3D Dose Research Articles

Commissioning of a commercial treatment planning system for scanned carbon-ion radiotherapy.

To commission the RayStation (RS) TPS (treatment planning system) for scanned CIRT (carbon-ion radiotherapy) utilizing pencil beam algorithms (PBv4.2). The beam model commissioning entailed employing 1D single beams and 2D monoenergetic fields to validate spot profiles with films, assess beam range using Peakfinder measurements, and evaluate fragment spectra through dose-averaged linear energy transfer (LETd) calculations. 3D dose distributions were verified in homogeneous phantoms for both absorbed and relative biological effectiveness (RBE)-weighted doses, and further assessed in double wedge and anthropomorphic phantoms for absorbed dose only. Finally, RBE-weighted dose verification and patient-specific quality assurance were conducted using 58 beams from 20 clinically treated patient plans. The results demonstrated good agreement in absolute dose distribution between TPS calculations and measurements, with mean dose discrepancies within 3%. However, deviations were slightly higher (>1%) for the cases involving the range shifter (RaShi) compared to those without the RaShi (<1%). Beam range, depth dose distribution, and lateral profiles of spread-out Bragg peaks (SOBPs) closely matched between RS TPS calculations and measurements. Some discrepancies (less than 0.5mm) were observed at field edges and in penumbra regions due to limitations in simulating asymmetrical spots, but within clinical tolerance. After model tuning, RBE-weighted dose calculations in RS TPS were in agreement with those from the clinically used TPS, except for variations exceeding 3% observed at energies exceeding 408.07 MeV/u, primarily attributed to fragment spectra differences. Overall, this study validated the RS TPS for calculating absorbed doses against measurements and RBE-weighted doses against a clinically used TPS. The results suggested that the RS TPS could be utilized for CIRT treatment planning, except for energies exceeding 408.07 MeV/u.

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations

BackgroundAccurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients. This study seeks to develop a more versatile model incorporating variable beam configurations of CK and considering the patient’s anatomy.MethodsThis study proposed that the AB (anatomy and beam) model be compared with the control Mask (only anatomy) model. These models are based on a 3D U-Net network to investigate the impact of CK beam encoding information on dose prediction. The study collected 86 lung cancer patients who received CK′s built-in MC algorithm plans using different beam configurations for training/validation (66 cases) and testing (20 cases). We compared the gamma passing rate, dose difference maps, and relevant dose-volume metrics to evaluate the model’s performance. In addition, the Dice similarity coefficients (DSCs) were calculated to assess the spatial correspondence of isodose volumes.ResultsThe AB model demonstrated superior performance compared to the Mask model, particularly in the trajectory dose of the beam. The DSCs of the AB model were 20–40% higher than that of the Mask model in some dose regions. We achieved approximately 99% for the PTV and generally more than 95% for the organs at risk (OARs) referred to the clinical planning dose in the gamma passing rates (3 mm/3%). Relative to the Mask model, the AB model exhibited more than 90% improvement in small voxels (p < 0.001). The AB model matched well with the clinical plan’s dose-volume histograms, and the average dose error for all organs was 1.65 ± 0.69%.ConclusionsOur proposed new model signifies a crucial advancement in predicting CK 3D dose distributions for clinical applications. It enables planners to rapidly and precisely predict MC doses for lung cancer based on patient-specific beam configurations and optimize the CK treatment process.

Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.

Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Deep learning based dose prediction methods have shown favorable solutions for enhancing efficiency, but development has primarily been on external beam radiation therapy. Thus, there is a need for translation to brachytherapy. This study proposes a dose prediction model utilizing an attention-gating mechanism and a 3D UNET for cervical cancer high-dose-rate intracavitary brachytherapy treatment planning with tandem-and-ovoid/ring applicators. A multi-institutional data set consisting of 77 retrospective clinical brachytherapy plans was utilized in this study. The data were preprocessed and augmented to increase the number of plans to 252. A 3D UNET architecture with attention gates was constructed and trained for mapping the contour information to dose distribution. The trained model was evaluated on a testing data set using various metrics, including dose statistics and dose-volume indices. We also trained a baseline UNET model for a fair comparison. The attention-gated 3D UNET model exhibited competitive accuracy in predicting dose distributions similar to the ground truth. The average values of the mean absolute errors were 0.46±11.71Gy (vs. 0.47±9.16Gy for a baseline UNET) in CTVHR, 0.55±0.67Gy (vs. 0.70±1.54Gy for a baseline UNET) in bladder, 0.42±0.46Gy (vs. 0.49±1.34Gy for a baseline UNET) in rectum, and 0.31±0.65Gy (vs. 0.20±3.76Gy for a baseline UNET) in sigmoid. Our results showed that the mean individual differences in ΔD2cc for bladder, rectum, and sigmoid were 0.38±1.19 (p=0.50), 0.43±0.71 (p=0.41), and -0.47±0.79 (p=0.30) Gy, respectively. Similarly, the mean individual differences in ΔD1cc for bladder, rectum, and sigmoid were 0.09±1.21 (p=0.36), 0.20±0.95 (p=0.24), and -0.21±0.59 (p=0.30) Gy. The mean individual differences for ΔD90, ΔV100%, ΔV150%, and ΔV200% of the CTVHR were -0.45±2.42 (p=0.26) Gy, 0.55±9.42% (p=0.78), 0.82±4.21% (p=0.81), and -0.80±10.48% (p=0.36), respectively. The model requires less than 5 s to predict a full 3D dose distribution for a new patient plan. Attention-gated 3D UNET revealed a promising capability in predicting voxel-wise dose distributions compared to 3D UNET. This model could be deployed for clinical use to predict 3D dose distributions for near real-time decision-making before planning, quality assurance, and guiding future automated planning, making the current workflow more efficient.

Deep learning prediction of scenario doses for direct plan robustness evaluations in IMPT for head-and-neck

Objective. Intensity modulated proton therapy (IMPT) is susceptible to uncertainties in patient setup and proton range. Robust optimization is employed in IMPT treatment planning to ensure sufficient coverage of the clinical target volume (CTV) in predefined scenarios, albeit at a price of increased planning times. We investigated a deep learning (DL) strategy for dose predictions in individual error scenarios in head and neck cancer IMPT treatment planning, enabling direct evaluation of plan robustness. The model is able to differentiate between scenarios by using embeddings of the scenario index.Approach. To accommodate resolution disparities in planning CT-scans and accommodate the setup error scenarios, we introduced scenario-specific isocentric distance maps as inputs to the DL models. For 392 H&N cancer patients, high-quality 9-scenario ground truth (GT) robust plans were generated with wish-list driven fully automated multi-criteria optimization. The scenario index is converted to one-hot-vector that is used to derive the scenarios embeddings through the training of the DL model, aiding the model to predict a scenario specific dose distribution.Main results. The model achieved within 1%-point of agreement with the GT the predictedV95%of the voxelwise minimum dose for CTV Low and CTV High for 96% and 75% respectively of the test patients. Considering all robustness scenarios, median differences were 0.035%-point for CTV HighV95%, 0.11%-point for CTV LowV95%, 0.29 GyE for parotidsDmean, 0.7 GyE for submandibular glandsDmeanand 0.9 GyE for oral cavityDmean. Prediction of full 3D dose distributions for all scenarios took around 14 s.Significance. Predicting individual scenarios for robust proton therapy using DL dose prediction is feasible, enabling direct robustness evaluation of the predicted scenario doses.

Metodología para la validación de programas de cálculo de dosis en piel

Objective. A methodology for facilitating the validation of skin dose estimation programs for interventional procedures is presented. Materials and methods. The methodology uses a series of irradiations stored as DICOM Radiation Dose Structured Reports (RDSRs) as well as reference skin dose values measured in each irradiation. Users must input the RDSRs to their program and compare output doses with reference doses. The authors performed 27 irradiations using two C-arms from different manufacturers. For each irradiation, the authors modified parameters that affect skin dose and measured it with an ionization chamber placed under a water phantom. The methodology was applied to validate the program SkinDose 2D. Results. Experimental measurements have an uncertainty of 13% (k=2). SkinDose 2D dose predictions differ from measurements in -9% to +8%, within experimental uncertainty. Conclusions. Varying different parameters that affect skin dose, this methodology allows for an initial validation of skin dose programs, characterizing them before any other additional measurement done by the user.

Comprehensive stereotactic radiosurgery platform characterization: A novel end-to-end approach with anthropomorphic 3D dosimetry.

Stereotactic radiosurgery (SRS) is a widely employed strategy for intracranial metastases, utilizing linear accelerators and volumetric modulated arc therapy (VMAT). Ensuring precise linear accelerator performance is crucial, given the small planning target volume (PTV) margins. Rapid dose falloff is vital to minimize brain radiation necrosis. Despite advances in SRS planning, tools for end-to-end testing of SRS treatments are lacking, hindering confidence in the procedure. This study introduces a novel end-to-end three-dimensional (3D) anthropomorphic dosimetry system for characterization of a radiosurgery platform, aiming to measure planning metrics, dose gradient index (DGI), brain volumes receiving at least 10 and 12Gy (V10, V12), as well as assess delivery uncertainties in multitarget treatments. The study also compares metrics from benchmark plans to enhance understanding and confidence in SRS treatments. The developed anthropomorphic 3D dosimetry system includes a modified Stereotactic End-to-End Verification (STEEV) phantom with a customized insert integrating 3D dosimeters and a fiber optic CT scanner. Labview and MATLAB programs handle optical scanning, image preprocessing, and dosimetric analysis. SlicerRT is used for 3D dose visualization and analysis. A film stack insert was used to validate the 3D dosimeter measurements at specific slices. Benchmark plans were developed and measured to investigate off-axis errors, dose spillage, small field dosimetry, and multi-target delivery. The accuracy of the developed 3D dosimetry system was rigorously assessed using radiochromic films. Two two-dimensional (2D) dose planes, extracted from the 3D dose distribution, were compared with film measurements, resulting in high passing rates of 99.9% and 99.6% in gamma tests. The mean relative dose difference between film and 3D dosimeter measurements was -1%, with a standard deviation of 2.2%, well within dosimeter uncertainties. In the first module, evaluating single-isocenter multitarget treatments, a 1.5mm dose distribution shift was observed when targets were 7cm off-axis. This shift was attributed to machine mechanical errors and image-guided system uncertainties, indicating potential limitations in conventional gamma tests. The second module investigated discrepancies in intermediate-to-low dose spillage, revealing higher measured doses in the connecting region between closely positioned targets. This discrepancy was linked to uncertainties in treatment planning system (TPS) modeling of out-of-field dose and multileaf collimator (MLC) characteristics, resulting in lower DGI values and higher V10 and V12 values compared to TPS calculations. In the third module, irradiating multiple targets showed consistent V10 and V12 values within 1 cm3 agreement with dose calculations. However, lower DGI values from measurements compared to calculations suggested intricacies in the treatment process. Conducting vital end-to-end testing demonstrated the anthropomorphic 3D dosimetry system's capacity to assess overall treatment uncertainty, offering a valuable tool for enhancing treatment accuracy in radiosurgery platforms. The study introduces a novel anthropomorphic 3D dosimetry system for end-to-end testing of a radiosurgery platform. The system effectively measures plan quality metrics, captures mechanical errors, and visualizes dose discrepancies in 3D space. The comprehensive evaluation capability enhances confidence in the commissioning and verification process, ensuring patient safety. The system is recommended for commissioning new radiosurgery platforms and remote auditing of existing programs.

A Tissue‐Equivalent, Reusable Dosimeter for 3D Verification of Radiotherapy

AbstractA new tissue‐equivalent, reusable material for dosimetric verification of radiotherapy is developed and characterized. The material comprises a hydrogel matrix containing copper‐doped lithium fluoride nanoparticles that exhibit optically stimulated luminescence (OSL). Using a laser light sheet and a sensitive camera equipped with appropriate filters, the OSL signal is read out layer by layer thus constructing the 3D dose distribution. The good tissue equivalence allows the new material to register the dose from a prescribed treatment plan as if delivered to a patient. Cubic dosimeters measuring 50 × 50 × 50 mm3 are developed and used to measure 3D dose distributions (from voxels measuring 0.8 × 0.8 × 1.0 mm3) with a statistical dose precision of 5% at 100 Gy dose levels. The spatial precision of offsets in dose gradients between planned and measured dose distributions is below the voxel size. A 3% / 3 mm gamma analysis between a prescribed treatment plan and the retrieved and corrected readout yields a pass rate of 89.5%. Light scattering within the dosimetric volume affects the dosimeter performance, indicating the potential for future improvements of spatial and dose precision. In conclusion, the dosimeter system shows high promise for future clinical validation of radiotherapy.

Three-dimensional radiation dosimetry of carbon ion beams using surfactant hydrogels: Fundamental investigation.

In carbon ion radiotherapy, accurate measurement of the three-dimensional (3D) absorbed dose distribution is critical for effectively targeting tumors. Although micellar gel dosimeters exhibit considerable potential for measuring 3D absorbed dose distributions, few studies have focused on radiotherapy using carbon ion beams. This study investigated the applicability of the surfactant hydrogel dosimeter (SHD), a micellar gel dosimeter, to measuring a 3D dose absorbed through carbon ion beam irradiation. A cubic target region of 34mm per side was established at a depth of 46mm below the upper surface of an SHD specimen. Scanning irradiation was performed using a pencil beam of carbon ions at the Ion-beam Radiation Oncology Center in Kanagawa ("i-ROCK"), Japan, under irradiation conditions set by the treatment planning system ("Monaco for Carbon", Ver. 5.20, Elekta AB, Sweden) to create a spread-out Bragg peak within the target. The physical dose was set to 10Gy at the isocenter, situated at the center of the target. The SHD responsiveness was measured twice using optical computed tomography (CT) ("Vista 15", Modus Medical Devices, Canada) for three irradiated specimens, and six types of measured optical attenuation coefficient (OAC) were obtained. To assess whether the OAC represented the absorbed dose expected in the treatment plan, we compared the relative distribution of the OAC and that of the absorbed dose. Relative fraction (RF) was used to measure the difference between the relative value of the OAC and that of the absorbed dose. Moreover, the distribution of OH radical (•OH) concentration obtained by Monte Carlo simulation ("PHITS" ver. 3.24 JAEA, Japan) and that of the OAC were compared. In the direction of beam travel, the relative distribution of the OAC was lower than that of the absorbed dose. This discrepancy could be attributed to a decrease in the concentration of •OH produced by irradiation owing to the recombination reaction, which does not accurately reflect the absorbed dose. By contrast, the distributions in the plane perpendicular to the beam travel were consistent. The RF increased from±3% to±13% along the beam travel direction. The small RF in the plane perpendicular to the beam travel could be attributed to the constant distribution of linear energy transfer, regardless of the irradiation position, and the generation of radicals proportionally to the absorbed dose. The increase in RF along the beam travel direction was ascribed to ring artifacts in the irradiated region. The measurement of the absorbed dose distribution in the beam travel direction should be improved. The observed discrepancy is attributed to the reduced reactivity of the SHD due to a high liner energy transfer near the Bragg peak. However, the absorbed dose distribution can be effectively evaluated in the plane perpendicular to the direction of beam travel.

Automatic Treatment Planning for Radiation Therapy: A Cross-Modality and Protocol Study

PurposeThis study investigates the applicability of 3D dose predictions from a model trained on one modality to a cross-modality automated planning workflow. Additionally, we explore the impact of integrating a multi-criteria optimizer (MCO) on adapting predictions to different clinical preferences. Methods and MaterialsUsing a previously created three-stage UNet in-house model trained on the 2020 AAPM OpenKBP challenge dataset (340 head and neck plans, all planned using 9-field static IMRT), we retrospectively generated dose predictions for 20 patients. These dose predictions were in turn used to generate deliverable IMRT, VMAT, and Tomotherapy plans using the fallback plan functionality in Raystation. The deliverable plans were evaluated against the dose predictions based on primary clinical goals. A new set of plans was also generated using MCO-based optimization with predicted dose values as constraints. Delivery QA was performed on a subset of the plans to assure clinical deliverability. ResultsThe mimicking approach accurately replicated the predicted dose distributions across different modalities, with slight deviations in spinal cord and external contour maximum doses. MCO optimization significantly reduced doses to OARs prioritized by our institution while maintaining target coverage. All tested plans met clinical deliverability standards, evidenced by a gamma analysis passing rate above 98%. ConclusionsOur findings show that a model trained only on IMRT plans can effectively contribute to planning across various modalities. Additionally, integrating predictions as constraints in an MCO-based workflow, rather than direct dose mimicking, enables a flexible, warm-start approach for treatment planning, though the benefit is reduced when the training set differs significantly from an institution's preference. Together, these approaches have the potential to significantly decrease plan turnaround time and quality variance, both at high resource medical centers that can train in-house models, and smaller centers that can adapt a model from another institution with minimal effort.

Enhancing stereotactic ablative boost radiotherapy dose prediction for bulky lung cancer: A multi-scale dilated network approach with scale-balanced structure loss.

Partial stereotactic ablative boost radiotherapy (P-SABR) effectively treats bulky lung cancer; however, the planning process for P-SABR requires repeated dose calculations. To improve planning efficiency, we proposed a novel deep learning method that utilizes limited data to accurately predict the three-dimensional (3D) dose distribution of the P-SABR plan for bulky lung cancer. We utilized data on 74 patients diagnosed with bulky lung cancer who received P-SABR treatment. The patient dataset was randomly divided into a training set (51 plans) with augmentation, validation set (7 plans), and testing set (16 plans). We devised a 3D multi-scale dilated network (MD-Net) and integrated a scale-balanced structure loss into the loss function. A comparative analysis with a classical network and other advanced networks with multi-scale analysis capabilities and other loss functions was conducted based on the dose distributions in terms of the axial view, average dose scores (ADSs), and average absolute differences of dosimetric indices (AADDIs). Finally, we analyzed the predicted dosimetric indices against the ground-truth values and compared the predicted dose-volume histogram (DVH) with the ground-truth DVH. Our proposed dose prediction method for P-SABR plans for bulky lung cancer demonstrated strong performance, exhibiting a significant improvement in predicting multiple indicators of regions of interest (ROIs), particularly the gross target volume (GTV). Our network demonstrated increased accuracy in most dosimetric indices and dose scores in different ROIs. The proposed loss function significantly enhanced the predictive performance of the dosimetric indices. The predicted dosimetric indices and DVHs were equivalent to the ground-truth values. Our study presents an effective model based on limited datasets, and it exhibits high accuracy in the dose prediction of P-SABR plans for bulky lung cancer. This method has potential as an automated tool for P-SABR planning and can help optimize treatments and improve planning efficiency.

3D gel dosimeter assessment for end-to-end geometric accuracy determination of the online adaptive workflow on the 1.5 T MR-linac

Background and purpose:During an end-to-end (E2E) test on the online workflow of the MR-linac, the performance of the treatment starting from the acquisition of pre-treatment MRI scans and ending with dose delivery is quantified. In such a test, the geometrical accuracy of the entire workflow is assessed. Ideally, the 3D geometrical accuracy of dose delivery on an MR-linac should be assessed using dosimeters that provide 3D dose distributions. Gel dosimeters, for instance, have proven to be valuable tools for evaluating 3D dose distributions on an MR-linac. In this study, we investigated the use of 3D gel dosimeters for the assessment of the 3D geometrical accuracy and reproducibility of the adaptive procedure on an MR-linac in an E2E verification. Materials and methods:All measurements were performed on a clinical Unity MR-linac using 3D gel dosimeters in an anthropomorphic head phantom. Film measurements were performed as a reference dosimeter. An online adapt-to-shape procedure was performed for each measurement. Results:The geometric accuracy and reproducibility of the gel dosimeter measurements were high, and similar to all in-plane film measurements. The largest shift found was 0.3 mm for the gel dosimeter, and 0.6 mm for the in-plane film measurements. The 3D displacement vectors of the gel dosimeter showed similar uncertainties as the in-plane film 2D displacement vectors. Conclusions:Gel dosimeters can be used for the assessment of the 3D end-to-end geometric accuracy of an MR-linac.

A 3D imaging system for dosimetry in FLASH radiotherapy

This study examines the feasibility of reconstructing the spatial dose distribution delivered by a 9 MeV electron beam within a plastic scintillator block using optical tomography. The system is designed for use in 3D-dosimetry for clinical applications in quality assurance and beam characterization, especially with FLASH-beam sources. The proposed detector reconstructs the 3D dose distribution map employing a monolith with 10 cm side, imaged in four different projections by scientific cameras. The 3D dose distribution simulated with Geant4 was reconstructed using a maximum likelihood expectation maximization algorithm applied to the lateral projection, resulting in a longitudinal image with a standard deviation of 0.2 Gy. Furthermore, a preliminary experimental test has been performed.

Deep learning methods for 2D in-vivo dose reconstruction with EPID detector

Over the past two decades, radiotherapy has seen a steep increase in technological complexity of treatment preparation and treatment execution, calling for the development of more and better quality assurance tools and procedures. In-vivo dosimetry has emerged as a very powerful tool for treatment verification in conjunction with Electronic Portal Imaging Devices (EPIDs). However, EPIDs present several drawbacks like non-water equivalence and cumbersome calibration procedures. Artificial intelligence, specifically Deep Learning (DL), plays a crucial role in this context. A critical step is modeling the EPID response to estimate 2D dose distributions (Portal Dose, PD). This work introduces a DL-based methodology aimed at converting EPID responses into PD images. The proposed procedure is fully data driven, being based on advanced DL methods. This approach will allow bypassing the complex calibration steps needed to overcome the non-water equivalent panel.

Large-language-model empowered 3D dose prediction for intensity-modulated radiotherapy.

Treatment planning is currently a patient specific, time-consuming, and resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radiotherapy plans and organs-at-risk (OAR) and planning target volume (PTV) has been well established. This study explores the potential of deep learning models for predicting DVHs using images and subsequent human intervention facilitated by a large-language model (LLM) to enhance the planning quality. We propose a pipeline to convert unstructured images to a structured graph consisting of image-patch nodes and dose nodes. A novel Dose Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the structured graph. The proposed DoseGNN is enhanced with the LLM to encode massive knowledge from prescriptions and interactive instructions from clinicians. In this study, we introduced an online human-AI collaboration (OHAC) system as a practical implementation of the concept proposed for the automation of intensity-modulated radiotherapy (IMRT) planning. The proposed DoseGNN model was compared to widely employed DL models used in radiotherapy, including Swin Transformer, 3D U-Net CNN, and vanilla MLP. For PTV, DoseGNN achieved the mean absolute error (MAE) of , , , and between true plans and predicted plans that were 64%, 53%, 64%, 61% of the best baseline model. For the worst case among OARs (left lung, right lung, chest wall, heart, spinal cord), DoseGNN achieved the mean absolute error of , , that were 85%, 91%, 80% of the best baseline model. Moreover, the LLM-empowered DoseGNN model facilitates seamless adjustment to treatment plans through interaction with clinicians using natural language. We developed DoseGNN, a novel deep learning model for predicting delivered radiation doses from medical images, enhanced by LLM to allow adjustment through seamless interaction with clinicians. The preliminary results confirm DoseGNN's superior accuracy in DVH prediction relative to typical DL methods, highlighting its potential to facilitate an online clinician-AI collaboration system for streamlined treatment planning automation.

Active dosimetry for VHEE FLASH radiotherapy using beam profile monitors and charge measurements

The discovery of the FLASH effect has revealed a high potential for treating cancer more efficiently by sparing healthy tissue. The surge in related medical research activities over the last couple of years has triggered a demand for technology with the capability of generating and measuring ionizing radiation at ultra-high dose-rates (UHDR). A reliable dosimetry system is an integral part of a radiotherapy machine. Because existing active dosimetry methods are unable to handle the dose-rates required for FLASH, UHDR dosimetry has emerged as an important area of research. In this paper we present an active dosimetry method based on a scintillating screen and an integrating current transformer. This method provides a simultaneous measurement of the absolute dose delivery as well as the 2D dose distribution. The measurements have been correlated with corresponding readings from radiochromic films (RCFs), and a procedure for image processing has been established. Moreover, different methods of calibrating the active dosimetry system against RCFs have been introduced and evaluated. Lastly, we present results which demonstrate that an agreement with RCFs of better than 5% can be realistically expected if camera parameters are carefully optimized.

Exploring the impact of field shape on predicted dose distribution in breast cancer patients using deep learning in radiation therapy

BackgroundGeometrical information such as field shape is essential for dose calculation in radiation therapy. However, new methods of dose prediction based on deep learning only use CT images and contouring of patients. The aim of this study is to develop a deep learning method for three-dimensional dose prediction for breast cancer radiotherapy using field shape as well as CT and contouring images of patients. MethodsA total of 150 breast cancer patients' data treated with 3D conformal radiation therapy (CRT) technique were used to train and test 3D-U-Res-F-Net model. The model inputs were CT images, patients' contouring and field shape. The network output was the corresponding the patients’ dose distribution. Then, the dosimetric parameters extracted from the dose volume histograms related to the planned and predicted dose distributions were compared and were statistically analysed with the paired-samples t-test. These parameters include Dmean and Dmax for planning target volume and organs at risk, D95%, D50%, V47.5Gy for PTV, V30Gy and V25Gy for heart and V20Gy for left lung. The gamma index with 3%/3 mm criteria was calculated for PTV and OARs. ResultsThe average absolute difference of the Dmean relative to the prescribed dose for the PTV, heart, left lung, right lung and spinal cord were 1.37 ± 0.4%, 2.02 ± 2%, 2.12 ± 1%, 0.37 ± 0.3% and 0.41 ± 0.3%, respectively. The average absolute difference of the Dmax relative to the prescribed dose for the PTV and OARs varied from 0.23 ± 0.2% to 2.04 ± 2%. The 3D gamma pass rate with 3mm/3% criteria for planning target volume, heart, left lung, right lung, spinal cord and body were 89 ± 3%, 91 ± 5%, 93 ± 5%, 99 ± 3%, 99 ± 1% and 96 ± 2% respectively. ConclusionsThe results of the proposed model demonstrate that there is no significant difference between the predicted and planned dose distributions. The deep learning model can directly predict the 3D dose distribution faster than treatment planning systems. Further studies with more patients and on other cancer sites are essential to fully validate the proposed method.

Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization

Background and PurposeTreatment planning is a time-intensive task that could be automated. We aimed to develop a “single-click” workflow, fully deployed within a commercial treatment planning system (TPS), for autoplanning prostate radiotherapy treatment plans using predictions from a deep learning model (DLM). Materials and MethodsAutomatically generated treatment plans were created with a single script, executed from within a commercial TPS scripting environment, that performed two stages sequentially. Initially, a 3D dose distribution was predicted with a ResUNet DLM. The DLM was trained and validated using previously treated datasets (n = 120) which used 3D contours as inputs. Following this, dose predictions were converted into treatment plans by extracting dose-volume metrics from the predictions to use as objectives for the inverse optimizer within the TPS. An independent test dataset (n = 20) was used to evaluate the similarity between automated and clinical plans. ResultsFor planning target volumes, the median percentage difference and interquartile range between the automatically generated plans and clinical plans were 0.4% [0.2-1.1%] for the V100%, −0.5% [(−1.0)-(−0.2)%] for D99% and −0.5% [(−1.0)-(−0.2)%] for D95%. Bladder and rectum volume-at-dose objectives agreed within −6.1% [(−12.5)-0.9%]. The conversion of the DLM prediction into a treatment plan took 15 min [13-16 min]. ConclusionsAn automatic plan generation workflow that uses a DL model with scripted optimization was fully deployed in a commercial TPS. Autoplans were compared to previously treated clinical plans and were found to be non-inferior.

Assessment of using an Octavius 4D Measuring System for Patient-specific VMAT Quality Assurance in Togo

Purpose and objective: Quality assurance (QA) programs are designed to improve the quality and the safety of radiation treatments, including machine- and patient-specific QA (PSQA). The objective of this study was to evaluate the current state of PSQA practice and identify the area for potential improvement for VMAT delivery. Materials and methods: The Octavius 4D (O4D) system accuracy was evaluated using an O4D homogeneous phantom for different field sizes. The system response to dose linearity, field sizes, and PDD difference tests were performed against the calculated dose of the treatment planning system (TPS) for a 6 MV photon beam. The deviation of the delivered dose was evaluated at the isocenter and different depths. Moreover, pretreatment verification of 40 VMAT plans was performed including prostate cancer (PC), head and neck cancer (HNC), uterine and cervical cancer (UCC), and breast cancer (BC). The PTW VeriSoft software was used to perform the local and global 3D gamma analysis by comparing the reconstructed 3D dose against the calculated dose using criteria 2%/2 mm and 3%/3 mm, 20% of low-dose threshold, and 95% of gamma passing rate (%GP) tolerance level. In the clinical scenario, the sensitivity of the O4D system in detecting VMAT delivery and setup errors has been investigated by measuring the variation of %GP values before and after the simulated errors using one of the VMAT plans related to each treatment site. Results: The O4D system reported good agreement for linearity, field size, and PDD differences with TPS dose being within ± 2% tolerance for a 6 MV photon beam. Output factors were consistent between the ionization chamber and the O4D detector 1500 array down to 4 x 4 cm2 field size with a maximum deviation of less than 1%. The introduction of deliberate errors caused the decrease of %GP values. In most scenarios, the %GP value of simulated errors was detected with 2%/2 mm and ranged between the detection threshold and gamma passing threshold. Conclusion: The results indicate that the O4D system is sensitive to detect delivery and setup errors with restrictive criteria of 2%/2 mm for routine pretreatment verification. Moreover, this system should be used in combination with kV-CBCT to improve dosimetry accuracy and treatment reproducibility.

A review of diamond dosimeters in advanced radiotherapy techniques.

This review article synthesizes key findings from studies on the use of diamond dosimeters in advanced radiotherapy techniques, showcasing their applications, challenges, and contributions to enhancing dosimetric accuracy. The article explores various dosimeters, highlighting synthetic diamond dosimeters as potential candidates especially due to their high spatial resolution and negligible ion recombination effect. The clinically validated commercial dosimeter, PTW microDiamond (mD), faces limitations in small fields, proton and hadron therapy and ultra-high dose per pulse (UHDPP) conditions. Variability in reported values for field sizes 2 2 is noted, reflecting the competition between volume averaging and density perturbation effects. PTW's introduction of flashDiamond (fD) holds promise for dosimetric measurements in UHDPP conditions and is reliable for commissioning ultra-high dose rate (UHDR) electron beam systems, pending the clinical validation of the device. Other advancements in diamond detectors, such as in 3D configurations and real-time dose per pulse x-ray detectors, are considered valuable in overcoming challenges posed by modern radiotherapy techniques, alongside relative dosimetry and pre-treatment verifications. The studies discussed collectively provide a comprehensive overview of the evolving landscape of diamond dosimetry in the field of radiotherapy, and offer insights into future directions for research and development in thefield.

Diffusion Correction in Fricke Hydrogel Dosimeters: A Deep Learning Approach with 2D and 3D Physics-Informed Neural Network Models.

In this work an innovative approach was developed to address a significant challenge in the field of radiation dosimetry: the accurate measurement of spatial dose distributions using Fricke gel dosimeters. Hydrogels are widely used in radiation dosimetry due to their ability to simulate the tissue-equivalent properties of human tissue, making them ideal for measuring and mapping radiation dose distributions. Among the various gel dosimeters, Fricke gels exploit the radiation-induced oxidation of ferrous ions to ferric ions and are particularly notable due to their sensitivity. The concentration of ferric ions can be measured using various techniques, including magnetic resonance imaging (MRI) or spectrophotometry. While Fricke gels offer several advantages, a significant hurdle to their widespread application is the diffusion of ferric ions within the gel matrix. This phenomenon leads to a blurring of the dose distribution over time, compromising the accuracy of dose measurements. To mitigate the issue of ferric ion diffusion, researchers have explored various strategies such as the incorporation of additives or modification of the gel composition to either reduce the mobility of ferric ions or stabilize the gel matrix. The computational method proposed leverages the power of artificial intelligence, particularly deep learning, to mitigate the effects of ferric ion diffusion that can compromise measurement precision. By employing Physics Informed Neural Networks (PINNs), the method introduces a novel way to apply physical laws directly within the learning process, optimizing the network to adhere to the principles governing ion diffusion. This is particularly advantageous for solving the partial differential equations that describe the diffusion process in 2D and 3D. By inputting the spatial distribution of ferric ions at a given time, along with boundary conditions and the diffusion coefficient, the model can backtrack to accurately reconstruct the original ion distribution. This capability is crucial for enhancing the fidelity of 3D spatial dose measurements, ensuring that the data reflect the true dose distribution without the artifacts introduced by ion migration. Here, multidimensional models able to handle 2D and 3D data were developed and tested against dose distributions numerically evolved in time from 20 to 100 h. The results in terms of various metrics show a significant agreement in both 2D and 3D dose distributions. In particular, the mean square error of the prediction spans the range 1×10-6-1×10-4, while the gamma analysis results in a 90-100% passing rate with 3%/2 mm, depending on the elapsed time, the type of distribution modeled and the dimensionality. This method could expand the applicability of Fricke gel dosimeters to a wider range of measurement tasks, from simple planar dose assessments to intricate volumetric analyses. The proposed technique holds great promise for overcoming the limitations imposed by ion diffusion in Fricke gel dosimeters.