| Title |
Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models
|
|---|---|
| Published in |
Biomechanics and Modeling in Mechanobiology, September 2017
|
| DOI | 10.1007/s10237-017-0960-0 |
| Pubmed ID | |
| Authors |
Roch Molléro, Xavier Pennec, Hervé Delingette, Alan Garny, Nicholas Ayache, Maxime Sermesant |
| Abstract |
Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified "0D" version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 50% |
| France | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 28 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 32% |
| Student > Ph. D. Student | 5 | 18% |
| Student > Bachelor | 3 | 11% |
| Other | 2 | 7% |
| Professor | 2 | 7% |
| Other | 3 | 11% |
| Unknown | 4 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 6 | 21% |
| Computer Science | 6 | 21% |
| Medicine and Dentistry | 3 | 11% |
| Mathematics | 1 | 4% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Other | 3 | 11% |
| Unknown | 8 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 September 2017.
All research outputs
#14,405,036
of 23,849,058 outputs
Outputs from Biomechanics and Modeling in Mechanobiology
#186
of 486 outputs
Outputs of similar age
#168,522
of 317,826 outputs
Outputs of similar age from Biomechanics and Modeling in Mechanobiology
#5
of 8 outputs
Altmetric has tracked 23,849,058 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 486 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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