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Thu, 03/07/2014 - 14:26
Intrauterine growth restriction is a disease that affects about 10% of the pregnancies leading to important structural and functional changes in the heart. These changes persist postnatally and have been associated with an increased risk of cardiovascular diseases in adulthood. Moreover there also changes in the structure and function at cellular and subcellular level. However how these changes affect the global function of the heart need to be further investigated. A 3D electromechanical model of the cardiomyocyte would be a useful tool to study the local behavior of the heart and to relate it with the global performance of the whole organ. In this presentation I’m going to talk about the study and modeling of the cardiomyocyte, and its applicability to study the cellular and subcellular changes observed in IUGR.
Thu, 03/07/2014 - 14:19

Last Thursday Carlos Alberto Figueroa from University of Michigan and King’s College of London was giving an overview of a series of methods for 3D blood flow modeling, ranging from Kalman filtering techniques for automatic outflow and material parameter estimation to baroreflex model for automatic control of blood pressure. We also discussed the recent progress made on the validation of CFD predictions of pressure gradients in coarctation patients at rest and stress using clinical pressure data.

Mon, 16/06/2014 - 16:27

In this work he presents a method for electrical simulation of cochlear implants that can be used for patient-specific planning of the surgical implantation and determination of stimulation patterns. The model combines high-resolution microCT and clinical CT data to create a detailed and personalized simulation of the electrical properties of the cochlea. 

Using this model, we can predict voltage spread after electrode implantation. As higher spreads correlate with inter-electrode interference and noisy hearing, we can use the results of this model to detect which configurations of electrode placement are to avoid during surgery.

Fri, 13/06/2014 - 11:44

The construction of statistical shape models (SSMs) that are rich, i.e., that represent well the natural and complex variability of anatomical structures, is an important research topic in medical imaging. To this end, existing works have addressed the limited availability of training data by decomposing the shape variability hierarchically or by combining statistical and synthetic models built using artificially created modes of variation.

In this paper, we present instead a method that merges multiple statistical models of 3D shapes into a single integrated model, thus effectively encoding extra variability that is anatomically meaningful, without the need for the original or new real datasets. The proposed framework has great flexibility due to its ability to merge multiple statistical models with unknown point correspondences. The approach is beneficial in order to re-use and complement pre-existing SSMs when the original raw data cannot be exchanged due to ethical, legal, or practical reasons. To this end, this paper describes two main stages, i.e., 1) statistical model normalization and 2) statistical model integration. The normalization algorithm uses surface-based registration to bring the input models into a common shape parameterization with point correspondence established across eigenspaces. This allows the model fusion algorithm to be applied in a coherent manner across models, with the aim to obtain a single unified statistical model of shape with improved generalization ability. The framework is validated with statistical models of the left and right cardiac ventricles, the L1 vertebra, and the caudate nucleus, constructed at distinct research centers based on different imaging modalities (CT and MRI) and point correspondences. The results demonstrate that the model integration is statistically and anatomically meaningful, with potential value for merging pre-existing multi-modality statistical models of 3D shapes.