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In the context of acute myocardial infarction, we propose that Low Energy Ultrasound (LEUS) exposures might attenuate the detrimental effects of ischemia and reperfusion injury. Specifically, our goal is to quantify and monitor the effects of ultrasound using an in vitro cardiac cell model of ischemia-reperfusion. The study was conducted using a mono-layer cell model (H9C2 cardiomyoblasts) exposed to a prolonged hypoxia-reoxygenation (HR) challenge. Two groups were formed: i). HR: cells submitted to hypoxia followed by reoxygenation period, and ii). HR+PostCond-LEUS: LEUS applied repeatedly for 20 min starting at the onset of reoxygenation. Cell death was evaluated by flow cytometry for each group at the end of US exposure. Cell viability was clearly improved in the HR+PostCond-LEUS group, at all time points during reoxygenation. This study suggests a potential protective effect of LEUS on cardiomyoblasts exposed to a prolonged hypoxia-reoxygenation insult. More complex in vitro models exploring potential protective mechanisms (SAFE and RISK signaling pathways and in vivo models will be required for a better comprehension of the underlying mechanisms.

We investigate the noise sensitivity in various Diffusion Tensor MRI acquisition protocols in sixteen human ex vivo hearts. In particular, we compare the accuracy of protocols with various numbers of excitations and diffusion sensitizing directions for estimating the principal diffusion directions in the myocardium. It is observed that noise sensitivity decreases as the number of excitations and the number of sensitizing directions increase (and hence as the acquisition time increases). To reduce the effects of noise and to improve the results obtained with a smaller number of excitations and/or a smaller number of sensitizing directions, we introduce a 3-D edge-preserving regularization method operating on diffusion weighted images. It allows to maintain the quality of the principal diffusion direction field while minimizing the acquisition time, which is a necessary step for in vivo diffusion tensor MR imaging of the human heart.

A new algorithm is presented to automatically segment the left ventricle from tagged MR images using motion information. This segmentation method is based on a deformable template which is fitted to tags' envelope image through an iterative process. Motion information is used for initializing the template. The method has been evaluated both on simulated and 5 in vivo datasets. A 5 slice levels, 36 frames per slice dataset is fully segmented in less than 10 minutes. The method proposed here has the potential to overcome the main drawbacks of previous approaches, such as user supervision and computing time, to foresee routine clinical use of tagged cardiac MRI.

ECG-gated cardiac MRI in the mouse is hindered by many technical difficulties in ECG signal recording inside static and variable high magnetic scanner fields. The present study proposes an alternative robust method of acquiring auto-gated cine images in mouse heart. In our approach, a motion synchronization signal is extracted from the echo peak MR signal of a non-triggered radial acquisition. This signal is then used for both cardiac and respiratory retrospective gating before cine image reconstruction. Highly asymmetric echoes were acquired to achieve the radial k-space sampling, in order to avoid radial acquisition related artifacts and to increase auto-gating robustness. In vivo experiments demonstrated the feasibility and robustness of self-gated cine-MRI in the mouse heart at 7T

New high resolution image techniques allow to capture the anatomy and movement of the heart of small animals. The availability of these in vivo images can be very useful for medical research, however the amount of generated data for large animal studies makes manual analysis a very tedious task. To cope with the problem of automatic analysis of these images, we propose the use of the deformable elastic template method to perform automatic segmentation of the ventricles. To adapt the method to the specificities of high-resolution MRI, several improvements are presented, including an image-context dependent scheme for more robust segmentation. Qualitative results show that our method is able to correctly retrieve the heart's contours in 3D

This paper presents a realistic numerical beating heart phantom. The development of such a model is motivated by the need of an appropriate ground truth reference for the evaluation of cardiac image processing algorithms. An anatomical model has been developed by extracting cardio-vascular structures contours from a 3D acquisition in magnetic resonance imaging (MRI) of a human healthy subject. Cardiac motion has been tracked by 3D non rigid registration between the consecutive linearly interpolated volumes from dynamic MRI sequences. The so-obtained numerical phantom is structurally rich and its motion realistic. As it issues from the same MRI acquisition on the same human healthy subject, it presents a very good overall consistency and will be use for cardiac image processing algorithms evaluation

In this paper, a fusion system which combines data generated by tagged magnetic resonance imaging (MRI) and F18-FDG positron emission tomography (PET) is presented. MRI and PET complementarity leads to a very accurate assessment of the myocardial viability in patients with coronary heart disease. An accurate viability analysis allows a better prediction of the successes of the revascularization procedure. The fusion system is based on soft computing techniques. It is a modular network which consists of four adaptive network-based fuzzy inference systems (ANFIS) organized in a hierarchical way. The network is able to learn and adapt itself and integrates expert knowledge. It is considered a valuable tool for clinical and research applications.

The non-invasive measurement of the spatio-temporal deformation of the myocardium and its analysis are very useful for improving the clinical diagnosis of cardiac ischemic pathologies. Magnetic resonance tissue tagging is a privileged technique able to provide a spatio-temporal quantification of the myocardial contraction. Based on this technique, existing methods to estimate the deformation generally provide a great number of 3D spatio-temporal parameters that are often quite difficult to interpret. In this paper, we propose software tools to help clinicians to analyze these deformation parameters. We also propose a strategy for the automatic detection of dysfunctional regions using approximate reasoning. The results of this simple approach, illustrated through 5 examinations, are reasonable for large and moderate infarct sizes.

We propose a novel deformable model to assess the deformation of a textured object in an image sequence by simultaneously tracking edge and intensity information. The proposed model is defined as an elastic region with snake-like rigidity constraints at its boundaries. By regularizing the displacement estimation with an elasticity-based constraint, this model is able to assess physically realistic deformations inside a region. By incorporating rigidity constraint at its boundaries, it can also accurately track edges. The numerical implementation of the model is performed using the finite element method. The good behavior of the proposed model is illustrated on synthetic images which simulates the heart contraction in tagged magnetic resonance imaging (MRI). The contour tracking abilities of the model are also illustrated on standard MR images.