Advances in Proton Therapy, Using Virtual CT, Reduce Nasal and Brain Tumor Range Uncertainty to 0.28%

Proton therapy represents a significant advancement in cancer treatment, yet accurately determining how far protons travel through the body remains a key challenge, as range uncertainty can compromise treatment effectiveness. Researchers led by S. Vrbaški, G. Stanić, and S. Mollineli, from their respective institutions, now demonstrate a novel approach to address this issue using virtual photon-counting CT scans and advanced tissue characterization. The team developed a simulation environment to validate methods for calculating stopping power ratio, a crucial factor in determining proton range, and compared a standard technique with a new software tool called TissueXplorer. Results show that TissueXplorer predicts stopping power with remarkable accuracy, exhibiting a mean percentage difference of only 0.28% across various head tissues, and importantly, leads to more accurate dose distribution compared to conventional methods, potentially improving the precision and efficacy of proton therapy for challenging tumours.

Virtual Simulators Validate Photon-Counting CT Accuracy

Hadron therapy, particularly proton therapy, demands precise knowledge of how the beam stops within the patient to effectively target tumours while protecting healthy tissue. Current methods relying on computed tomography (CT) scans have limitations in accuracy. Photon-counting CT, a new imaging technology, promises reduced noise and direct measurement of photon energy, potentially improving tissue stopping power calculations. Researchers developed virtual imaging simulators to evaluate beam range uncertainty in complex patient anatomies, addressing the challenges of validating improvements in realistic scenarios.

This approach enables systematic investigation of how imaging techniques affect treatment planning accuracy. The team created a realistic simulation environment to complement traditional phantom-based experiments and comprehensively assess the benefits of photon-counting CT. This work proposes a novel method for validating beam range uncertainty, extending beyond physical phantoms to explore complex clinical cases.

PCCT Improves Proton Therapy Stopping Power Prediction

This research investigates photon-counting computed tomography (PCCT) to improve the accuracy of proton therapy treatment planning. The study focuses on reducing uncertainties in determining the stopping power ratio (SPR), a crucial factor in calculating proton travel through tissue. Researchers used a simulation framework to model PCCT imaging and compared its performance to conventional CT for SPR prediction and treatment planning accuracy, validating the framework against established methods and realistic anatomical models. The team’s simulations suggest that PCCT has the potential to improve SPR prediction compared to conventional CT, potentially reducing range uncertainty in proton therapy planning. The developed simulation framework, DukeSim, was successfully validated, demonstrating its reliability for research and development of PCCT-based treatment planning. This highlights the value of realistic simulations for evaluating new imaging technologies in radiation therapy.

Virtual Imaging Validates Proton Therapy Accuracy

Scientists developed virtual imaging simulators to validate beam range uncertainty in complex patient anatomy, offering an alternative to traditional experimental methods. Utilizing a detailed model of a human head and a photon-counting CT scanner, the research team assessed the accuracy of stopping power ratio (SPR) calculations, crucial for precise proton therapy. Validation involved comparing SPR estimations from a conventional method with a new software solution, TissueXplorer. The team measured a mean percentage difference of 0.28% in estimating the stopping power ratio with TissueXplorer across all head tissues, demonstrating high accuracy. Results demonstrate that SPR values obtained with TissueXplorer yielded smaller dose distribution differences compared to the conventional method, indicating that software leveraging spectral information holds significant promise for more accurate SPR prediction. This delivers a virtual imaging framework for validating SPR prediction from CT imaging and assessing its impact on dose distribution, potentially improving the precision and effectiveness of proton therapy.

Virtual Imaging Validates Proton Therapy Planning Tools

This research demonstrates the potential of virtual imaging as a valuable method for validating the accuracy of stopping power ratio (SPR) predictions derived from computed tomography (CT) scans, with direct implications for proton therapy treatment planning. Scientists successfully employed a virtual imaging simulator, utilizing a detailed model of the human head and a photon-counting CT scanner, to assess SPR calculations and validate a new software solution, TissueXplorer, against conventional methods. TissueXplorer achieves a mean percentage difference of only 0.28% when estimating the stopping power ratio across various head tissues, significantly improving upon the accuracy of traditional techniques. This enhanced precision translates to smaller discrepancies in calculated dose distributions, suggesting improved potential for accurate tumour targeting and sparing of healthy tissue. While this study employed a computational head phantom, the results support further investigation into spectral imaging solutions like TissueXplorer for more reliable SPR prediction in clinical settings.