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PhySense seminar (Thursday, June 26th): Carlos Alberto Figueroa was talking about Advances in 3D Blood flow Simulation
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.
PhySense seminar (Friday, June 20th) : Mario Ceresa will talk about patient-specific electrical simulation of cochlear implants for surgical planning and optimization of stimulation patterns
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.
Constantine Butakoff published an article about a framework for the Merging of Pre-Existing and Correspondenceless 3D Statistical Shape Models.
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.