Objective: Proton therapy of cancer improves dose conformality to the target and sparing of surrounding healthy tissues compared to conventional photon treatments. However, proton therapy’s advantage could be even larger if proton range uncertainties were reduced. Sources of range uncertainties include computed tomography (CT) treatment planning images and variations in patient anatomy and setup. To reduce range uncertainties, we have developed a system for real-time in vivo range monitoring. The system is based on spectroscopy of prompt gamma-rays emitted through proton-nuclear interactions during irradiation. We validated the performance of our prompt gamma-ray spectroscopy detector prototype using tissue-mimicking and porcine samples. Approach: Measurements were performed in water, four tissue-mimicking samples (spongiosa, muscle, adipose tissue, and cortical bone), and two porcine samples (liver and brain). A dose of 0.9 Gy was delivered to a target at a depth of 12.5-17.5 cm. Multi-layer ionization chamber (MLIC) measurements were performed to determine stopping power ratios (SPRs) relative to water and ground truth proton ranges. Ground truth elemental compositions were determined using combustion analysis. Proton ranges and elemental compositions measured using prompt gamma-ray spectroscopy were compared to the ground truth. Main results: For all samples, the mean measured range over all pencil-beam spots differed from the ground truth by less than 1.2 mm. The mean standard deviation was 0.9 mm (range: 0.4 mm to 1.6 mm). The mean difference between ground truth and measured elemental compositions was 0.06 g/(cm^3) (range: 0.00 g/(cm^3) to 0.12 g/(cm^3)). Significance: We verified the performance of our prompt gamma-ray spectroscopy detector prototype for proton range verification using tissue-mimicking and porcine samples. Measured proton ranges and elemental sample compositions were in good agreement with the ground truth. These measurements confirm the system’s reliability for a variety of tissues and bridge the gap between previously-reported experiments and ongoing in vivo patient measurements.

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Approximately 2.5% of the proton range uncertainty comes from computed tomography (CT) number to material characteristic conversion. We aim to conquer this CT-to-material conversion error by proposing a multimodal imaging framework to enable deep learning (DL)-based material mass density inference using dual-energy CT (DECT) and magnetic resonance imaging (MRI). To ensure the robustness of DL models, we integrated physics insights into the framework to regularize DL models and achieve DL using small datasets. Five MRI-compatible phantoms were created from tissue-mimicking materials that served as a ground true reference to validate the proposed framework. The reference mass densities for each phantom were measured by a 150 MeV proton beam. Multimodal images were acquired from T1and T2-weighted images and DECT images as training and validation data for DL. Residual networks (ResNet) were implemented to evaluate the feasibility of the proposed framework. ResNet-DE-MR denotes that ResNet was trained with MRI and DECT images, while ResNet-DE presents that only DECT images were used to train ResNet. ResNet was also compared to an empirical DECT model. Meanwhile, a retrospective patient case was included in the study to demonstrate the proof of concept for the proposed framework. The phantom validation experiment showed that ResNet-DE-MR achieved mass density errors of -0.4%, 0.3%, 0.4%, 0.7%, and -0.2% for adipose, muscle, liver, skin, and bone. The proposed DL-based multimodal imaging framework was demonstrated to enable accurate material mass density inference using DECT and MR images. The framework can potentially improve the treatment quality for proton therapy by reducing proton range uncertainty.