Tomographic reconstruction from planar thermal imaging using convolutional neural network.

ABSTRACT: In this study, we investigate perspectives for thermal tomography based on planar infrared thermal images. Volumetric reconstruction of temperature distribution inside an object is hardly applicable in a way similar to ionizing-radiation-based modalities due to its non-penetrating character. Here, we aim at employing the autoencoder deep neural network to collect knowledge on the single-source heat transfer model. For that purpose, we prepare a series of synthetic 3D models of a cylindrical phantom with assumed thermal properties with various heat source locations, captured at different times. A set of planar thermal images taken around the model is subjected to initial backprojection reconstruction, then passed to the deep model. This paper reports the training and testing results in term

SUBMITTER: Ledwon D 

PROVIDER: S-EPMC8837619 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature