Liyong Lin

Background: Currently, proton therapy is the main radiation treatment modality that can treat deeply seated targets at ultra‐high dose rates. The safe translation of FLASH RT into clinic requires dedicated dosimeters capable of measurements at sufficiently high spatiotemporal resolution. Purpose: The objective of this work is to demonstrate the feasibility of three‐dimensional (3D) measurements of dose rate and dose for FLASH pencil beam scanning (PBS) proton therapy. Methods: A multi‐layer strip ionization chamber (MLSIC) device, along with a reconstruction algorithm, was designed and developed to reconstruct dose and dose rate distribution over a 3D volume. Our MLSIC is composed of 66 layers of strip ionization chamber arrays with total water‐equivalent thickness (WET) of 19.2 cm along the beam direction. The first two layers, composed of 128 channels with orthogonal direction with respect to each other, provide the ( x,y ) coordinate. The other 64 layers contain 32 channels with 8 mm lateral spacing. Data readout at a high‐speed of 6250 fps allows spot‐by‐spot measurement. To prove the concept, PBS proton therapy plans were delivered at conventional and FLASH dose rates. Dose and dose rate information were reconstructed in 3D using an in‐house reconstruction algorithm. Results: Ion recombination remained under 1% in the majority of cases. 3D dose reconstruction showed agreement with the treatment planning software; the 3D gamma analysis of the reconstructed dose showed 96.2% (5 mm/5%) and 86.8% (3 mm/3%) passing rates with 10% threshold for the conventional dose rate plan, 99.1% (5 mm/5%) and 92.6% (3 mm/3%) passing rates for the FLASH dose rate plan. 3D dose rate distributions were successfully generated using different definitions. Conclusions: Our MLSIC device allows obtaining 3D dose and dose rate distribution of PBS proton beams at FLASH dose rates with high spatiotemporal resolution.

Background/Objectives: Proton therapy delivers highly conformal doses to the target area without producing an exit dose, minimizing cumulative doses to healthy liver tissue. This study aims to evaluate current practices, challenges, and variations in the implementation of proton stereotactic body radiation therapy (SBRT) and hypofractionated therapy for liver malignancies, with the goal of providing a technical assessment to promote broader adoption and support future clinical trials. Methods and Materials: An extensive survey was conducted by NRG Oncology across North American proton treatment centers to assess the current practices of proton liver SBRT and hypofractionated therapy. The survey focused on key aspects, including patient selection, prescription and normal tissue constraints, simulation and motion management, treatment planning, quality assurance (QA), treatment delivery, and the use of image-guided radiation therapy (IGRT). Results: This survey captures the current practice patterns and status of proton SBRT and hypofractionated therapy in liver cancer treatment. Proton therapy is increasingly preferred for treating inoperable liver malignancies due to its ability to minimize healthy tissue exposure. However, the precision required for proton therapy presents challenges, particularly in managing uncertainties and target motion during high-dose fractions and short treatment courses. Survey findings revealed significant variability in clinical practices across centers, highlighting differences in motion management, dose fractionation schedules, and QA protocols. Conclusion: Proton SBRT and hypofractionated therapy offer significant potential for treating liver malignancies. A comprehensive approach involving precise patient selection, treatment planning, and QA is essential for ensuring safety and effectiveness. This survey provides valuable insights into current practices and challenges, offering a foundation for technical recommendations to optimize the use of proton therapy and guide future clinical trials.

Very fast imaging techniques can enhance the precision of image-guided radiation therapy, which can be useful for external beam radiation therapy. This work aims to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for treating lung cancer patients with gating, and it is presented in the context of FLASH which leverages ultrahigh dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. The proposed framework comprises four modules, including orthogonal kV X-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and proton water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a 4-D computed tomography (CT) dataset with ten respiratory phases. Considering all evaluation metrics, the kV projections with source angles of 135° and 225° yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were $75\pm 22$ hounsfield unit, $19\pm 3$ .7 dB, $0.938\pm 0.044$ , and −1.3%±4.1%. The proposed framework can rapidly deliver volumetric images to potentially guide proton FLASH treatment delivery systems.

Objective. In current clinical practice for quality assurance (QA), intensity modulated proton therapy (IMPT) fields are verified by measuring planar dose distributions at one or a few selected depths in a phantom. A QA device that measures full 3D dose distributions at high spatiotemporal resolution would be highly beneficial for existing as well as emerging proton therapy techniques such as FLASH radiotherapy. Our objective is to demonstrate feasibility of 3D dose measurement for IMPT fields using a dedicated multi-layer strip ionization chamber (MLSIC) device. Approach. Our developed MLSIC comprises a total of 66 layers of strip ion chamber (IC) plates arranged, alternatively, in the x and y direction. The first two layers each has 128 channels in 2 mm spacing, and the following 64 layers each has 32/33 IC strips in 8 mm spacing which are interconnected every eight channels. A total of 768-channel IC signals are integrated and sampled at a speed of 6 kfps. The MLSIC has a total of 19.2 cm water equivalent thickness and is capable of measurement over a 25 × 25 cm² field size. A reconstruction algorithm is developed to reconstruct 3D dose distribution for each spot at all depths by considering a double-Gaussian–Cauchy–Lorentz model. The 3D dose distribution of each beam is obtained by summing all spots. The performance of our MLSIC is evaluated for a clinical pencil beam scanning (PBS) plan. Main results. The dose distributions for each proton spot can be successfully reconstructed from the ionization current measurement of the strip ICs at different depths, which can be further summed up to a 3D dose distribution for the beam. 3D Gamma Index analysis indicates acceptable agreement between the measured and expected dose distributions from simulation, Zebra and MatriXX. Significance. The dedicated MLSIC is the first pseudo-3D QA device that can measure 3D dose distribution in PBS proton fields spot-by-spot.

Purpose: An accurate estimation of range uncertainties is essential to exploit the potential of proton therapy. According to Paganetti's study, a value of 2.4% (1.5 standard deviation) is currently recommended for planning robust treatments with Monte Carlo dose engines. This number is based on a dominant contribution from the mean excitation energy of tissues. However, it was recently shown that expressing tissues as a mixture of water and “dry” material in the CT calibration process allowed for a significant reduction of this uncertainty. We thus propose an adapted framework for pencil beam scanning robust optimization. First, we move towards a spot‐specific range uncertainty (SSRU) determination. Second, we use the water‐based formalism to reduce range uncertainties and, potentially, to spare better the organs at risk.Methods: The stoichiometric calibration was adapted to provide a molecular decomposition (including water) of each voxel of the CT. The SSRU calculation was implemented in MCsquare, a fast Monte Carlo dose engine dedicated to proton therapy. For each spot, a ray‐tracing method was used to propagate molecular I‐values uncertainties and obtain the corresponding effective range uncertainty. These were then combined with other sources of range uncertainties, according to Paganetti's study of 2012. The method was then assessed on three head‐and‐neck patients. Two plans were optimized for each patient: the first one with the classical 2.4% flat range uncertainty (FRU), the second one with the variable range uncertainty. Both plans were then compared in terms of target coverage and OAR mean dose reduction. Robustness evaluations were also performed, using the SSRU for both plans in order to simulate errors as realistically as possible.Results: For patient 1, it was found that the median SSRU was 1.04% (1.5 standard deviation), yielding, therefore, a very large reduction from the 2.4% FRU. All three SSRU plans were found to have a very good robustness level at a 90% confidence interval while sparing OAR better than the classical plan. For instance, in nominal cases, average reductions in the mean dose of 15.7, 8.4, and 13.2% were observed in the left parotid, right parotid, and pharyngeal constrictor muscle, respectively. As expected, the classical plans showed a higher but unnecessary level of robustness.Conclusions: Promising results of the SSRU framework were observed on three head‐and‐neck cases, and more patients should now be considered. The method could also benefit to other tumor sites and, in the long run, the variable part of the range uncertainty could be generalized to other sources of uncertainty in order to move towards more and more patient‐specific treatments.

This work of fiction is part of a case study series developed by the Medical Physics Leadership Academy (MPLA). It is intended to facilitate the discussion of how students and advisors can better communicate expectations and navigate difficult conversations. In this case, a fourth‐year Ph.D. student Emma learns that her advisor Dr. So is leaving the institution and has not arranged to bring any students with him. As Emma and Dr. So meet to discuss Emma's next steps, the conversation reveals misunderstandings and miscommunications of expectations, including a specific publication requirement for graduation from Dr. So. Having just learned of Dr. So's publication requirement, Emma realizes that graduating before the lab shuts down is not feasible. The intended use of this case, through group discussion or self‐study, is to encourage readers to discuss the situation at hand and inspire professionalism and leadership thinking. This case study falls under the scope of and is supported by the MPLA, a committee in the American Association of Physicists in Medicine (AAPM).

Purpose: Metallic implants have been correlated to local control failure for spinal sarcoma and chordoma patients due to the uncertainty of implant delineation from computed tomography (CT). Such uncertainty can compromise the proton Monte Carlo dose calculation (MCDC) accuracy. A component method is proposed to determine the dimension and volume of the implants from CT images.Methods: The proposed component method leverages the knowledge of surgical implants from medical supply vendors to predefine accurate contours for each implant component, including tulips, screw bodies, lockers, and rods. A retrospective patient study was conducted to demonstrate the feasibility of the method. The reference implant materials and samples were collected from patient medical records and vendors, Medtronic and NuVasive. Additional CT images with extensive features, such as extended Hounsfield units and various reconstruction diameters, were used to quantify the uncertainty of implant contours.Results: For in vivo patient implant estimation, the reference and the component method differences were 0.35, 0.17, and 0.04 cm³for tulips, screw bodies, and rods, respectively. The discrepancies by a conventional threshold method were 5.46, 0.76, and 0.05 cm³, respectively. The mischaracterization of implant materials and dimensions can underdose the clinical target volume coverage by 20 cm³for a patient with eight lumbar implants. The tulip dominates the dosimetry uncertainty as it can be made from titanium or cobalt–chromium alloys by different vendors.Conclusions: A component method was developed and demonstrated using phantom and patient studies with implants. The proposed method provides more accurate implant characterization for proton MCDC and can potentially enhance the treatment quality for proton therapy. The current proof‐of‐concept study is limited to the implant characterization for lumbar spine. Future investigations could be extended to cervical spine and dental implants for head‐and‐neck patients where tight margins are required to spare organs at risk.

In this paper, we consider the problem of synthesizing the supremal covert damage-reachable attacker, in the setup where the model of the supervisor is unknown to the adversary but the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system. The synthesized attacker needs to ensure both the damage-reachability and the covertness against all the supervisors which are consistent with the given set of observations. There is a gap between the de facto supremality, assuming the model of the supervisor is known, and the supremality that can be attained with a limited knowledge of the model of the supervisor, from the adversary's point of view. We consider the setup where the attacker can exercise sensor replacement/deletion attacks and actuator enablement/disablement attacks. The solution methodology proposed in this work is to reduce the synthesis of the supremal covert damage-reachable attacker, given the model of the plant and the finite set of observations, to the synthesis of the supremal safe supervisor for certain transformed plant, which shows the decidability of the observation-assisted covert attacker synthesis problem. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.