Angelo Schiavi

Objective. Proton therapy treatment planning currently needs to account for relatively large range uncertainty margins primarily due to the semi-empirical calibration of the treatment planning x-ray computed tomography (CT) to proton stopping power relative to water (RSP). Proton radiography enables direct measurement of integral RSP, offering potential to improve calibration and reduce these uncertainties. Approach. Three deep neural network architectures are trained to infer a patient RSP map using the treatment planning CT and proton radiographies, assuming straight proton trajectories. The best suited architecture is then evaluated on more realistic, clinical-like data generated with Monte Carlo simulations. Three idealized proton imaging detectors are simulated: single particle tracking (SPT), a pixelated energy-resolving imager (PERI) and a proton-integrating detector (PID). Main results. The learned primal dual (LPD) architetcure performs best in the simplified imaging scenario. In the more realistic scenario, the initial calibration error (median absolute percentage error of 2.24% across the test set) is reduced using only two projections across all detector types. SPT and PERI reach similar performance (1.10%/1.12%), followed by PID (1.30%). Restricting the LPD to the calibration task by incorporating prior knowledge of the functional relationship of Hounsfield Units (HUs) and RSP further improves calibration performance. For SPT, conventional optimization on detector data acquired for individual protons outperformed the data-driven method (0.16% vs. 1.10%). However, for PERI and PID, the data-driven approach (1.12%/1.30%) slightly outperformed conventional optimization (1.63%/1.72%). Significance. To our knowledge, this is the first study to apply a deep learning-based approach fusing proton radiographies and treatment planning CT data for improved RSP calibration. The method achieves lower calibration errors than idealized, conventional calibration curve optimization on PERIs and PIDs—detector types that offer a promising path for clinical adoption due to their lower complexity and cost compared to SPT systems.

This study presents Fast paRticle thErapy Dose optimizer (FREDopt), a newly developed GPU-accelerated open-source optimization software for simultaneous proton dose and dose-averaged linear energy transfer (LET_d) optimization in intensity-modulated proton therapy treatment planning. FREDopt was implemented entirely in Python, leveraging CuPy for GPU acceleration and incorporating fast Monte Carlo simulations from the FRED code. The treatment plan optimization workflow includes pre-optimization and optimization, the latter equipped with a novel superiorization of feasibility-seeking algorithms. Feasibility-seeking requires finding a point that satisfies prescribed constraints. Superiorization interlaces computational perturbations into iterative feasibility-seeking steps to steer them toward a superior feasible point, replacing the need for costly full-fledged constrained optimization. The method was validated on two treatment plans of patients treated in a clinical proton therapy center, with dose and LET_d distributions compared before and after reoptimization. Simultaneous dose and LET_d optimization using FREDopt led to a substantial reduction of LET_d and (dose) × (LET_d) in organs at risk while preserving target dose conformity. Computational performance evaluation showed execution times of 14–50 min, depending on the algorithm and target volume size—satisfactory for clinical and research applications while enabling further development of the well-tested, documented open-source software.

Gas-filled plastic spherical shells have been imploded using the 60-beam OMEGA laser. The same laser pulse was used as in a previous experiment, which demonstrated the dynamic formation of a shell by irradiating a homogeneous plastic foam sphere [Igumenshchev et al., Phys. Rev. Lett. 131, 015102 (2023)]. In the present experiment, the shell initially implodes, bounces, and expands; a new shell then forms and undergoes a second implosion. Analysis of x-ray self-emission-streaked images and sequences of 2D x-ray self-emission framing camera images show shell evolution that is in reasonable agreement with 1D simulations. Shell integrity and symmetry are well maintained until the formation of the new shell. The final implosion, instead, is affected by substantial asymmetries.

Objective. Verification of delivered proton therapy treatments is essential for reaping the many benefits of the modality, with the most widely proposed in vivo verification technique being the imaging of positron emitting isotopes generated in the patient during treatment using positron emission tomography (PET). The purpose of this work is to reduce the computational resources and time required for simulation of patient activation during proton therapy using the GPU accelerated Monte Carlo code FRED, and to validate the predicted activity against the widely used Monte Carlo code GATE. Approach. We implement a continuous scoring approach for the production of positron emitting isotopes within FRED version 5.59.9. We simulate treatment plans delivered to 95 head and neck patients at Centrum Cyklotronowe Bronowice using this GPU implementation, and verify the accuracy using the Monte Carlo toolkit GATE version 9.0. Main results. We report an average reduction in computational time by a factor of 50 when using a local system with 2 GPUs as opposed to a large compute cluster utilising between 200 to 700 CPU threads, enabling simulation of patient activity within an average of 2.9 min as opposed to 146 min. All simulated plans are in good agreement across the two Monte Carlo codes. The two codes agree within a maximum of 0.95 σ on a voxel-by-voxel basis for the prediction of 7 different isotopes across 472 simulated fields delivered to 95 patients, with the average deviation over all fields being 6.4 × 10 ⁻³ σ . Significance. The implementation of activation calculations in the GPU accelerated Monte Carlo code FRED provides fast and reliable simulation of patient activation following proton therapy, allowing for research and development of clinical applications of range verification for this treatment modality using PET to proceed at a rapid pace.

Different therapies are adopted for the treatment of deep seated tumours in combination or as an alternative to surgical removal or chemotherapy: radiotherapy with photons (RT), particle therapy (PT) with protons or even heavier ions like ¹²C, are now available in clinical centres. In addition to these irradiation modalities, the use of Very High Energy Electron (VHEE) beams (100–200 MeV) has been suggested in the past, but the diffusion of that technique was delayed due to the needed space and budget, with respect to standard photon devices. These disadvantages were not paired by an increased therapeutic efficacy, at least when comparing to proton or carbon ion beams. In this contribution we investigate how recent developments in electron beam therapy could reshape the treatments of deep seated tumours. In this respect we carefully explored the application of VHEE beams to the prostate cancer, a well-known and studied example of deep seated tumour currently treated with high efficacy both using RT and PT. The VHEE Treatment Planning System was obtained by means of an accurate Monte Carlo (MC) simulation of the electrons interactions with the patient body. A simple model of the FLASH effect (healthy tissues sparing at ultra-high dose rates), has been introduced and the results have been compared with conventional RT. The study demonstrates that VHEE beams, even in absence of a significant FLASH effect and with a reduced energy range (70–130 MeV) with respect to implementations already explored in literature, could be a good alternative to standard RT, even in the framework of technological developments that are nowadays affordable.

Prostate cancer is among the most common cancers in men and one of the leading causes of death worldwide. Different therapies are adopted for its treatment and generally radiotherapy with photons (RT) is the preferred solution in almost all cases. Up to now, in addition to photons, only protons have been implemented as alternative radiotherapy. The use of Very High Energy Electron (VHEE) beams (100-200 MeV) has been suggested in literature but the needed accelerators are more demanding, as far as space and cost are concerned, with respect to standard photon devices, with only limited advantages when compared to protons or other heavy ions. In this contribution we investigate how recent developments in electron beam therapy could reshape the landscape of prostate treatments. The VHEE Treatment Planning System obtained combining an accurate Monte Carlo (MC) simulation with a simple modelling of the FLASH effect (healthy tissues sparing at very high dose rates) is compared with conventional RT. The results demonstrate that FLASH therapy with VHEE beams of 70-130 MeV could represent a valid alternative to standard RT allowing a better sparing of the healthy tissues surrounding the tumour, in the framework of an affordable technological development.