By Wenyu Fu, Aike Qiao (auth.), Oscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young (eds.)

This publication constitutes the completely refereed post-conference lawsuits of the 3rd overseas Workshop on Statistical Atlases and Computational versions of the guts: Imaging and Modelling demanding situations, STACOM 2012, held at the side of MICCAI 2012, in great, France, in October 2012.
The forty two revised complete papers have been conscientiously reviewed and chosen from quite a few submissions. The papers are geared up in topical sections on CFD problem, DE-MRI segmentation problem, LV landmark detection problem, movement monitoring research problem, and ordinary papers.

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Extra info for Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: Third International Workshop, STACOM 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 5, 2012, Revised Selected Papers

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3. The absolute pressure over time at the ascending aorta is shown for different spacial resolutions. The upper graph shows the resulting curve for the higher time resolution. The according time-step sizes can be found in Table 1. 40 T. Henn et al. Fig. 4. The pressure drop between the planes π1 and π2 around the coarctation are shown for different spatial resolutions. The upper graph shows the resulting curve for the higher time resolution. The according time-step sizes can be found in Table 1. 41 · 10−6 Table 1.

This double buffer approach further increases the already large memory demand of the simulation. In the case of this data set when simulated at a 200 micron resolution, there are 64,435 fluid voxels in a bounding box of 11,254,320 voxels. For each lattice point, there are two buffers that make up the bulk of the memory requirements. These buffers store the density data for each discrete velocity at each lattice point as a floating point number. For a 200 micron resolution simulation, this requires at least three gigabytes of memory.

I is the unit tensor in Cartesian space. A key advantage of the LBM is that macroscopic quantities such as density are moments of the distribution function. This means that they can be calculated based on its summation and therefore are available entirely locally. In the study of CoA, the fluid pressure is particularly important. Pressure can be easily recovered through the ideal gas relation: P = c2s ρ. This means that the value is available locally which is particularly advantageous as this means they do not require solving an expensive Poisson problem as in other CFD methods [8].

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