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Development of a patient‐specific atrial phantom model for planning and training of inter‐atrial interventions
Author(s) -
Morais Pedro,
Tavares João Manuel R. S.,
Queirós Sandro,
Veloso Fernando,
D'hooge Jan,
Vilaça João L.
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12559
Subject(s) - imaging phantom , biomedical engineering , computer science , ultrasound , medical physics , artificial intelligence , medicine , radiology
Background Several authors have presented cardiac phantoms to mimic the particularities of the heart, making it suitable for medical training and surgical planning. Although the initial models were mainly focused on the ventricles, personalized phantoms of the atria were recently presented. However, such models are typically rigid, the atrial wall is not realistic and they are not compatible with ultrasound (US), being sub‐optimal for planning/training of several interventions. Methods In this work, we propose a strategy to construct a patient‐specific atrial model. Specifically, the target anatomy is generated using a computed tomography (CT) dataset and then constructed using a mold‐cast approach. An accurate representation of the inter‐atrial wall (IAS) was ensured during the model generation, allowing its application for IAS interventions. Two phantoms were constructed using different flexible materials (silicone and polyvinyl alcohol cryogel, PVA‐C), which were then compared to assess their appropriateness for US acquisition and for the generation of complex anatomies. Results Two experiments were set up to validate the proposed methodology. First, the accuracy of the manufacturing approach was assessed through the comparison between a post‐production CT and the virtual references. The results proved that the silicone‐based model was more accurate than the PVA‐C‐based one, with an error of 1.68 ± 0.79, 1.36 ± 0.94, 1.45 ± 0.77 mm for the left (LA) and right atria (RA) and IAS, respectively. Second, an US acquisition of each model was performed and the obtained images quantitatively and qualitatively assessed. Both models showed a similar performance in terms of visual evaluation, with an easy detection of the LA, RA, and the IAS. Furthermore, a moderate accuracy was obtained between the atrial surfaces extracted from the US and the ideal reference, and again a superior performance of the silicone‐based model against the PVA‐C phantom was observed. Conclusions The proposed strategy proved to be accurate and feasible for the correct generation of complex personalized atrial models.