Abstract
Breast reconstruction using autologous tissue, specifically with Deep Inferior Epigastric Artery Perforator (DIEP) flaps, has made significant advancements in recent years by aiming to minimize damage to the donor site. In this regard, selecting the appropriate perforator is crucial, given that the flap is perfused by a single perforator. The current standard method for selecting perforators is Computed Tomography Angiography (CTA), which has drawbacks including the use of contrast agents, exposure to radiation, high costs, and no information on flap perfusion. Recent studies have demonstrated that Dynamic Infrared Thermography (DIRT) is a non-invasive method capable of visualizing both the dominant perforators preoperatively and the perfused zones associated with these perforators intraoperatively. Identifying these perfused zones is essential for optimizing the breast's survival chances, but it requires an additional average surgical time of 60 minutes. The aim of this project is to predict perfused zones of specific perforators without intraoperative measurements, resulting in faster and more accurate treatments. This is achieved through the development of a Convolutional Neural Network trained with Finite Element Method (FEM) models of the abdomen with perforators. These models are constructed using both CTA and pre- and intraoperative DIRT data, with FEM updating to adapt the model's thermal behavior to the infrared measurements.
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