An end-to-end assessment of range uncertainty in proton therapy using animal tissues

Investigating beam range uncertainty in proton prostate treatment using pelvic-like biological phantoms

This study aims to develop a method for verifying site-specific and/or beam path specific proton beam range, which could reduce range uncertainty margins and the associated treatment complications. It investigates the range uncertainties from both CT HU to relative stopping power conversion and patient positioning errors for prostate treatment using pelvic-like biological phantoms. Three 25 × 14 × 12 cm³phantoms, made of fresh animal tissues mimicking the pelvic anatomies of prostate patients, were scanned with a general electric CT simulator. A 22 cm circular passive scattering beam with 29 cm range and 8 cm modulation width was used to measure the water equivalent path lengths (WEPL) through the phantoms at multiple points using the dose extinction method with a MatriXXPT detector. The measured WEPLs were compared to those predicted by TOPAS simulations and ray-tracing WEPL calculations. For the three phantoms, the WEPL differences between measured and theoretical prediction (WDMT) are below 1.8% for TOPAS, and 2.5% for ray-tracing. WDMT varies with phantom anatomies by about 0.5% for both TOPAS and ray-tracing. WDMT also correlates with the tissue types of a specific treated region. For the regions where the proton beam path is parallel to sharp bone edges, the WDMTs of TOPAS and ray-tracing respectively reach up to 1.8% and 2.5%. For the region where proton beams pass through just soft tissues, the WDMT is mostly less than 1% for both TOPAS and ray-tracing. For prostate treatments, range uncertainty depends on the tissue types within a specific treated region, patient anatomies and the range calculation methods in the planning algorithms. Our study indicates range uncertainty is less than 2.5% for the whole treated region with both ray-tracing and TOPAS, which suggests the potential to reduce the current 3.5% range uncertainty margin used in the clinics by at least 1% even for single-energy CT data.

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning

Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been proposed, techniques that include estimation of uncertainty are limited. This study proposes a novel uncertainty-aware pCT image correction method using a Bayesian convolutional neural network (BCNN). A DenseNet-based BCNN was constructed to predict both a corrected SPR image and its uncertainty from a noisy SPR image. A total 432 noisy SPR images of 6 non-anthropomorphic and 3 head phantoms were collected with Monte Carlo simulations, while true noise-free images were calculated with known geometric and chemical components. Heteroscedastic loss and deep ensemble techniques were performed to estimate aleatoric and epistemic uncertainties by training 25 unique BCNN models. 200-epoch end-to-end training was performed for each model independently. Feasibility of the predicted uncertainty was demonstrated after applying two post-hoc calibrations and calculating spot-specific path length uncertainty distribution. For evaluation, accuracy of head SPR images and water-equivalent thickness (WET) corrected by the trained BCNN models was compared with a conventional method and non-Bayesian CNN model. BCNN-corrected SPR images represent noise-free images with high accuracy. Mean absolute error in test data was improved from 0.263 for uncorrected images to 0.0538 for BCNN-corrected images. Moreover, the calibrated uncertainty represents accurate confidence levels, and the BCNN-corrected calibrated WET was more accurate than non-Bayesian CNN with high statistical significance. Computation time for calculating one image and its uncertainties with 25 BCNN models is 0.7 s with a consumer grade GPU. Our model is able to predict accurate pCT images as well as two types of uncertainty. These uncertainties will be useful to identify potential cause of SPR errors and develop a spot-specific range margin criterion, toward elaboration of uncertainty-guided proton therapy.

A Probability-Based Investigation on the Setup Robustness of Pencil-beam Proton Radiation Therapy for Skull-Base Meningioma

Introduction

The intracranial skull-base meningioma is in proximity to multiple critical organs and heterogeneous tissues. Steep dose gradients often result from avoiding critical organs in proton treatment plans. Dose uncertainties arising from setup errors under image-guided radiation therapy are worthy of evaluation.

Patients and Methods

Fourteen patients with skull-base meningioma were retrospectively identified and planned with proton pencil beam scanning (PBS) single-field uniform dose (SFUD) and multifield optimization (MFO) techniques. The setup uncertainties were assigned a probability model on the basis of prior published data. The impact on the dose distribution from nominal 1-mm and large, less probable setup errors, as well as the cumulative effect, was analyzed. The robustness of SFUD and MFO planning techniques in these scenarios was discussed.

Results

The target coverage was reduced and the plan dose hot spot increased by all setup uncertainty scenarios regardless of the planning techniques. For 1 mm nominal shifts, the deviations in clinical target volume (CTV) coverage D99% was -11 ± 52 cGy and -45 ± 147 cGy for SFUD and MFO plans. The setup uncertainties affected the organ at risk (OAR) dose both positively and negatively. The statistical average of the setup uncertainties had <100 cGy impact on the plan qualities for all patients. The cumulative deviations in CTV D95% were 1 ± 34 cGy and -7 ± 18 cGy for SFUD and MFO plans.

Conclusion

It is important to understand the impact of setup uncertainties on skull-base meningioma, as the tumor target has complex shape and is in proximity to multiple critical organs. Our work evaluated the setup uncertainty based on its probability distribution and evaluated the dosimetric consequences. In general, the SFUD plans demonstrated more robustness than the MFO plans in target coverages and brainstem dose. The probability-weighted overall effect on the dose distribution is small compared to the dosimetric shift during single fraction.

Carbon-11 and Carbon-12 beam range verifications through prompt gamma and annihilation gamma measurements: Monte Carlo simulations

Range uncertainty remains a big concern in particle therapy, as it may cause target dose degradation and normal tissue overdosing. Positron emission tomography (PET) and prompt gamma imaging (PGI) are two promising modalities for range verification. However, the relatively long acquisition time of PET and the relatively low yield of PGI pose challenges for real-time range verification. In this paper, we explore using the primary Carbon-11 (C-11) ion beams to enhance the gamma yield compared to the primary C-12 ion beams to improve PET and PGI by using Monte Carlo simulations of water and PMMA phantoms at four incident energies (95, 200, 300, and 430 MeV u-1). Prompt gammas (PGs) and annihilation gammas (AGs) were recorded for post-processing to mimic PGI and PET imaging, respectively. We used both time-of-flight (TOF) and energy selections for PGI, which boosted the ratio of PGs to background neutrons to 2.44, up from 0.87 without the selections. At the lowest incident energy (100 MeVu-1), PG yield from C-11 was 0.82 times of that from C-12, while AG yield from C-11 was 6 ∼ 11 folds higher than from C-12 in PMMA. At higher energies, PG differences between C-11 and C-12 were much smaller, while AG yield from C-11 was 30%∼90% higher than from C-12 using minute-acquisition. With minute-acquisition, the AG depth distribution of C-11 showed a sharp peak coincident with the Bragg peak due to the decay of the primary C-11 ions, but that of C-12 had no such one. The high AG yield and distinct peaks could lead to more precise range verification of C-11 than C-12. These results demonstrate that using C-11 ion beams for potentially combined PGI and PET has great potential to improve online single-spot range verification accuracy and precision.

Validation of the RayStation Monte Carlo dose calculation algorithm using a realistic lung phantom

PURPOSE

Our purposes are to compare the accuracy of RaySearch's analytical pencil beam (APB) and Monte Carlo (MC) algorithms for clinical proton therapy and to present clinical validation data using a novel animal tissue lung phantom.

METHODS

We constructed a realistic lung phantom composed of a rack of lamb resting on a stack of rectangular natural cork slabs simulating lung tissue. The tumor was simulated using 70% lean ground lamb meat inserted in a spherical hole with diameter 40 ± 5 mm carved into the cork slabs. A single-field plan using an anterior beam and a two-field plan using two anterior-oblique beams were delivered to the phantom. Ion chamber array measurements were taken medial and distal to the tumor. Measured doses were compared with calculated RayStation APB and MC calculated doses.

RESULTS

Our lung phantom enabled measurements with the MatriXX PT at multiple depths in the phantom. Using the MC calculations, the 3%/3 mm gamma index pass rates, comparing measured with calculated doses, for the distal planes were 74.5% and 85.3% for the APB and 99.1% and 92% for the MC algorithms. The measured data revealed up to 46% and 30% underdosing within the distal regions of the target volume for the single and the two field plans when APB calculations are used. These discrepancies reduced to less than 18% and 7% respectively using the MC calculations.

CONCLUSIONS

RaySearch Laboratories' Monte Carlo dose calculation algorithm is superior to the pencil-beam algorithm for lung targets. Clinicians relying on the analytical pencil-beam algorithm should be aware of its pitfalls for this site and verify dose prior to delivery. We conclude that the RayStation MC algorithm is reliable and more accurate than the APB algorithm for lung targets and therefore should be used to plan proton therapy for patients with lung cancer.

Validation of the RayStation Monte Carlo dose calculation algorithm using realistic animal tissue phantoms

PURPOSE

The aim of this study is to validate the RayStation Monte Carlo (MC) dose algorithm using animal tissue neck phantoms and a water breast phantom.

METHODS

Three anthropomorphic phantoms were used in a clinical setting to test the RayStation MC dose algorithm. We used two real animal necks that were cut to a workable shape while frozen and then thawed before being CT scanned. Secondly, we made a patient breast phantom using a breast prosthesis filled with water and placed on a flat surface. Dose distributions in the animal and breast phantoms were measured using the MatriXX PT device.

RESULTS

The measured doses to the neck and breast phantoms compared exceptionally well with doses calculated by the analytical pencil beam (APB) and MC algorithms. The comparisons between APB and MC dose calculations and MatriXX PT measurements yielded an average depth difference for best gamma agreement of <1 mm for the neck phantoms. For the breast phantom better average gamma pass rates between measured and calculated dose distributions were observed for the MC than for the APB algorithms.

CONCLUSIONS

The MC dose calculations are more accurate than the APB calculations for the static phantoms conditions we evaluated, especially in areas where significant inhomogeneous interfaces are traversed by the beam.

Comprehensive clinical commissioning and validation of the RayStation treatment planning system for proton therapy with active scanning and passive treatment techniques

PURPOSE

To commission the treatment planning system (TPS) RayStation for proton therapy including beam models for spot scanning and for uniform scanning.

METHODS

Tests consist of procedures from ESTRO booklet number 7, the German DIN for constancy checks of TPSs, and extra tests checking the dose perturbation function. The dose distributions within patients were verified in silico by a comparison of 65 clinical treatment plans with the TPS XiO. Dose-volume parameters, dose differences, and three-dimensional gamma-indices serve as measures of similarity. The monthly constancy checks of Raystation have been automatized with a script.

RESULTS

The basic functionality of the software complies with ESTRO booklet number 7. For a few features minor enhancements are suggested. The dose distribution in RayStation agrees with the calculation in XiO. This is supported by a gamma-index (3mm/3%) pass rate of >98.9% (median over 59 plans) for the volume within the 20% isodose line and a difference of <0.3% of V₉₅ of the PTV (median over 59 plans). If spot scanning is used together with a range shifter, the dose level calculated by RayStation can be off by a few percent.

CONCLUSIONS

RayStation can be used for the creation of clinical proton treatment plans. Compared to XiO RayStation has an improved modelling of the lateral dose fall-off in passively delivered fields. For spot scanning fields with range shifter blocks an empirical adjustment of monitor units is required. The computation of perturbed doses also allows the evaluation of the robustness of a treatment plan.