thin structures in medical images

Machine learning approaches to model cardiac shape in large-scale imaging studies

Recent improvements in non-invasive imaging, together with the introduction of fully-automated segmentation algorithms and big data analytics, has paved the way for large-scale population-based imaging studies. These studies promise to increase our understanding of a large number of medical conditions, including cardiovascular diseases. However, analysis of cardiac shape in such studies is often limited to simple morphometric indices, ignoring large part of the information available in medical images. Discovery of new biomarkers by machine learning has recently gained traction, but often lacks interpretability. The research presented in this thesis aimed at developing novel explainable machine learning and computational methods capable of better summarizing shape variability, to better inform association and predictive clinical models in large-scale imaging studies. A powerful and flexible framework to model the relationship between three-dimensional (3D) cardiac atlases, encoding multiple phenotypic traits, and genetic variables is first presented. The proposed approach enables the detection of regional phenotype-genotype associations that would be otherwise neglected by conventional association analysis. Three learning-based systems based on deep generative models are then proposed. In the first model, I propose a classifier of cardiac shapes which exploits task-specific generative shape features, and it is designed to enable the visualisation of the anatomical effect these features encode in 3D, making the classification task transparent. The second approach models a database of anatomical shapes via a hierarchy of conditional latent variables and it is capable of detecting, quantifying and visualising onto a template shape the most discriminative anatomical features that characterize distinct clinical conditions. Finally, a preliminary analysis of a deep learning system capable of reconstructing 3D high-resolution cardiac segmentations from a sparse set of 2D views segmentations is reported. This thesis demonstrates that machine learning approaches can facilitate high-throughput analysis of normal and pathological anatomy and of its determinants without losing clinical interpretability.Open Acces