Impact of TPS calculation algorithms on dose delivered to the patient in proton therapy treatments

Molinelli, S.; Russo, Stefania; Magro, Giuseppe; Maestri, Davide; Mairani, A.; Mastella, E.; Mirandola, A.; Vai, Alessandro; Vischioni, Barbara; Valvo, Francesca; Ciocca, Mario

doi:10.1088/1361-6560/ab0a4d

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…Several physical characteristics of a proton beam, such as a beam energy-dependent Bragg peak and a beam range, render dosimetric advantages to proton beams over the conventional photon therapy. Similar to the implementation of photon therapy, accurate commissioning, dosimetric verification of the treatment planning system (TPS), and quality assurance are fundamental for implementing high-quality proton therapy [3][4][5]. To verify the proton dose, various detectors, such as an ionization chamber, a radiochromic film, a metal oxide-semiconductor field-effect transistor (MOSFET) detector, a thermoluminescent dosimeter (TLD), an optically stimulated luminescence dosimeter (OSLD), a diamond detector, and a radiophotoluminescent glass dosimeter (RGD), have been investigated and used [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Dosimetric response of a glass dosimeter in proton beams: LET-dependence and correction factor

et al. 2021

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A radiophotoluminescent glass dosimeter (RGD) is widely used in postal audit system for photon beams in Japan. However, proton dosimetry in RGDs is scarcely used owing to a lack of clarity in their response to beam quality. In this study, we investigated RGD response to beam quality for establishing a suitable linear energy transfer (LET)-corrected dosimetry protocol in a therapeutic proton beam.The RGD response was compared with ionization chamber measurement for a 100-225 MeV passive proton beam. LET of the measurement points was calculated by the Monte Carlo method. An LET-correction factor, defined as a ratio between the non-corrected RGD dose and ionization chamber dose, of 1.226 × (LET) − 0.171 was derived for the RGD response. The magnitude of the LET-dependence of RGD increased with LET; for an LET of 8.2 keV/μm, the RGD under-response was up to 16%. The coefficient of determination, mean difference ± SD of non-corrected RGD dose, residual range-corrected RGD dose, and LET-corrected RGD dose to the ionization chamber are 0.923, 3.7 ± 4.2%, − 2.4 ± 7.5%, and 0.04 ± 2.1%, respectively. The LET-corrected RGD dose was within 5% of the corresponding ionization chamber dose at all energies until 200 MeV, where it was 5.3% lower than the ionization chamber dose.A corrected LET-dependence of RGD using a correction factor based on a power function of LET and precise dosimetric verification close to the maximum LET were realized here. We further confirmed establishment of an accurate postal audit under various irradiation conditions.

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Section: Introductionmentioning

confidence: 99%

Dosimetric response of a glass dosimeter in proton beams: LET-dependence and correction factor

et al. 2021

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“…Radiotherapy treatment plans are based on medical imaging from which CT is the most used technique for both tissue delineation and dose calculation 1,2 . Modern dose calculation algorithms can precisely calculate dose distributions and optimize treatment plans to deliver a highly conformal dose to the patient using material composition and density extracted from CT images as input 3–5 . In addition to dose calculation, CT images are often used for manual and automated tissue delineation, 6 surface tracking during treatment, 7 prediction on portal imaging and treatment verification 8 .…”

Section: Introductionmentioning

confidence: 99%

“… 1 , 2 Modern dose calculation algorithms can precisely calculate dose distributions and optimize treatment plans to deliver a highly conformal dose to the patient using material composition and density extracted from CT images as input. 3 , 4 , 5 In addition to dose calculation, CT images are often used for manual and automated tissue delineation, 6 surface tracking during treatment, 7 prediction on portal imaging and treatment verification. 8 Therefore, the accuracy of CT reconstruction regarding body contours and accurate Hounsfield unit (HU) can affect several aspects of radiotherapy treatments.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of novel AI‐based extended field‐of‐view CT reconstructions

et al. 2021

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Purpose: Modern computed tomography (CT) scanners have an extended field-of-view (eFoV) for reconstructing images up to the bore size, which is relevant for patients with higher BMI or nonisocentric positioning due to fixation devices. However, the accuracy of the image reconstruction in eFoV is not well known since truncated data are used. This study introduces a new deep learningbased algorithm for extended field-of-view reconstruction and evaluates the accuracy of the eFoV reconstruction focusing on aspects relevant for radiotherapy. Methods: A life-size three-dimensional (3D) printed thorax phantom, based on a patient CT for which eFoV was necessary, was manufactured and used as reference. The phantom has holes allowing the placement of tissue mimicking inserts used to evaluate the Hounsfield unit (HU) accuracy. CT images of the phantom were acquired using different configurations aiming to evaluate geometric and HU accuracy in the eFoV. Image reconstruction was performed using a state-of-the-art reconstruction algorithm (HDFoV), commercially available, and the novel deep learning-based approach (HDeepFoV). Five patient cases were selected to evaluate the performance of both algorithms on patient data. There is no ground truth for patients so the reconstructions were qualitatively evaluated by five physicians and five medical physicists. Results: The phantom geometry reconstructed with HDFoV showed boundary deviations from 1.0 to 2.5 cm depending on the volume of the phantom outside the regular scan field of view. HDeepFoV showed a superior performance regardless of the volume of the phantom within eFOV with a maximum boundary deviation below 1.0 cm. The maximum HU (absolute) difference for soft issue inserts is below 79 and 41 HU for HDFoV and HDeepFoV, respectively. HDeepFoV has a maximum deviation of À18 HU for an inhaled lung insert while HDFoV reached a 229 HU difference. The qualitative evaluation of patient cases shows that the novel deep learning approach produces images that look more realistic and have fewer artifacts. Conclusion: To be able to reconstruct images outside the sFoV of the CT scanner there is no alternative than to use some kind of extrapolated data. In our study, we proposed and investigated a new deep learning-based algorithm and compared it to a commercial solution for eFoV reconstruction. The deep learning-based algorithm showed superior performance in quantitative evaluations based on phantom data and in qualitative assessments of patient data.

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“…The TPS currently in use at CNAO is Raystation v.8B [64], which will be soon updated to v.10B. All versions of the TPS are commissioned against experimental measurements and an independent Monte Carlo system (Fluka) to ascertain dosimetric accuracy in homogeneous and inhomogeneous conditions before clinical implementation [65]. The new version of the TPS will allow fast Monte Carlo calculation of proton treatment plans and multi-model RBE-weighted dose optimization and evaluation of carbon ion plans.…”

Section: Treatment Planning Monte Carlo and Adaptive Protocols At Cnaomentioning

confidence: 99%

Hadron Therapy Achievements and Challenges: The CNAO Experience

Rossi

2022

Physics

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Protons and carbon ions (hadrons) have useful properties for the treatments of patients affected by oncological pathologies. They are more precise than conventional X-rays and possess radiobiological characteristics suited for treating radio-resistant or inoperable tumours. This paper gives an overview of the status of hadron therapy around the world. It focusses on the Italian National Centre for Oncological Hadron therapy (CNAO), introducing operation procedures, system performance, expansion projects, methodologies and modelling to build individualized treatments. There is growing evidence that supports safety and effectiveness of hadron therapy for a variety of clinical situations. However, there is still a lack of high-level evidence directly comparing hadron therapy with modern conventional radiotherapy techniques. The results give an overview of pre-clinical and clinical research studies and of the treatments of 3700 patients performed at CNAO. The success and development of hadron therapy is strongly associated with the creation of networks among hadron therapy facilities, clinics, universities and research institutions. These networks guarantee the growth of cultural knowledge on hadron therapy, favour the efficient recruitment of patients and present available competences for R&D (Research and Development) programmes.

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Impact of TPS calculation algorithms on dose delivered to the patient in proton therapy treatments

Cited by 8 publications

References 27 publications

Dosimetric response of a glass dosimeter in proton beams: LET-dependence and correction factor

Dosimetric response of a glass dosimeter in proton beams: LET-dependence and correction factor

Evaluation of novel AI‐based extended field‐of‐view CT reconstructions

Hadron Therapy Achievements and Challenges: The CNAO Experience

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