3D Dose Distributions Research Articles

Skin Dose Distributions from Total Skin Electron Irradiation Therapy (TSET)

Skin Dose Distributions from Total Skin Electron Irradiation Therapy (TSET)

Automatic Prediction of 3D Radiation Dose Distribution in Prostate Cancer Treated with Volumetric Modulated Arc Therapy (VMAT) Using a Conditional Generative Adversarial Network (cGAN)

VMAT is a commonly used technique for the treatment of prostate cancer, but the planning process can be time-consuming due to the inherent complexity of the iterative optimizations. In this study, we aim to show the efficacy of a conditional generative adversarial network (cGAN) in automatically predicting the 3D radiation dose distribution trained with previously delivered plans.In an IRB-approved analysis, we obtained the data of 167 patients with prostate adenocarcinoma who received definitive RT at our institution. All included cases were planned with VMAT technique with a prescribed dose of 78 Gy in 39 fx or 79.2 Gy in 44 fx to the prostate gland +/- seminal vesicles. For each patient, the inputs to the model were 6 structure masks (PTV and 5 OARs including bladder, rectum, penile bulb, and femoral heads), all resampled to voxel size of 2.5mm x 2.5mm x 3.0mm to be consistent with the grid size for dose, and the model predicts a 3D radiation dose distribution. The cGAN's generator was a modified 3D U-net, and its discriminator was a 5-layer 3D convolutional neural network. For training, mean absolute error (MAE) and binary cross entropy were used as the loss functions for the generator and discriminator, respectively, with Adam as the optimizers. Forty cases were randomly selected for testing only, and the remaining 127 formed the training set. The algorithm was implemented in Python 3.8 with PyTorch 1.7 as the framework. Model training was performed on NVIDIA Tesla V100 GPU. Model performance was assessed by comparing dosimetric metrics (based on institutional and RTOG 0415 guidelines) between the predicted doses (normalized to PTV maximal dose of 107%) and clinical doses with two-tailed paired t-test.The model was trained for 300 epochs. On the independent test set, the mean MAE for the predicted 3D dose distribution was 1.6 Gy per voxel (range, 0.9 - 4.5 Gy). Dosimetric comparison (Table 1) demonstrated that the predicted doses had similar to improved PTV coverage and comparable dose to OARs, except for the femoral heads for which the model predicted higher D5's that still fell within dose constraint of < 50 Gy.We demonstrated that, with the structures of the PTV and OARs as inputs, the 3D cGAN is excellent at the rapid prediction of radiation dose distributions of VMAT plans for prostate cancer that closely resemble clinically delivered doses. With further work on the deliverability of the predicted doses, our method can significantly increase the efficiency of treatment planning.

Association Between Radiation Heart Dose and Overall Survival in Lung Stereotactic Body Radiation Therapy

<h3>Purpose/Objective(s)</h3> To determine if heart radiation dose and location affects overall survival (OS) in patients treated with Stereotactic Body Radiation Therapy (SBRT) to the lung. <h3>Materials/Methods</h3> Patients treated with SBRT to the lung at a single institution were identified. Pertinent information regarding patient characteristics, treatment delivered, and patient outcomes were obtained from a prospectively maintained cancer registry and the medical record. Radiation treatment information was obtained from the treatment planning system. Radiation dose in each voxel was extracted from the treatment planning system, mapped on anatomical organs, and converted to 2 Gy equivalent dose using linear quadratic model. The V% dosimetric variable, a percentage of volume receiving the dose ‘D' or greater, was used in the statistical analysis. Multivariate Cox regression analysis with Akaike Information Criterion (AIC) was used to determine which patient specific variables were predictive for OS. All predictive variables were further used in the Cox model with an addition of dosimetric variables for the heart. We examined V%_D variables for the whole heart, heart digitally subdivided into four segments (right-superior, left-superior, right-inferior, left-inferior), and heart digitally subdivided into 64 segments. <h3>Results</h3> 197 patients treated for lung lesions with SBRT were selected for this study. 135 patients were treated for primary lung cancer (117 stage I, 18 stage II) and 42 patients were treated for lung metastasis from other primary sites. There were 79 patients with lung adenocarcinoma, 38 with lung squamous cell carcinoma (SCC), and 62 patients with other histologies. Dose prescriptions were 50 Gy in 5 fractions (35%) and 48 Gy in 4 fractions (65%). The reference patient was 72.2 years old, with stage I, primary lung adenocarcinoma. Mean OS was 76.8 months and mean follow up was 40 months (2 – 230). Patient age (HR = 1.08/year, <i>P</i> < 0.01), histology (HR = 2.8 for SCC, <i>P</i> < 0.01) and stage (HR = 3.5 for stage II primary lung cancer, <i>P</i> < 0.01) were predictive for OS. Patients with right lung disease had longer OS than patients with left lung disease (HR = 0.6, <i>P</i> = 0.03). Whole heart DVH analysis did not reveal statistically significant predictors for OS. A DVH analysis of the heart divided into four segments suggested that low doses in the inferior segments of the heart decreased OS (V%_2 Gy, HR = 1.007/1%, <i>P</i> = 0.02). Maximum dose in the right-inferior portion of the heart was also predictive for OS (HR = 1.02/Gy, <i>P</i> = 0.02). A more detailed analysis of 3D dose distributions in the heart, using a digital subdivision of the heart into 64 segments, confirmed that low doses to the inferior part of the heart decreased OS (<i>P</i> = 0.01). <h3>Conclusion</h3> Low doses to the inferior portion of the heart may be associated with reduced OS in patients treated with SBRT to the lung. Age, histology and stage were associated with OS.

Generalizability Study of a Fluence Map Prediction Network

<h3>Purpose/Objective(s)</h3> A deep learning-based fluence map prediction network (FMPN) was developed for predicting fluence maps for given desired dose distributions. The FMPN was trained with head and neck (HN) VMAT plans only. Theoretically, the FMPN learned an inverse planning optimization procedure, so it is a general method working for other types of plans. This work is to investigate the FMPN's generalizability in various clinical scenarios apart from the training data. <h3>Materials/Methods</h3> The FMPN which maps projections of 3D dose distribution to fluence maps was trained only with clinical HN full-arc VMAT plans. The baseline performance of the FMPN was evaluated on 102 HN full-arc VMAT plans independent of training dataset. To evaluate how well it generalizes to other clinical scenarios, we designed three tests, each of which has a feature different from training scenario. In Test A, we test the FMPN on a different treatment site, prostate. In Test B, we test the network on different delivery modality with three partial-arc VMAT plans and one IMRT plan. The first partial-arc plan is for a simple pelvis case with small PTV, while the other two are derived by blocking some angles of a plan for a complicated HN case with large concave PTV. The arc coverage for three plans are 120-360, 0-180, and 0-90/180-270 degrees. The IMRT plan is re-optimized from the HN case using eight equi-spaced angles. In Test C, we test the network on different degrees of modulation (DOM), in which four plans are generated by re-optimizing the HN case using four levels of penalty on DOM. Since there's a trade-off between plan DOM and plan quality, these four plans also distribute in a wide range of plan quality. The FMPN's baseline performance and generalizability were quantified by comparing the dose of FMPN-predicted fluence maps to the ground truth dose (the desired dose, input of FMPN) using 3D Gamma passing rate. <h3>Results</h3> All results are listed in Table 1. As shown in Test A, FMPN achieved a prediction accuracy in prostate site as high as in training site (HN). In test B, performance is excellent on partial-arc VMAT and degrades on IMRT, which is reasonable because IMRT is too far away from training data (full-arc VMAT) from data distribution point of view. In Test C, FMPN can predict fluence map for plans with various DOM/quality and accuracy doesn't vary with DOM. <h3>Conclusion</h3> The FMPN trained on HN full-arc VMAT plans achieves high accuracy on various clinical scenarios without retraining the model. Our results demonstrate that the FMPN is well generalizable to other treatment site, delivery modality and various DOM, showing great potential for clinical use.

Toward AI-Driven Proton Dose Calculation: Development and Evaluation of 3D and 2D Sequential Neural Network Design

AI-driven proton dose calculation may provide both the accuracy and computational speed needed during memory-intensive operations (e.g., robust treatment planning) and could ultimately pave the way to realizing a seamless adaptive proton therapy workflow. Recently, multiple approaches have been devised to effectively exploit the capabilities of the neural networks in simulating the proton transport in matter. Primarily, the three-dimensional (3D) proton beam dose distribution estimation was carried out by either training a 3D Convolutional Neural Network (3D-CNN) or by transforming the problem into a sequence of two-dimensional (2D) slices in the beam's eye view. In this study, we compare the performance of the two approaches for estimating the dose for single pencil beams (PB) and we discuss the strength and weaknesses of each approach, primarily in terms of the underlying inherent model characteristics.The two models were trained on two distinct datasets created via gold standard Monte Carlo (MC) simulation. A dataset of 10000 Topas MC simulations on artificial water phantoms exhibiting sizable cuboid inhomogeneities of varying dimensions, densities, and alignment along the beam trajectory, and a dataset of 12000 MC simulations on real-world lung patient case with different impinging proton energies, gantry angles, and isocenter shifts. While the latter dataset is meant to demonstrate the performance of each model in challenging real-life scenarios, the former is designed to monitor the behavior of each model in extreme heterogeneous scenarios. The 3D-CNN is constituted of a U-net model with a state-of-the-art ResNet encoder. The sequenced approach is incorporating a Long Short-Term Memory (LSTM) model as the frontend to transmit relevant information along the beam, and a fully connected neural network as a backend to generate the 2D dose distribution in each step along the beam trajectory.The response of the two models to simple and extreme clinical scenarios was investigated extensively in terms of range, lateral spread, and the profile deformation of the Bragg peaked region. Both models demonstrate comparable accuracy in comparison to the gold standard MC simulations resulting in 96.5 % (3D-CNN) and 97.7 % (LSTM) mean gamma index pass rate ([1%, 3mm]) when executed on the set-aside test set of the lung dataset. The 3D-CNN proves to be capable of extracting spatio-temporal features between the heterogeneities and the consequent dose deformations, however, the LSTM network exhibits a better propagation of the heterogeneities for higher ranged PBs.Both approaches to AI-driven dose calculation in proton therapy were shown to be viable candidates to estimate dose distributions at the PB level; however, in extreme scenarios of highly heterogeneous tissues and high-energy protons, advanced procedures are warranted for the design of the 3D model neural network.

Intracavitary Electron FLASH Radiotherapy in a Canine Cancer Patient With Oral Malignant Melanoma

Studies of electron FLASH radiotherapy (FLASH-RT) in companion animals are being conducted at several institutions. High energy electron beams are generally suitable for treatment of superficial cancers, but of limited use for deep-seated tumors. In this case report, the feasibility of intracavitary electron FLASH-RT is demonstrated.A canine cancer patient with a large oral malignant melanoma in the caudal part of the hard palate was palliatively treated with FLASH-RT at a modified clinical linear accelerator, using a cylindrical PMMA applicator (length 200 mm, inside diameter 50 mm). The patient had a poor prognosis, with an estimated survival of two weeks and no other available treatment options. A dose of 35 Gy was delivered to the depth of dose maximum in 26 pulses with a pulse dose rate of 4.5•105 Gy/s. The average dose rate was 280 Gy/s, corresponding to a total beam-on time of 125 months. One month after the treatment, the tumor was re-irradiated, using the same treatment parameters. The treatment head of the LINAC was fitted with a short electron applicator holder with a Cerrobend collimator at 65 cm from the source. The cylindrical PMMA applicator was aligned perpendicularly to the Cerrobend collimator using soft docking with a 1 cm gap. Prior to the treatments, the 2D dose distribution of the FLASH beam exiting the PMMA applicator was measured with radiographic film. In addition, the dosimetric effect of misalignment between the collimator and the electron applicator was studied based on intentional misalignments of 2.5°, 5°, 2.5 mm, and 5 mm.A partial response of the tumor and clinical improvement of the patient was observed two weeks after the first treatment. A hypopigmented area in the hard palate was observed, consistent with a grade 1 adverse event, but there were no signs of mucositis or reports of patient discomfort. However, due to the thickness of the tumor (> 3 cm), the posterior part only received a limited dose, and the tumor was still obstructing the airflow at one month post treatment. At this time, a second treatment was administered with limited normal tissue receiving full dose from both treatments. At one month post the second treatment, a partial response was achieved, and no evidence of side effects other than hypopigmentation was observed. The patient was euthanized 2.5 months after the initial therapy due to progressive disease involving the oropharynx. The dosimetric evaluation showed that a conformal dose distribution could be obtained by delivering the electron beam using a cylindrical PMMA applicator. Misalignments between the applicator and the collimator were shown to result in reduced dose homogeneity.This case report of a canine cancer patient, with a large oral malignant melanoma in the caudal part of the hard palate, has demonstrated the feasibility of intracavitary electron FLASH-RT. Two treatments of 35 Gy each, resulted in prolonged survival and increased quality-of-life.

Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy.

The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID). A deep learning-based signal processing strategy, referred to as 3DosiNet, is utilized to learn a mapping from an EPID image to planar dose distributions at given depths. The network was trained with the volumetric dose exported from the clinical treatment planning system (TPS). Given the latent inconsistency between measurements and corresponding TPS calculations, unsupervised learning is formulated in 3DosiNet to capture abstractive image features that are less sensitive to the potential variations. Validation experiments were performed using five regular fields and three clinical intensity-modulated radiation therapy (IMRT) cases. The measured dose profiles and percentage depth dose (PDD) curves were compared with those measured using standard tools in terms of the 1D gamma index. The mean gamma pass rates (2%/2mm) over the regular fields are 100% and 97.3% for the dose profile and PDD measurements, respectively. The measured volumetric dose was compared to the corresponding TPS calculation in terms of the 3D gamma index. The mean 2%/2mm gamma pass rates are 97.9% for square fields and 94.9% for the IMRT fields. The system promises to be a practical 3D dosimetric tool for pre-treatment patient-specific quality assurance and further developed for in-treatment patient dose monitoring.

On measuring the 3D dose distribution for notched and circular Ru-106 plaque shapes through Gafchromic film dosimetry approach

Ruthenium-106 (106Ru) plaques are specific applicators dedicated for intraocular brachytherapy. Small tumor volume and presence of vital organs in its close vicinity as well as appearance of very high dose gradient regions around the employed plaques cause unique challenges in plaque-based brachytherapy. Therefore, access to 3D dose distribution for each brachytherapy plaque is quite helpful to decide about tumor volume coverage and overexposure of surrounding sensitive organs. Current study aims to measure the 3D dose distribution for two commercially available 106Ru plaques using film dosimetry.COB (a notched-shape plaque) and CGD (a fully circular plaque) 106Ru plaques were used and dose distributions in different planes were measured by simultaneously employing a 3D printed eyeball phantom and Gafchromic EBT3 films. Finally, 3D dose distributions were acquired by interpolating the measured 2D dose data at different planes.The results showed that employing the COB plaque tilts the isodose curves toward the non-notched edge of the plaque, while no isodose tilting was observed for CGD plaque. The obtained 3D dose distributions were different for studied plaques. Non-uniform 3D dose distributions were found for both plaques which can be attributed to the plaque design and reference point position on plaque central axis.Finally, it can be concluded that the obtained 3D dose distributions can specify the optimal tumor shape which can be adequately covered by brachytherapy plaque with minimizing the dose non-uniformity and hot point formation inside the treated volume. Obtained 3D dose distribution for each plaque can be employed for patient treatment planning.

Evaluation of Radiation Shielding Requirements and Self-shielding Characteristics for a Novel Radiosurgery System.

In order to provide safe operating conditions for radiation workers and the public at large, the radiation shielding for radiotherapy treatment rooms will have to be determined by an expert physicist according to the applicable radiation control regulations. A new radiosurgery system with integrated self-shielding, called the Zap-X, obviates the need for costly and complex radiation bunkers. Radiation levels in the vicinity of the ZAP-X radiosurgery system were acquired for a number of different operating conditions, and a 3D radiation dose distribution was measured for better visualization of hot spots and the general dose distribution. The radiation shielding requirements were evaluated according to the International and Chinese standards, respectively. While the integral self-shielding of the Zap-X was designed in accordance with international standards, it was found to be insufficient for Chinese standards. In order to meet the needs of the growing new generation of radiotherapy equipment, several suggestions for improvement of Chinese standards are proposed.

Radiotherapy dosimetry parameters intercomparison among eight gel dosimeters by Monte Carlo simulation

Dosimetry in radiotherapy is extremely important to guarantee effectiveness of the treatment, as described by quality assurance procedures. The main dosimeter framework proposed by International Protocols is commonly based on determinations of in-water absorbed dose. Gel dosimetry, mainly based on liquid water, offers the unique characteristic that the phantom constitutes itself the radiosensitive material. This study reports on the suitability of gel dosimetry formulations, typically implemented for clinical purposes, evaluating dosimetry outputs by means of PENELOPE and FLUKA Monte Carlo simulations, along with experimental data. The effects on the absorbed dose distributions due to variation in the mass density and the mean excitation energy are calculated for BANG®, Fricke, ITABIS, MAGIC, NBT-Pluronic, NIPAM, PAGAT, and PRESAGE formulations at irradiations with 6 and 15 MV photon and 6 MeV electron beams. Dosimetry parameters for photon and electron beams are calculated, and effects due to variations in mass density and mean excitation energy are carefully investigated. According to the results obtained from Monte Carlo simulations, along with dose profile comparisons with simulations of in-water irradiations, and comparisons with experimental data, all studied gel dosimetry formulations are capable of assessing reliable dosimetry outputs extracted from 3D dose distributions, even with uncertainties in their mass density or mean excitation energy of ±10%.

A simulation study of ionizing radiation acoustic imaging (iRAI) as a real-time dosimetric technique for ultra-high dose rate radiotherapy (UHDR-RT).

Electron-based ultra-high dose rate radiation therapy (UHDR-RT), also known as Flash-RT, has shown the ability to improve the therapeutic index in comparison to conventional radiotherapy (CONV-RT) through increased sparing of normal tissue. However, the extremely high dose rates in UHDR-RT have raised the need for accurate real-time dosimetry tools. This work aims to demonstrate the potential of the emerging technology of Ionized Radiation Acoustic Imaging (iRAI) through simulation studies and investigate its characteristics as a promising relative in vivo dosimetric tool for UHDR-RT. The detection of induced acoustic waves following a single UHDR pulse of a modified 6 MeV 21EX Varian Clinac in a uniform porcine gelatin phantom that is brain-tissue equivalent was simulated for an ideal ultrasound transducer. The full 3D dose distributions in the phantom for a 1×1 cm2 field were simulated using EGSnrc (BEAMnrc∖DOSXYZnrc) Monte Carlo (MC) codes. The relative dosimetry simulations were verified with dose experimental measurements using Gafchromic films. The spatial dose distribution was converted into an initial pressure source spatial distribution using the medium-dependent dose-pressure relation. The MATLAB-based toolbox k-Wave was then used to model the propagation of acoustic waves through the phantom and perform time-reversal (TR)-based imaging reconstruction. The effect of the various linear accelerator (linac) operating parameters, including linac pulse duration and pulse repetition rate (frequency), were investigated as well. The MC dose simulation results agreed with the film measurement results, specifically at the central beam region up to 80% dose within approximately 5% relative error for the central profile region and a local relative error of<6% for percentage dose depth. IRAI-based FWHM of the radiation beam was within approximately 3mm relative to the MC-simulated beam FWHM at the beam entrance. The real-time pressure signal change agreed with the dose changes proving the capability of the iRAI for predicting the beam position. IRAI was tested through 3D simulations of its response to be based on the temporal changes in the linac operating parameters on a dose per pulse basis as expected theoretically from the pressure-dose proportionality. The pressure signal amplitude obtained through 2D simulations was proportional to the dose per pulse. The instantaneous pressure signal amplitude decreases as the linac pulse duration increases, as predicted from the pressure wave generation equations, such that the shorter the linac pulse the higher the signal and the better the temporal (spatial) resolutions of iRAI. The effect of the longer linac pulse duration on the spatial resolution of the 3D constructed iRAI images was corrected for linac pulse deconvolution. This correction has improved the passing rate of the 1%/1mm gamma test criteria, between the pressure-constructed and dosimetric beam characteristics, to as high as 98%. A full simulation workflow was developed for testing the effectiveness of iRAI as a promising relative dosimetry tool for UHDR-RT radiation therapy. IRAI has shown the advantage of 3D dose mapping through the dose signal linearity and, hence, has the potential to be a useful dosimeter at depth dose measurement and beam localization and, hence, potentially for in vivo dosimetry in UHDR-RT.

Technical Note: A cascade 3D U-Net for dose prediction in radiotherapy.

Although large datasets are available, to learn a robust dose prediction model from a limited dataset still remains challenging. This work employed cascaded deep learning models and advanced training strategies with a limited dataset to precisely predict three-dimensional (3D) dose distribution. A Cascade 3D (C3D) model is developed based on the cascade mechanism and 3D U-Net network units. During model training, data augmentations are used to improve the generalization ability of the prediction model. A knowledge distillation technique is employed to further improve the capability of model learning. The C3D network was evaluated using the OpenKBP challenge dataset and competed with those models proposed by more than 40 teams globally. Additionally, it was compared with five existing cutting-edge dose prediction models. The performance of these prediction models was evaluated by voxel-based mean absolute error (MAE) and clinical-related dosimetric metrics. The code and models are publicly available online (https://github.com/LSL000UD/RTDosePrediction). The MAE of a single C3D model without test-time augmentation is 2.50Gy (3.57% related to prescription dose) for nonzero dose area, which outperforms the other five dose prediction models by about 0.1Gy-1.7Gy. The C3D model won both dose and DVH streams of AAPM 2020 OpenKBP challenge with dose score of 2.31 and DVH score of 1.55. The Cascading U-Nets is an ideal solution for 3D dose prediction from a limited dataset. The proper data preprocessing, data augmentation, and optimization procedure are more important than architectural modifications of deep learning network.

Gel and thermoluminescence dosimetry for dose verifications of a real anatomy simulated prostate conformal radiation treatment in the presence of metallic femoral prosthesis.

This study aims to verify the dose delivery of prostate radiotherapy treatments in an adult pelvic phantom with two metallic hip and femur prosthesis using a four‐field box technique. The prostate planned target volume (PTV) tridimensional (3D) dose distribution was evaluated using gel dosimetry, and thermoluminescent dosimeters (TLD) were used for point‐dose measurements outside it. Both results were compared to the treatment planning system (TPS) dose calculation without using heterogeneity corrections to evaluate the influence of the metal in the dose distribution. MAGIC‐f gel dosimeter (Methacrylic and Ascorbic acid in Gelatin Initiated by Copper with Formaldehyde) associated with magnetic resonance imaging was used. TLD were positioned at several points at the bone metal interface and the sacrum region. The comparison of the gel measured and the TPS calculated dose distributions were done using gamma analysis (3%/3 mm), and a pass rate of 93% was achieved. The TLD dose values at the bone‐metal interface showed variations from the planned dose. However, at the sacrum region, where the beams did not intercept the prosthesis, there was a good agreement between TPS planning and TLD measurements. Our results show how the combination of 3D dosimetry and measurements at specific points in the phantom allowed a comprehensive view of the dose distribution and identified that care must also be paid to regions outside the PTV.

Plastic scintillation dosimeter with a conical mirror for measuring 3D dose distribution.

To test the measurement technique of the three-dimensional (3D) dose distribution measured image by capturing the scintillation light generated using a plastic scintillator and a scintillating screen. Our imaging system constituted a column shaped plastic scintillator covered by a Gd2 O2 S:Tb scintillating screen, a conical mirror and a cooled CCD camera. The scintillator was irradiated with 6 MV photon beams. Meanwhile, the irradiated plan was prepared for the static field plans, two-field plan (2F plan) and the conformal arc plan (CA plan). The 2F plan contained 16 mm2 and 10 mm2 fields irradiated from gantry angles of 0° and 25°, respectively. The gantry was rotated counterclockwise from 45° to 315° for the CA plan. The field size was then obtained as 10 mm2 . A Monte Carlo simulation was performed in the experimental geometry to obtain the calculated 3D dose distribution as the reference data. Dose response was acquired by comparing between the reference and the measurement. The dose rate dependence was verified by irradiating the same MU value at different dose rates ranging from 100 to 600 MU/min. Deconvolution processing was applied to the measured images for the correction of light blurring. The measured 3D dose distribution was reconstructed from each measured image. Gamma analysis was performed to these 3D dose distributions. The gamma criteria were 3% for the dose difference, 2mm for the distance-to-agreement and 10% for the threshold. Dose response for the scintillation light was linear. The variation in the light intensity for the dose rate ranging from 100 to 600 MU/min was less than 0.5%, while our system presents dose rate independence. For the 3D dose measurement, blurring of light through deconvolution processing worked well. The 3D gamma passing rate (3D GPR) for the 10 × 10 mm2 , 16 × 16 mm2 , and 20 × 20 mm2 fields were observed to be 99.3%, 98.8%, and 97.8%, respectively. Reproducibility of measurement was verified. The 3D GPR results for the 2F plan and the CA plan were 99.7% and 100%, respectively. We developed a plastic scintillation dosimeter and demonstrated that our system concept can act as a suitable technique for measuring the 3D dose distribution from the gamma results. In the future, we will attempt to measure the 4D dose distribution for clinical volumetric modulated arc radiation therapy (VMAT)-SBRTplans.

Lung volume irradiation procedures in patients with pneumonia during COVID-19 infection – physical aspects of treatment planning and dosimetry

Introduction. The paper presents an original, used in our Institute, method of lung volume irradiation in patients with pneumonia during COVID-19 infection. Material and methods. Procedures such as the simulation of treatment and radiotherapy are performed in a treatment room. Real time radiation treatment planning is realized as 2D planning (Irreg Planning VMS) in a separate room, and the 3D (eclipse VMS) dose distribution is calculated after the treatment. During radiation exposure, a fluence map is measured. Results. A method of irradiating the lungs of patients with COVID-19 was developed, which allows to shorten the time the patient is on the treatment table and minimize contacts between the patient and staff. Conclusions. The presented procedure made it possible to minimize the time of patient’s stay in the radiotherapy department and at the same time, it retains all the required quality assurance procedures in radiotherapy treatment.

Measuring breathing induced oesophageal motion and its dosimetric impact.

Stereotactic body radiation therapy allows for a precise dose delivery. Organ motion bears the risk of undetected high dose healthy tissue exposure. An organ very susceptible to high dose is the oesophagus. Its low contrast on CT and the oblong shape render motion estimation difficult. We tackle this issue by modern algorithms to measure oesophageal motion voxel-wise and estimate motion related dosimetric impacts. Oesophageal motion was measured using deformable image registration and 4DCT of 11 internal and 5 public datasets. Current clinical practice of contouring the organ on 3DCT was compared to timely resolved 4DCT contours. Dosimetric impacts of the motion were estimated by analysing the trajectory of each voxel in the 4D dose distribution. Finally an organ motion model for patient-wise comparisons was built. Motion analysis showed mean absolute maximal motion amplitudes of 4.55±1.81mm left-right, 5.29±2.67mm anterior-posterior and 10.78±5.30mm superior-inferior. Motion between cohorts differed significantly. In around 50% of the cases the dosimetric passing criteria was violated. Contours created on 3DCT did not cover 14% of the organ for 50% of the respiratory cycle and were around 38% smaller than the union of all 4D contours. The motion model revealed that the maximal motion is not limited to the lower part of the organ. Our results showed motion amplitudes higher than most reported values in the literature and that motion is very heterogeneous across patients. Individual motion information should be considered in contouring and planning.

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity.

Simple SummaryDosiomics is born directly as an extension of radiomics: it entails extracting features from the patients’ three-dimensional (3D) radiotherapy dose distribution rather than from conventional medical images to obtain specific spatial and statistical information. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. This study provides the first multicentre evaluation of the dosiomic features in terms of reproducibility, stability and sensitivity across various dose distributions obtained from multiple technologies and techniques and considering different dose calculation algorithms of TPS and two different resolutions of the dose grid. Harmonisation strategies to account for a possible variation in the dose distribution due to these confounding factors should be adopted when investigating a correlation between dosiomic features and clinical outcomes in multicentre studies.Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.

Anisotropic diffusion of Fe ions in Fricke-XO-Pluronic F-127 and Fricke-XO-gelatine 3D radiotherapy dosimeters

A new radiochromic dosimeter was examined with Raman spectroscopy and an optical approach for assessment of 3D dose distribution integrity. The acronym of the dosimeter is Fricke-XO-Pluronic F-127, where XO denotes xylenol orange; Pluronic F-127 is a copolymer matrix of poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide), and the dosimeter contains the components of a Fricke dosimetric solution. Two dosimeter samples in cuvettes were partially irradiated such that a radiation dose was absorbed at the bottom of the cuvettes. After irradiation, one sample was stored upside down such that the irradiated part was at the top and another one was stored with the irradiated part at the bottom. Two diffusion coefficients of ferric ion complexes with XO ([XO-Fe]+3) were calculated. They were compared with those for similar dosimeter, however with gelatine matrix instead of Pluronic F-127. The results obtained indicate an impact of the gravitational force on the diffusion of [XO-Fe]+3 ions over time after irradiation and thus a possibility of severely undermining the integrity of a dose distribution in irradiated dosimeter. The conclusions drawn suggest the necessity of examination of different 3D Fricke dosimeter compositions for anisotropic diffusion of ferric ions.

A feasibility study on deep learning-based individualized 3D dose distribution prediction.

Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient-specific anatomy but also on physicians' preferred trade-offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine-tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade-offs, as represented by a dose volume histogram (DVH), as inputs. In this work, we developed a modified U-Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine-tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients. The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose. In this feasibility study, we have developed a 3D U-Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.

Fully automated treatment planning for MLC-based robotic radiotherapy.

PurposeTo propose and validate a fully automated multicriterial treatment planning solution for a CyberKnife® equipped with an InCiseTM 2 multileaf collimator.MethodsThe AUTO BAO plans are generated using fully automated prioritized multicriterial optimization (AUTO MCO) of pencil‐beam fluence maps with integrated noncoplanar beam angle optimization (BAO), followed by MLC segment generation. Both the AUTO MCO and segmentation algorithms have been developed in‐house. AUTO MCO generates for each patient a single, high‐quality Pareto‐optimal IMRT plan. The segmentation algorithm then accurately mimics the AUTO MCO 3D dose distribution, while considering all candidate beams simultaneously, rather than replicating the fluence maps. Pencil‐beams, segment dose depositions, and final dose calculations are performed with a stand‐alone version of the clinical dose calculation engine. For validation, AUTO BAO plans were generated for 33 prostate SBRT patients and compared to reference plans (REF) that were manually generated with the commercial treatment planning system (TPS), in absence of time pressure. REF plans were also compared to AUTO RB plans, for which fluence map optimization was performed for the beam angle configuration used in the REF plan, and the segmentation could use all these beams or only a subset, depending on the dosimetry.ResultsAUTO BAO plans were clinically acceptable and dosimetrically similar to REF plans, but had on average reduced numbers of beams ((beams in AUTO BAO)/(beams in REF) (relative improvement): 24.7/48.3 (−49%)), segments (59.5/98.9 (−40%)), and delivery times (17.1/22.3 min. (−23%)). Dosimetry of AUTO RB and REF were also similar, but AUTO RB used on average fewer beams (38.0/48.3 (−21%)) and had on average shorter delivery times (18.6/22.3 min. (−17%)). Delivered Monitor Units (MU) were similar for all three planning approaches.ConclusionsA new, vendor‐independent optimization workflow for fully automated generation of deliverable high‐quality CyberKnife® plans was proposed, including BAO. Compared to manual planning with the commercial TPS, fraction delivery times were reduced by 5.3 min. (−23%) due to large reductions in beam and segment numbers.