To overcome the issue of interfractional anatomical modifications, state-of-the-art ion therapy facilities are largely adopting the integration of different flavours of X-ray cone-beam CT (CBCT) imaging systems, which allow imaging the patient directly at the isocenter of the treatment unit. In particular, medPhoton has developed and is constantly improving a prototype CBCT system currently adopted in several ion beam therapy facilities. However, CBCT typically suffers from reduced image quality due to scattering and several other shortcomings. Hence, while the quality of CBCT images is deemed sufficient for the task of patient positioning, it is not yet suitable for accurate dosimetric calculations for adaptive therapy.

The first task of the PhD candidate will be to further extend and integrate existing computational frameworks to enable thorough investigations on the potential enhancement strategies of CBCT at the projection and image level with the advanced imaging ring prototype being developed at medPhoton. To this end, he/she will be working on in-silico and real DE-CBCT data of clinical cases and phantoms of increasing complexity, respectively. The study will be especially focused on those clinical entities for which interfractional anatomical modifications and inaccurate SPR represent a major limitation to full clinical exploitation of ion beam therapy. Validation of image registration strategies on a case-specific basis as well as overall validation of the new imaging performance against accessible ground truth data (both in terms of anatomical fidelity and SPR values) will be also performed, in order to facilitate clinical utilization of the developed methods.

Finally, a systematic evaluation of the optimal solutions for realistic clinical indications, resulting in the recommendation for future development paths in isocentric ion therapy image guidance will be performed. The result of the project could thus have great relevance by motivating a paradigm shift in CBCT image guidance in ion therapy, with an expected improvement of treatment quality, which could ultimately positively impact treatment outcome and become a cornerstone of the RAPTOR workflow.