AugmentA: Patient-specific augmented atrial model generation tool
Digital twins of patients’ hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria ge...
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Veröffentlicht in: | Computerized medical imaging and graphics 2023-09, Vol.108, p.102265, Article 102265 |
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Sprache: | eng |
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Zusammenfassung: | Digital twins of patients’ hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.
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•Methodology to generate anatomical and functional atrial digital twins.•Personalized models from either imaging data or electroanatomical maps.•Reproducible framework to process geometries derived from clinical data.•User-interaction reduced to the selection of one reference point per atrium.•Augment original geometry fitting a bi-atrial statistical shape model. |
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ISSN: | 0895-6111 1879-0771 1879-0771 |
DOI: | 10.1016/j.compmedimag.2023.102265 |