Speaker
Description
Proton computed tomography (pCT) promises a direct, low-dose measurement of the relative stopping power (RSP) map that governs proton-therapy dose calculations. In a tracking-based scanner every proton is recorded individually, and reconstructing the RSP image requires estimating the path each proton actually took through the patient. This is intrinsically hard: multiple Coulomb scattering makes the path stochastic — two protons entering identically exit along measurably different tracks — so the true per-proton trajectory can only be inferred from the noisy exit measurement. Classical approaches address this by building and repeatedly applying a large system matrix, a step that is memory-hungry and ill-suited to the streaming, high-rate data of a real scanner.
We explore a machine-learning route to this inference problem: rather than storing a system matrix, a neural network estimates each proton's path on the fly, directly from its exit signature, and feeds the result straight into iterative RSP reconstruction. Framing the per-proton path as a learned inference task naturally raises the question of resolution limits and uncertainty, which we take as central rather than incidental.
In this talk we motivate the problem from the underlying physics and present our progress toward reconstruction quality and throughput competitive with classical methods — a step toward truly on-the-fly proton CT during treatment.