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Algorithmic derivatives can be useful to quantify uncertainties and optimize parameters using computer simulations. Whether they actually are, depends on how "well-linearizable" the program is.Proton computed tomography (pCT) is a medical imaging technology with the potential to increase the spatial accuracy of the dose delivered in protonbeam radiotherapy. The Bergen pCT collaboration is developing and constructing a digital tracking calorimeter (DTC) to measure the position, direction and energy of protons after they passed through a patient, and a software pipeline to process these data into a pCT image.We revisit the software pipeline from the perspective of algorithmic differentiation (AD). In the early subprocedures, several obstacles such as discrete variables or frequent discontinuities were identified, and are probably tackled best by using surrogate models. The model-based iterative reconstruction (MBIR) subprocedure in the end seems to be AD-ready, and we propose changes in the AD workflow that can reduce the memory consumption in reverse mode.

The Bergen proton Computed Tomography (pCT) is a prototype detector under construction. It aims to have the capability to track and measure ions’ energy deposition to minimize uncertainty in proton treatment planning. It is a high granularity digital tracking calorimeter, where the first two layers will act as tracking layers to obtain positional information of the incoming particle. The remainder of the detector will act as a calorimeter. Beam tests have been performed with multiple beams. These tests have shown that the ALPIDE chip sensor can measure the deposited energy, making it possible for the sensors to distinguish between the tracks in the Digital Tracking Calorimeter (DTC).

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