Yibao Zhang

Objective. Accurate dose verification remains a major challenge in particle therapy. Dose monitoring methods such as positron emission tomography (PET) and prompt-gamma (PG) imaging are highly sensitive to uncertainties in tissue composition derived from CT, which can be improved by dual-energy CT (DECT)-based material decomposition. This study systematically assessed the accuracy of four DECT elemental decomposition algorithms in predicting physical dose, annihilation-photon, and PG reference distributions in particle therapy, focusing on CT-based model uncertainty without detector considerations. Approach. Three parameterization-based DECT methods, a machine-learning (ML) DECT method, and a conventional single-energy CT (SECT) method were used to predict the elemental composition of the ICRP110 human phantom and 10 head-and-neck patients. The physical dose, annihilation, and PG distributions for incident proton, helium, carbon, and oxygen pencil beams were compared using Monte Carlo simulations. For the phantom, mean relative errors (MREs) were calculated relative to the reference distributions. For the patients, the distal fall-off positions of annihilation and PG profiles were compared across the methods. Main results. In the phantom study, the ML method yielded the lowest MREs across the four incident particle types. For oxygen ions, the ML method yielded an annihilation-photon MRE of 2.64%, compared with 6.50%–6.74% for the parameterization-based DECT methods. The corresponding PG MRE were 1.53%, 2.44%–2.60% for the ML and parameterization-based methods. The matched SECT analysis provided a conventional baseline under the same simulation and evaluation settings. In the patient analyses, the distal fall-off positions of annihilation and PG differed by up to 1.93 ± 0.42 mm and 1.76 ± 0.44 mm, respectively, between ML and the parameterization-based methods. Without voxel-wise patient ground truth, these results describe inter-method differences rather than an accuracy ranking. Significance. Compared with the parameterization-based methods, the ML method reduced errors in predicting source-level PET and PG reference distributions under the idealized Monte Carlo conditions used here, especially for heavier incident particles. These results quantify model-related uncertainty in complementary with detector resolution in clinical monitoring accuracy.

The development of laser-plasma accelerators has brought new opportunities for tumor radiotherapy. After appropriate energy selection and beam transport, laser-accelerated proton beams can provide depth-dose characteristics relevant to proton radiotherapy. Owing to their ultrashort pulse duration and extremely high instantaneous dose rate, they also provide a promising platform for ultra-high-dose-rate and FLASH-related radiobiological studies. However, the low-frequency single-shot mode and dose fluctuations of laser proton beams, resulting from the transient nonlinear acceleration process, limit the accuracy of irradiation doses. Traditional irradiation control methods are inadequate to address these issues. Here, we propose a model-based adaptive dose-delivery method based on sequential decision optimization, which can maintain stable dose control with an accuracy of 5% under unstable laser-accelerated proton beams. This method involves adjusting the beam transport system and employing feedback control systems to monitor and adjust beam parameters in real time, adaptively compensating for upstream instabilities without altering the conditions at the driver end. Simulation results show that these schemes significantly outperform traditional methods in terms of dose delivery accuracy and irradiation efficiency, providing important references for the further development of laser proton radiotherapy technology.

Objective. The limited longitudinal field-of-view (FOV) of cone-beam computed tomography (CBCT) restricts patient setup and adaptive radiotherapy (ART). This study aims to restore the longitudinal CBCT anatomy by introducing updated optical surface image (OSI) guidance into a diffusion-based FOV extension framework. Approach. Longitudinal CBCT FOV extension was formulated as a surface-aware image restoration task. A denoising diffusion implicit model (DDIM)-based RePaint was developed to use the truncated CBCT on the treatment-day, prior planning computed tomography (pCT), and updated OSI as complementary inputs, where pCT provided patient-specific anatomical prior, while OSI supplied synchronized surface information with CBCT to constrain inter-fractional differences from pCT. Various noise levels were simulated to test the stability. RePaint was used to improve consistency between known and restored regions, and DDIM sampling was adopted to accelerate three-dimensional inference. To test its performance, three CBCTs on different days and one pCT of 41 thoracic patients from two centers were retrospectively selected (Center A: 34 training, 4 testing, Trilogy; Center B: 3 testing, Ethos), yielding 102 training pairs and 21 test pairs. Main results. On testing cases, DDIM-RePaint achieved MAE = 25.28 ± 2.45 HU, SSIM = 94.89 ± 0.73, PSNR = 23.05 ± 0.83 for center A, and MAE = 35.20 ± 4.30 HU, SSIM = 91.27 ± 2.50, PSNR = 21.12 ± 0.89 for center B, both were significantly better than that of fusing pCT and CBCT as the current clinical approach (all P < 0.001). The inference time on 12 samples was reduced from 45 min – 2.2 min by sampler optimization. Significance. The proposed surface-aware DDIM-RePaint framework provided an accurate and efficient approach for longitudinal CBCT FOV extension, potentially facilitating patient setup and ART planning without extra imaging dose and hardware upgrade.

Objective. To develop deep-learning (DL) predictive models of internal gastrointestinal (GI) gas from radiographs, a surrogate for dose degradation in abdominal proton beam therapy (PBT). Approach. DL architectures (convolutional neural networks and U-Nets) were trained to predict GI air local path length and/or global volume from radiographs. Frontal/lateral radiographs with paired volume and path length labels were simulated from CTs and GI gas segmentations using the Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox. CTs from The Cancer Imaging Archive Paediatric-CT-SEG dataset ( N = 349, 0–16 year, North America) were used for model development and internal evaluation; variation in GI filling, organ motion and body size were simulated for augmentation. External evaluation utilised data from two international radiotherapy institutions: one consisting mostly of children and young people (CYP) ( N = 21, 2–19 year, Europe); the other predominantly of teenagers and young adults (TYAs) ( N = 14, 10–24 year, Asia). Finally, real-life use case was demonstrated on two subjects treated with PBT. Main results. The optimal configuration was the dual U-Net, accurately predicting both volume (mean absolute error (MAE) = 24.0 ± 32.9 ml) and path length (MAE = 0.80 ± 0.59 mm) on the held-out Paediatric-CT-SEG dataset, while also generalising well to external radiotherapy datasets (volume MAE = {18.9 ± 22.4, 34.6 ± 35.2} ml, and path length MAE = {0.52 ± 0.38, 0.67 ± 0.55} mm on the CYP and TYA datasets, respectively). We found a lower Dice similarity coefficient for gas segmentations in TYA vs Paediatric-CT-SEG and CYP (0.56 ± 0.24 vs 0.87 ± 0.09 and 0.84 ± 0.16, respectively), reflecting cohort-specific gas morphology differences. When applied longitudinally, the network detected both monotonic and realistic intrasubject GI gas variation. Proof-of-concept evaluation on clinical radiographs showed promising performance (volume MAE = 29.5 ± 25.9 ml, and path length MAE = 1.10 ± 0.75 mm). Significance. DL-powered radiographs quantified internal GI gas changes with promising performance and generalisability to diverse ages and demographics. This was the first step towards a novel personalised image-guided radiotherapy traffic-light workflow for abdominal PBT with the goal of reducing the need for CBCT to required fractions.

Objective: This study aims to investigate the impact of the beam temporal profile on the radical dynamics and inter-track interactions of FLASH radiotherapy, supporting parameter optimization for the equipment development, radio-biological experiments and clinical implementation. Approach: Monte-Carlo simulations based on the independent reaction time (IRT) method were performed to analyze the dynamics after irradiation, including single-pulse or multi-pulses irradiation, pulse repetition rate, pulse width and dose. The physicochemical experiments were performed to measure the hydrated electron lifetimes for validation. The generation and recombination of hydroxyl radicals and hydrated electrons were recorded under 6 MeV electron irradiation with varying beam temporal profiles. The radial distributions of the radicals were statistically analyzed, and the inter-track interactions were assessed through a mathematical model. Main results: The spatial distribution and temporal evolution of radicals were significantly affected by the beam temporal profiles. Compared with multi-pulses irradiation, single-pulse irradiation mode with a pulse width less than 1/10 of the radical lifetime, a repetition interval longer than the radical lifetime, and a dose exceeding 1 Gy/pulse can lead to rapid consumption of radicals within the first 30% of their lifetime, hence reduced the residual radical content. Instantaneous high dose rates induced overlapping of radical tracks. When the single-pulse dose exceeded 1 Gy, the overlap probability approached 100%, aligning with the dose threshold for the instantaneous radical combination. Significance: Under a low-duty cycle and high instantaneous dose-rate temporal profile, the radicals were rapidly consumed through track overlap, affecting FLASH effect. The optimized temporal profile can be used to guide the development of equipment and parameter settings in clinical practice to maximize the FLASH effect, such as the laser accelerators and superconducting photocathode guns.