A deep-learning based algorithm for image reconstruction in Compton tomography

• Published in: International Workshops on Image Processing Theory, Tools, and Applications pp. 1 - 6
• Main Authors: Ayad, Ishak, Tarpau, Cecilia, Cebeiro, Javier, Nguyen, Mai K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.10.2023
• Subjects: Attenuation; Brain modeling; circular Radon transform; Compton tomography; deep-learning image reconstruction; Detectors; GANs; Image quality; Tomography; Transforms
• ISSN: 2154-512X
• DOI: 10.1109/IPTA59101.2023.10320071

In this paper we study the applications of deep-learning to the problem of image reconstruction in Compton scatter tomography, a field where deep-learning techniques are still unexplored. Particularly, we focus on a new design with uncollimated detectors that simplifies some previous configurations of Compton scanners. The system inherits attractive advantages such as non-moving components and the ability to combine with other imaging modes. Since there is no an analytic inverse formula for image reconstruction, we developed a GAN based algorithm that provides an efficient mapping between data and image domains. We compare our method against several algorithmic approaches and show that high quality image reconstruction is feasible. Results encourage further research in the application of deep-learning reconstruction techniques in Compton scatter tomography, particularly when inverse reconstruction formulas are unknown.