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BACKGROUND: While individual cardiac myocytes only have a limited ability to shorten, the heart efficiently pumps a large volume-fraction thanks to a cell organization in a complex 3D fibre structure. Subclinical subtle cardiac structural remodelling is often present before symptoms arise. Understanding and early detection of these subtle changes is crucial for diagnosis and prevention. Additionally, personalized computational modelling requires knowledge on the multi-scale structure of the whole heart and vessels. METHODS AND RESULTS: We developed a rapid acquisition together with visualization and quantification methods of the integrated microstructure of whole in-vitro rodents hearts using synchrotron based X-ray phase-contrast tomography. These images are formed not only by X-ray absorption by the tissue but also by wave propagation phenomena, enhancing structural information, thus allowing to raise tissue contrast to an unprecedented level. We used a (ex-vivo) normal rat heart and fetal rabbit hearts suffering intrauterine growth restriction as a model of subclinical cardiac remodelling to illustrate the strengths and potential of the technique. For comparison, histology and diffusion tensor magnetic resonance imaging was performed. CONCLUSIONS: We have developed a novel, high resolution, image acquisition, and quantification approach to study a whole in-vitro heart at myofibre resolution, providing integrated 3D structural information at microscopic level without any need of tissue slicing and processing. This superior imaging approach opens up new possibilities for a systems approach towards analysing cardiac structure and function, providing rapid acquisition of quantitative microstructure of the heart in a near native state.

The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. This transform, by means of the lifting scheme, can be performed in a memory mid computation efficient way on modern, programmable GPUs, which can be regarded as massively parallel co-processors through NVidia's CUDA compute paradigm. The method is scalable and the fastest GP U implementation among the methods considered. We have integrated our DWT into the Dirac Wavelet Video Codec (DWVC), of which the overlapped block motion compensation compensation and frame arithmetic have been accelerated using CUDA as well.

Background: In severe aortic stenosis (AS), the gradient measured over a stenotic valve is indicative of stenosis severity, but often does not relate to a potential decrease in myocardial function. After initial hypertrophic remodeling of the left ventricle, myocardial fibrosis occurs leading to systolic dysfunction. We hypothesize a relation between the shape of the aortic outflow velocity profile and myocardial functional impairment due to fibrosis in AS. Methods: 34 patients undergoing aortic valve surgery for severe AS were studied by echocardiography pre- (PRE) and 9 months postoperatively (POST). 29 healthy volunteers served as controls. Semi-automated analysis was applied to aortic continuous wave velocity traces from which the asymmetry of the velocity profile (asymm) was quantified as the normalized difference of area under the curve of left and right half of the spectrum so that a lower asymm corresponded to more symmetrical, later peaking flow. Cardiac Magnetic Resonance imaging (CMR) including images for late enhancement (LE) diagnostics was performed preoperatively in 18 of the pts for the assessment of replacement fibrosis. Left ventricular myocardial biopsy specimens were acquired during aortic valve replacement surgery in 29 of the pts for the assessment of interstitial fibrosis. Results: Asymm PRE values ≥0.15 showed 95 % specificity and 100 % sensitivity in detecting patients with postoperative EF improvement, regardless of PG mean PRE. Signs of LE were demonstrated by CMR in 72% of the patients in which such a study was performed. The same percentage of patients showed signs of myocardial fibrosis in the biopsies, In the subgroup of patients with asymm PRE ≥ 0.15 (which all improved EF postoperatively), CMR LE was present in 50% of these patients. Conversely, in the subgroup of patients with asymm PRE < 0.15, CMR detected LE in 90.9% of these patients. Finally, after AVR, values of asymm had increased to ≥ 0.15 in all patients. Nevertheless, 10 patients retained asymm < 0.25. Preoperative CMR data were available in 6/10 patients and in all of them showed signs of LE detectable on CMR imaging. Conclusions: A symmetrical aortic outflow profile seems related to a reduction in left ventricular function as well as to replacement fibrosis as detected by CMR LE. Furthermore, it predicts functional recovery after surgery with high specificity and sensitivity. Semi-automated analysis provides additional clinical information on the functional impact of AS.

The DiscreteWavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. This transform, by means of the lifting scheme, can be performed in a memory and computation efficient way on modern, programmable GPUs, which can be regarded as massively parallel co-processors through NVidia’s CUDA compute paradigm. The method is scalable and the fastest GPU implementation among the methods considered. We have integrated our DWT into the Dirac Wavelet Video Codec (DWVC), of which the overlapped block motion compensation compensation and frame arithmetic have been accelerated using CUDA as well.

An undirected weighted graph may be constructed from diffusion weighted magnetic resonance imaging data. Every node represents a voxel and the edge weights between nodes represent the white matter connectivity between neighboring voxels. In this paper we propose and test a new method for calculating trajectories of fiber bundles in the brain by applying Dijkstra's shortest path algorithm to the weighted graph. Subsequently. the resulting tree structure is pruned, showing the main white matter structures of the brain. The time consumption of this method is in tire order of seconds.

We propose a texture analysis method based on Rényi’s entropies. The method aims at identifying texels in regular textures by searching for the smallest window through which the minimum number of different visual patterns is observed when moving the window over a given texture. The experimental results show that any of Rényi’s entropies can be used for texel identification. However, the second order entropy, due to its robust estimation, is the most reliable. The main advantages of the proposed method are its robustness and its flexibility. We illustrate the usefulness and the effectiveness of the method in a texture synthesis application and we compare it with other structural approaches.