Speaker
Description
Abstract: Proton computed tomography (pCT) is poised to advance precise dose planning in hadron therapy, an innovative cancer treatment that uses protons and heavy ions to deliver targeted radiation. By harnessing the Bragg peak effect, hadron therapy can concentrate radiation on tumors while minimizing exposure to surrounding healthy tissues. Achieving high-resolution pCT images, however, requires cutting-edge computational solutions capable of processing granular detector data and reconstructing detailed images in real time. In my talk I present the development of neural network models and GPU-accelerated algorithms designed to meet the computational demands of proton CT, enabling more effective and accurate treatment planning in clinical settings.
References
[1] G. Bíró, Á. Sudár, Zs. Jólesz, G. Papp, G.G. Barnaföldi; arXiv:2212.00126
[2] M. Aehle et al.; arXiv:2503.02788
[3] D. Röhrich et al.; Phys.Med.Biol. 65 (2020) 135012
