Resumo (PT):
Abstract (EN):
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 it is not compatible with
ultrasound, 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 ultrasound (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. Secondly, 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 found.
Conclusions: The proposed strategy proved
Language:
English
Type (Professor's evaluation):
Scientific
No. of pages:
12