A single detector energy-resolved proton radiography system: a proof of principle study by Monte Carlo simulations

A denoising method based on deep learning for proton radiograph using energy resolved dose function

Objective.Proton radiograph has been broadly applied in proton radiotherapy which is affected by scattered protons which result in the lower spatial resolution of proton radiographs than that of x-ray images. Traditional image denoising method may lead to the change of water equivalent path length (WEPL) resulting in the lower WEPL measurement accuracy. In this study, we proposed a new denoising method of proton radiographs based on energy resolved dose function curves.Approach.Firstly, the corresponding relationship between the distortion of WEPL characteristic curve, and energy and proportion of scattered protons was established. Then, to improve the accuracy of proton radiographs, deep learning technique was used to remove scattered protons and correct deviated WEPL values. Experiments on a calibration phantom to prove the effectiveness and feasibility of this method were performed. In addition, an anthropomorphic head phantom was selected to demonstrate the clinical relevance of this technology and the denoising effect was analyzed.Main results.The curves of WEPL profiles of proton radiographs became smoother and deviated WEPL values were corrected. For the calibration phantom proton radiograph, the average absolute error of WEPL values decreased from 2.23 to 1.72, the mean percentage difference of all materials of relative stopping power decreased from 1.24 to 0.39, and the average relative WEPL corrected due to the denoising process was 1.06%. In addition, WEPL values correcting were also observed on the proton radiograph for anthropomorphic head phantom due to this denoising process.Significance.The experiments showed that this new method was effective for proton radiograph denoising and had greater advantages than end-to-end image denoising methods, laying the foundation for the implementation of precise proton radiotherapy.

A comparative study of machine-learning approaches in proton radiography using energy-resolved dose function

PURPOSE

The feasibility of machine learning (ML) techniques and their performance compared to the conventional χ²-minimization technique in the context of the proton energy-resolved dose imaging method are presented.

MATERIALS AND METHOD

Various geometries resembling a wedge and varying gradients are simulated in GATE to obtain energy-resolved dose functions (ERDF) from proton beams of different energies. These ERDFs are used to predict the WEPL using a conventional technique and other ML-based methods. The results are compared to gain an understanding of the performance of ML models in proton radiography.

RESULTS

The results obtained from the χ²-minimization technique indicate that it is robust and more reliable compared to the ML-based techniques. It is also observed that the ML-based techniques did not mitigate the effect of range-mixing but seem to be more affected by it compared to the χ²-minimization technique. Substantial data reduction was required in order to make the results of ML-based methods comparable to that of χ²-minimization. We also note that such data reduction might not be possible in a clinical setting. The only advantage in using the ML-based technique is the computational time required to generate a WEPL map which, in our case study, is 10-30 times shorter than the time required for the conventional χ²-minimization technique.

CONCLUSIONS

The first results from this preliminary study indicate that the ML techniques failed to be on par with the conventional χ²-minimization technique in terms of the achievable accuracy in the predictions of WEPL and in the mitigation of range-mixing effects in the WEPL image. Modern strategies like the GAN-based models may be suitable for such applications.

Optimizing calibration settings for accurate water equivalent path length assessment using flat panel proton radiography

OBJECTIVE

Proton range uncertainties can compromise the effectiveness of proton therapy treatments. Water equivalent path length (WEPL) assessment by flat panel detector proton radiography (FP-PR) can provide means of range uncertainty detection. Since WEPL accuracy intrinsically relies on the FP-PR calibration parameters, the purpose of this study is to establish an optimal calibration procedure that ensures high accuracy of WEPL measurements. To that end, several calibration settings were investigated.

APPROACH

FP-PR calibration datasets were obtained simulating PR fields with different proton energies, directed towards water-equivalent material slabs of increasing thickness. The parameters investigated were the spacing between energy layers (ΔE) and the increment in thickness of the water-equivalent material slabs (ΔX) used for calibration. 30 calibrations were simulated, as a result of combining ΔE = 9, 7, 5, 3, 1 MeV and ΔX = 10, 8, 5, 3, 2, 1 mm. FP-PRs through a CIRS electron density phantom were simulated, and WEPL images corresponding to each calibration were obtained. Ground truth WEPL values were provided by range probing multi-layer ionization chamber simulations on each insert of the phantom. Relative WEPL errors between FP-PR simulations and ground truth were calculated for each insert. Mean relative WEPL errors and standard deviations across all inserts were computed for WEPL images obtained with each calibration.

MAIN RESULTS

Large mean and standard deviations were found in WEPL images obtained with large ΔEvalues (ΔE = 9 or 7 MeV), for any ΔX. WEPL images obtained with ΔE ≤ 5 MeV and ΔX ≤ 5 mm resulted in a WEPL accuracy with mean values within ±0.5% and standard deviations around 1%.

SIGNIFICANCE

An optimal FP calibration in the framework of this study was established, characterized by 3 MeV ≤ ΔE ≤ 5 MeV and 2 mm ≤ ΔX ≤ 5 mm. Within these boundaries, highly accurate WEPL acquisitions using FP-PR are feasible and practical, holding the potential to assist future online range verification quality control procedures.

Artificial intelligence supported single detector multi-energy proton radiography system

Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specific CT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus^®One accelerator was built at 200 and 220 MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4% ± 0.4% in our test dataset. For one example patient, detector dose to WET conversions were performed based on the total dose (ITotal), the primary proton dose (IPrimary), and the ProtonDSE corrected detector dose (ICorrected). The determined WET accuracy was compared with respect to the reference WET by idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was determined 4.3% ± 4.1% forWET(ITotal),2.2% ± 1.4% forWET(IPrimary),and 2.5% ± 2.0% forWET(ICorrected).

Feasibility of energy-resolved dose imaging technique in pencil beam scanning mode

Purpose:Proton energy-resolved dose imaging (pERDI) is a recently proposed technique to generate water equivalent path length (WEPL) images using a single detector. Owing to its simplicity in instrumentation, analysis and the possibility of using the in-room x-ray flat panels as detectors, this technique offers a promising avenue towards a clinically usable imaging system for proton therapy using scanned beams. The purpose of this study is to estimate the achievable accuracy in WEPL and Relative Stopping Power (RSP) using the pERDI technique and to assess the minimum dose required to achieve such accuracy. The novelty of this study is the first demonstration of the feasibility of pERDI technique in the pencil beam scanning (PBS) mode.Methods:A solid water wedge was placed in front of a 2D detector (Lynx). A library of energy-resolved dose functions (ERDF) was generated from the dose deposited in the detector by 50 PBS layers of energy varying from 100 MeV to 225 MeV. This set-up is further used to image the following configurations using the pERDI technique: stair-case shaped solid water phantom (configuration 1), electron density phantom (configuration 2) and head phantom (configuration 3). The result from configuration 1 was used to determine the achievable WEPL accuracy. The result from configuration 2 was used to estimate the relative uncertainty in RSP. Configuration 3 was used to evaluate the effect of range mixing on the WEPL. In all three cases, the variation of the accuracy with respect to dose, by varying the number of scanning layers, was also studied.Results:An accuracy of 1 mm in WEPL was achieved using the Lynx detector with an imaging field of 10 PBS layers or more, which is equivalent to a total dose of 5 cGy. The RSP is measured with a precision better than 2% for all homogeneous inserts of tissue surrogates. The pERDI technique failed for tissues surrogates with total WEPL outside the calibration window (WEPL < 70 mm) like in the case of lung exhale and lung inhale. The imaging of an anthropomorphic head phantom, in the same condition, produced a WEPL radiograph and compared to the WEPL derived from CT using gamma index analysis. The gamma index failed in the heterogeneous areas due to range mixing.Conclusions:The pERDI technique is a promising clinically usable imaging modality for reducing range uncertainties and set-up errors in proton therapy. The first results have demonstrated that WEPL and RSP can be estimated with clinically acceptable accuracy using the Lynx detector. Similar accuracy is also expected with in-room flat-panel detectors but at significantly reduced imaging dose. Though the issue of range mixing is still to be addressed, we expect that a statistical moment analysis of the ERDFs can be implemented to filter out the regions with high gradient of range mixing.

The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research

PURPOSE

This paper covers recent developments and applications of the TOPAS TOol for PArticle Simulation and presents the approaches used to disseminate TOPAS.

MATERIALS AND METHODS

Fundamental understanding of radiotherapy and imaging is greatly facilitated through accurate and detailed simulation of the passage of ionizing radiation through apparatus and into a patient using Monte Carlo (MC). TOPAS brings Geant4, a reliable, experimentally validated MC tool mainly developed for high energy physics, within easy reach of medical physicists, radiobiologists and clinicians. Requiring no programming knowledge, TOPAS provides all of the flexibility of Geant4.

RESULTS

After 5 years of development followed by its initial release, TOPAS was subsequently expanded from its focus on proton therapy physics to incorporate radiobiology modeling. Next, in 2018, the developers expanded their user support and code maintenance as well as the scope of TOPAS towards supporting X-ray and electron therapy and medical imaging. Improvements have been achieved in user enhancement through software engineering and a graphical user interface, calculational efficiency, validation through experimental benchmarks and QA measurements, and either newly available or recently published applications. A large and rapidly increasing user base demonstrates success in our approach to dissemination of this uniquely accessible and flexible MC research tool.

CONCLUSIONS

The TOPAS developers continue to make strides in addressing the needs of the medical community in applications of ionizing radiation to medicine, creating the only fully integrated platform for four-dimensional simulation of all forms of radiotherapy and imaging with ionizing radiation, with a design that promotes inter-institutional collaboration.