Dose Calculation Research Articles

PE-CycleGAN network based CBCT-sCT generation for nasopharyngeal carsinoma adaptive radiotherapy.

To explore the synthesis of high-quality CT (sCT) from cone-beam CT (CBCT) using PE-CycleGAN for adaptive radiotherapy (ART) for nasopharyngeal carcinoma. A perception-enhanced CycleGAN model "PE-CycleGAN" was proposed, introducing dual-contrast discriminator loss, multi-perceptual generator loss, and improved U-Net structure. CBCT and CT data from 80 nasopharyngeal carcinoma patients were used as the training set, with 7 cases as the test set. By quantifying the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), as well as the dose gamma pass rate and the relative dose deviations of the target area and organs at risk (OAR) between sCT and reference CT, the image quality and dose calculation accuracy of sCT were evaluated. The MAE of sCT generated by PE-CycleGAN compared to the reference CT was (56.89±13.84) HU, approximately 30% lower than CBCT's (81.06±15.86) HU (P<0.001). PE-CycleGAN's PSNR and SSIM were 26.69±2.41dB and 0.92±0.02 respectively, significantly higher than CBCT's 21.54±2.37dB and 0.86±0.05 (P<0.001), indicating substantial improvements in image quality and structural similarity. In gamma analysis, under the 2 mm/2% criterion, PE-CycleGAN's sCT achieved a pass rate of (90.13±3.75)%, significantly higher than CBCT's (81.65±3.92)% (P<0.001) and CycleGAN's (87.69±3.50)% (P<0.05). Under the 3 mm/3% criterion, PE-CycleGAN's sCT pass rate of (90.13±3.75)% was also significantly superior to CBCT's (86.92±3.51)% (P<0.001) and CycleGAN's (94.58±2.23)% (P<0.01). The mean relative dose deviation of the target area and OAR between sCT and planned CT was within ±3% for all regions, except for the Lens Dmax (Gy), which had a deviation of 3.38% (P=0.09). The mean relative dose deviations for PTVnx HI, PTVnd HI, PTVnd CI, PTV1 HI, PRV_SC, PRV_BS, Parotid, Larynx, Oral, Mandible, and PRV_ON were all less than ±1% (P>0.05). PE-CycleGAN demonstrates the ability to rapidly synthesize high-quality sCT from CBCT, offering a promising approach for CBCT-guided adaptive radiotherapy in nasopharyngeal carcinoma.

Nuclear interaction correction based on dual-energy computed tomography in carbon-ion radiotherapy.

&#xD;Accurate dose predictions are crucial to maximizing the benefits of carbon-ion therapy. Carbon beams incident on the human body cause nuclear interactions with tissues, resulting in changes in the constituent nuclides and leading to dose errors that are conventionally corrected using conventional single-energy computed tomography (SECT). Dual-energy computed tomography (DECT) has frequently been used for stopping power estimation in particle therapy and is well suited for correcting nuclear reactions because of its detailed body-tissue elemental information. This study proposes a correction method for the absolute dose in carbon-ion therapy that considers changes in nuclide composition resulting from nuclear reactions with body tissues, as a novel application of DECT.&#xD;Approach:&#xD;The change in dose associated with nuclear reactions is determined by correcting each integrated depth dose component of the carbon beam using a nuclear interaction correction factor. This factor is determined based on the stopping power, mass density, and nuclear interaction cross-section in body tissue. The stopping power and mass density were calculated using established methods, whereas the nuclear interaction cross-section was newly defined through a conversion equation derived from the effective atomic number.&#xD;Main results:&#xD;Nuclear interaction correction factors and corrected doses were determined for 85 body tissues with known compositions, comparing them with existing SECT-based methods. The root-mean-square errors of the SECT- and DECT-based nuclear interaction correction factors relative to theoretical values were 0.66% and 0.39%, respectively.&#xD;Significance:&#xD;This indicates a notable enhancement in the estimation accuracy with DECT. The dose calculations in uniform body tissues derived from SECT showed slight over-correction in adipose and bone tissues, whereas those based on DECT were almost consistent with theoretical values. Our proposed method demonstrates the potential of DECT for enhancing dose calculation accuracy in carbon-ion therapy, complementing its established role in stopping power estimation.&#xD.

A library of proton lineal energy spectra spanning the full range of clinically relevant energies.

In locations where the proton energy spectrum is broad, lineal energy spectrum-based proton biological effects models may be more accurate than dose-averaged linear energy transfer (LETd) based models. However, the development of microdosimetric spectrum-based biological effects models is hampered by the extreme computational difficulty of calculating microdosimetric spectra. Given a precomputed library of lineal energy spectra for monoenergetic protons, a weighted summation can be performed which yields the lineal energy spectrum of an arbitrary polyenergetic beam. Using this approach, lineal energy spectra can be rapidly calculated on a voxel-by-voxel level. Monoenergetic proton tracks generated using Geant4-DNA were imported into SuperTrack, a GPU-accelerated software for calculation of microdosimetric spectra. Libraries of proton lineal energy spectra which span the energy range of 0-300MeV were computed. The libraries were validated by comparison to Monte Carlo calculations in the literature, as well as lineal energy spectra measured experimentally with a tissue equivalent proportional counter. The lineal energy libraries have been made available in three data formats, two are plain-text,.csv and.les, and one binary encoded, root. Library files include the lineal energy bin abscissa in keV per micron and the unnormalized number of counts occurring within that bin. A computational technique for summation of the library files to yield the lineal energy of a polyenergetic beam is described in this work. The lineal energy libraries can be used to rapidly determine the lineal energy spectra at the location of cell cultures for in-vitro experiments and in each voxel of a treatment plan for in-vivo outcome modelling. These libraries have already been incorporated into RayStation 2023B-IonPG for lineal energy spectra calculation and we anticipate they will be incorporated into further dose calculation engines and Monte Carlo toolkits.

Clinical Application Of Deep Learning-assisted Needles Reconstruction In Prostate Ultrasound Brachytherapy.

High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often employed during the procedure, minimizing overall planning time is crucial. In this study, we explore the clinical feasibility and time-saving potential of artificial intelligence (AI)-driven auto-reconstruction of transperineal needles in the context of US-guided prostate BT planning. This study included a total of 102 US-planned BT images from a single institution and split into three groups: 50 for model training and validation, 11 to evaluate reconstruction accuracy (test set), and 41 to evaluate the AI tool in a clinical implementation (clinical set). Reconstruction accuracy for the test set was evaluated by comparing the performance of AI-derived and manually reconstructed needles from 5 medical physicists on the 3D-US scans after treatment. The needle total reconstruction time for the clinical set was defined as the timestamp difference from scan acquisition to the start of dose calculations and was compared to values recorded before the clinical implementation of the AI-assisted tool. A mean error of (0.44±0.32)mm was found between the AI-reconstructed and the human consensus needle positions in the test set, with 95.0% of AI needle points falling below 1mm from their human-made counterparts. Post-hoc analysis showed only one of the human observers' reconstructions were significantly different from the others including the AI's. In the clinical set, the AI algorithm achieved a true positive reconstruction rate of 93.4% with only 4.5% of these needles requiring manual corrections from the planner before dosimetry. Total time required to perform AI-assisted catheter reconstruction on clinical cases was on average 15.2min lower (p < 0.01) compared to procedure without AI assistance. This study demonstrates the feasibility of an AI-assisted needle reconstructing tool for 3D-US based HDR prostate BT. This is a step toward treatment planning automation and increased efficiency in HDR prostate BT.

Evaluation of dose calculation method with a combination of Monte Carlo method and removal-diffusion equation in heterogeneous geometry for boron neutron capture therapy.

Clinical research in boron neutron capture therapy (BNCT) has been conducted worldwide. Currently, the Monte Carlo (MC) method is the only dose calculation algorithm implemented in the treatment planning system for the clinical treatment of BNCT. We previously developed the MC-RD calculation method, which combines the MC method and the removal-diffusion (RD) equation, for fast dose calculation in BNCT. This study aimed to verify the partial-MC-RD calculation method, which utilizes the MC-RD calculation method for a portion of the entire neutron energy range, in terms of calculation accuracy and time as the dose calculation method. We applied the partial-MC-RD calculation method to calculate the total dose for head phantom, comprising soft tissue, brain tissue, and bone. The calculation time and accuracy were evaluated based on the full-MC method. Our accuracy verifications indicated that the partial-MC-RD calculation was mostly comparable with full-MC calculation in the accuracy. However, the assumptions and approximation used in the RD calculation mainly occurred the discrepancy from the full-MC calculation result. Additionally, the partial-MC-RD calculation reduced the time required to approximately 45% for the irradiation to the top and cheek region of head phantom, compared to the full-MC calculation. In conclusion, the MC-RD calculation method can be the basis of a fast dose calculation method in BNCT.

Does extending the body contour into air enhance surface dose calculation accuracy?

In radiotherapy treatment planning systems, modelling of superficial dose may be aided by a body contour that is, by default, placed at the outermost air-tissue interface. Here we investigate the accuracy of superficial dose calculated using either the default body contour (DBC) or an extended body contour (EBC) compared to radiochromic film measurements made on a slab phantom and an anthropomorphic phantom. Depth dose curves in the superficial region of the slab phantom were measured using stacked radiochromic films and irradiated using static beams delivered from varying incident angles. Surface doses on the anthropomorphic phantom were measured using films taped to the outside and irradiated with dynamic beams targeting the skin. No intervening immobilization devices were between the incident beams and films. Measurements were compared with dose calculations performed with Eclipse 15.6 Acuros XB using both the DBC and EBC. The EBC was generated by extending the DBC out into air by 1 cm. Differences between the DBC- and EBC-calculated dose were largest within the region ranging from the surface to a depth of 2 mm. Beyond 2 mm, calculations agreed to within 5%. For the slab phantom, the DBC- and EBC-calculated surface doses for 0-degree incident angle were 26% and 44% respectively, while measured dose was 24 ± 3%. At 60-degrees incident angle, DBC- and EBC-calculated surface doses were 33% and 66% respectively, while the measured dose was 43 ± 3%. For the anthropomorphic phantom, the DBC- and EBC-calculated surface doses were 65% and 99% of the prescription dose respectively, while the measured surface dose was 76 ± 3%. Calculating with the EBC overestimated surface dose, while the DBC underestimated surface dose under conditions of oblique delivery. Overall, the default method of placing the body contour at the air-tissue interface produced a more accurate estimation of surface dose for bare skin.

Dosimetry Software for Theranostic Applications: Current Capabilities and Future Prospects.

Dosimetry is integral to informed implementation of radiopharmaceutical therapies, enabling personalized treatment planning and ensuring patient safety by calculating absorbed doses to organs and tumors. As the therapeutic radiopharmaceutical field continues to expand, dosimetry software has emerged as a crucial tool for optimization of treatment efficacy. This review discusses key features and capabilities that current dosimetry software solutions have or should have in the future. We highlight the need for standardization across platforms to support consistent and accurate dose calculations. Furthermore, we explore opportunities for advancing software, such as incorporating biologically effective dose modeling and improving uncertainty quantification. Looking ahead, we advocate for expanding infrastructure for open data sets and fostering ongoing collaboration between vendors and end users to guide the field toward greater integration and efficacy.

Knowledge-based planning, multicriteria optimization, and plan scorecards: A winning combination

Background and purposeThe ESTRO 2023 Physics Workshop hosted the Fully-Automated Radiotherapy Treatment Planning (Auto-RTP) Challenge, where participants were provided with CT images from 16 prostate cancer patients (6 prostate only, 6 prostate + nodes, and 4 prostate bed + nodes) across 3 challenge phases with the goal of automatically generating treatment plans with minimal user intervention. Here, we present our team’s winning approach developed to swiftly adapt to both different contouring guidelines and treatment prescriptions than those used in our clinic. Materials and methodsOur planning pipeline comprises two main components: 1) auto-contouring and 2) auto-planning engines, both internally developed and activated via DICOM operations. The auto-contouring engine employs 3D U-Net models trained on a dataset of 600 prostate cancer patients for normal tissues, 253 cases for pelvic lymph node, and 32 cases for prostate bed. The auto-planning engine, utilizing the Eclipse Scripting Application Programming Interface, automates target volume definition, field geometry, planning parameters, optimization, and dose calculation. RapidPlan models, combined with multicriteria optimization and scorecards defined on challenge scoring criteria, were employed to ensure plans met challenge objectives. We report leaderboard scores (0–100, where 100 is a perfect score) which combine organ-at-risk and target dose-metrics on the provided cases. ResultsOur team secured 1st place across all three challenge phases, achieving leaderboard scores of 79.9, 77.3, and 78.5 outperforming 2nd place scores by margins of 6.4, 0.4, and 2.9 points for each phase, respectively. Highest plan scores were for prostate only cases, with an average score exceeding 90. Upon challenge completion, a “Plan Only” phase was opened where organizers provided contours for planning. Our current score of 90.0 places us at the top of the “Plan Only” leaderboard. ConclusionsOur automated pipeline demonstrates adaptability to diverse guidelines, indicating progress towards fully automated radiotherapy planning. Future studies are needed to assess the clinical acceptability and integration of automatically generated plans.

A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.

Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic level. DL-SpiQA was evaluated based on retrospective testing of spine radiation therapy treatments and prospective clinical deployment. The DL-SpiQA workflow involves auto-segmentation and labelling of all vertebral volumes on CT imaging using TotalSegmentator, an open-source deep learning algorithm based on nnU-Net, calculation of the radiation dose to each vertebra, and flagging and categorisation of potential treatments at the wrong anatomic level with automated email reports sent to involved radiation therapy personnel. We developed the DL-SpiQA tool based on retrospective clinical data from patients treated with palliative spine radiation therapy from sites included in the multicentre hospital network between Feb 12, 2014, and Nov 15, 2022. We used historic cases of patients who had a near-miss (ie, wrong-anatomic-level errors caught before the patient was treated) or had received wrong-anatomic-level treatment to test whether the tool could identify known errors successfully. We then used the tool prospectively over 15 months (April 24, 2023, to July 22, 2024) to evaluate any new spine radiation therapy treatment plan created for a patient, looking for any targeting errors, and dose and volume discrepancies. An email report was circulated with all the radiation therapy personnel; if any errors were found, these were highlighted and each error was defined. The tool was internally validated. All cases flagged by DL-SpiQA for both the retrospective and prospective studies were manually reviewed for dosimetric targeting, variant spine anatomy or spinal anomalies, and artificial intelligence (AI) segmentation errors. DL-SpiQA was further validated based on false positive and negative rates estimated from the retrospective results. DL-SpiQA was first tested retrospectively on 513 patients with segmentation of 10 106 vertebrae. The system raised flags for ten dose discrepancies, 49 normal anatomic variants, 49 cases with implants or other anomalies, and 20 segmentation errors (4% false positive rate). DL-SpiQA caught one historic treatment at the wrong anatomic level and three near-misses. DL-SpiQA was then prospectively deployed, reviewing 520 cases and identifying six documentation errors, which triggered detailed review by clinicians, and 43 additional cases, which confirmed clinical knowledge of variant anatomy. In all detected cases (ie, 49 of 520 cases in total), the appropriate personnel were alerted. A false negative rate of 0·03% is estimated based on the 4% AI segmentation error rate and the frequency of reported spine radiation therapy errors. The low false positive rate, the low false negative rate, and the high accuracy in flagging errors show that DL-SpiQA is an effective, AI-driven, automated quality assurance tool that could be used to identify anatomic spine variants and errors in targeting at the anatomic level. The tool could therefore help improve the safety of spine radiotherapy. Further external validation and tailoring is needed. None.

Proton dose deposition in heterogeneous media: A TOPAS Monte Carlo simulation study.

This study investigated the influence of tissue electron density on proton beam dose distribution using TOPAS Monte Carlo simulation. Heterogeneous tissue models composed of 14 materials were constructed to simulate the dose deposition process of a 169.23MeV proton beam. The study analyzed the relationships between electron density and key parameters such as maximum dose, total dose, and dose distribution. Results showed that increasing electron density led to higher local maximum dose, lower total dose, and decreased Bragg peak depth, range, penumbra width, and full width at half maximum (FWHM). High-density tissues caused a sharp, concentrated Bragg peak at shallower depths, while low-density tissues caused a backward shift and widening of the Bragg peak. Differences in proton energy deposition in various tissues were the fundamental reasons for dose distribution variations. This study quantified the relationship between electron density and proton beam dose distribution, providing a reference for accurate dose calculation and optimization in proton therapy.

A method to obtain optimal relative electron densities of metallic samples for a density scaling-based commercial photon dose calculation algorithm

A method to obtain optimal relative electron densities of metallic samples for a density scaling-based commercial photon dose calculation algorithm

Influence of spatial redistribution of heterogeneities in proton beam characteristics.

The purpose of this study was to investigate the fundamental properties of spot-scanning proton beams and compare them to Monte Carlo (MC) simulations, both with and without CT calibration, using spatially diverse combinations of materials. A heterogeneous phantom was created by spatially distributing titanium, wax, and thermocol to generate six scenarios of heterogeneous combinations. Proton pencil beams ranging in energy from 100 to 226.2MeV were directed perpendicular to each heterogeneous combination, and the exit proton was measured using a Lynx scintillation detector and a Zebra Multi-Layer-Ionization-Chamber for depth dose and spot profile measurements, respectively. The identical measurement configuration was duplicated in the RayStation-TPS. The measured and simulated RayStation-MC beam characteristics were compared. The results showed that at 100MeV, the mean standard deviation of spot size was 5.66±0.27mm, while at 226.2MeV, it rapidly decreased to 3.37±0.07mm. The physical phantom showed a larger perturbation difference between measurement and MC simulation than the virtual phantom. MC overestimates ranges up to 1.5% in virtual phantoms, but underestimates ranges up to 5% in physical phantoms. Range perturbations over 1mm occurred in 35.7% of virtual phantom measurements and in 85.7% of physical phantom measurements. Despite using a CT artefact reduction approach and an accurate Monte-Carlo dose calculation algorithm, perturbations in proton characteristics were still observed. It is essential to be aware of the limits of the TPS in managing such heterogeneous combinations. It is recommended to perform more validation checks on heterogeneous combinations than on individual materials.

Generation of deep learning based virtual non-contrast CT using dual-layer dual-energy CT and its application to planning CT for radiotherapy.

This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficient data pairs of contrast-enhanced and non-contrast CT images, we trained our model on dual-energy CT (DECT) images, using virtual non-contrast CT (VNC CT) images as outputs instead of true non-contrast CT images. We used a deterministic method to convert CE-pCT images into pseudo DECT images for model application. Model training and evaluation were conducted on 45 patients. The performance of our model, 'VNC-Net', was evaluated using various metrics, demonstrating high scores for quantitative performance. Moreover, our model accurately replicated target VNC CT images, showing close correspondence in CT numbers. The versatility of our model was further demonstrated by applying it to pseudo VNC DECT generation, followed by conversion to VNC-pCT. CE-pCT images of ten liver cancer patients and ten left-sided breast cancer patients were used. A quantitative comparison with true non-contrast planning CT (TNC-pCT) images validated the accuracy of the generated VNC-pCT images. Furthermore, dose calculations on CE-pCT and VNC-pCT images from patients undergoing volumetric modulated arc therapy for liver and breast cancer treatment showed the clinical relevance of our approach. Despite the model's overall good performance, limitations remained, particularly in maintaining CT numbers of bone and soft tissue less influenced by contrast agent. Future research should address these challenges to further improve the model's accuracy and applicability in radiotherapy planning. Overall, our study highlights the potential of deep learning models to improve imaging protocols and accuracy in radiotherapy planning.

Edpuzzle Application in Improving Nursing Students' Metric and Drug Dose Calculation Skills in a Virtual Learning Environment: Mixed Methods Study

Objective: This study aims to determine the effect of Edpuzzle application on nursing students' metric conversion and drug dose calculation skills. Methods: 84 nursing students participated in parallel groups in the mixed model single-blind randomized controlled study, conducted between April and June 2021.Data were collected using a descriptive features form, metric and drug dose calculation skill test. After all students completed the theoretical lessons, a pre-application of the skill test was made. After the experimental group students watched the interactive videos in the Edpuzzle application for fifteen days, a final skill test was applied to all students. Then, four focus group interviews were conducted with 28 experimental group students. Results: The last scores of the students in the experimental group, pertaining to metric and drug dose calculation skill test, were higher than those of the students in the control group and were statistically significant (p

Local anesthetic systemic toxicity after brachial plexus block with ropivacaine: a case report

The aim of this article is presentation of clinical case of local anesthetic systemic toxicity (LAST). Local anesthetic systemic toxicity is a rare but life-threatening complication. Ropivacaine is widely used for the regional blocks and rarely leads to the development of LAST. LAST may develop due to intravascular injection or high dose of ropivacaine. We present the case of 48-year-old-female (ASA I, 56 kg, and 165 cm), who underwent surgery on the right shoulder under brachial plexus block. Brachial plexus nerve block was performed by interscalene access with introduction of 40 ml of 0.75% ropivacaine (300 mg). Twenty minutes after the injection, complete sensory/motor block was achieved. Twenty-five minutes after the injection, the patient complained of numbness in the tongue, tinnitus and dizziness. During this time, her blood pressure was 132/83 mmHg, heart rate 82 beats/min. oxygen saturation 97%. No changes were observed on the ECG. Suspecting ropivacaine-induced toxicity, 50 mg of thiopental sodium followed by 2 mg of midazolam were administered for seizure prophylaxis, and supplemental oxygen was given via face mask. Patient’s state improved in 10 minutes. Ten minutes later 20% intralipid emulsion bolus during 2-3 min was administered followed by continuous infusion at a dose of0.25 ml/kg/min. Against clinical stablility and consciousness of the patient, decision was made to proceed with surgery, which was uneventful. Implementation of preventive measures can decrease the possibility for development of complication and emergency treatment can safe patient’s health and life. Preventive measures should include implementation of ultrasound nerve location guidance, individual calculation of local anesthetic dose, taking into account sex, weight, and physical status of a patient.

Validation of a rapid algorithm for repeated intensity modulated radiation therapy dose calculations

As adaptive radiotherapy workflows and deep learning model training rise in popularity, the need for repeated applications of a rapid dose calculation algorithm increases. In this work we evaluate the feasibility of a simple algorithm that can calculate dose directly from MLC positions in near real-time. Given the necessary machine parameters, the intensity modulated radiation therapy (IMRT) doses are calculated and can be used in optimization, deep learning model training, or other cases where fast repeated segment dose calculations are needed. The algorithm uses normalized beamlets to modify a pre-calculated patient specific open field into any MLC segment shape. This algorithm was validated on 91 prostate IMRT plans as well as 20 lung IMRT plans generated for the Elekta Unity MR-Linac. IMRT plans calculated using the proposed method were found to match reference Monte Carlo calculated dose within98.02±0.84%and96.57±2.41%for prostate and lung patients respectively with a 3%/2 mm gamma criterion. After the patient-specific open field calculation, the algorithm can calculate the dose of a 9-field IMRT plan in 1.016±0.284 s for a single patient or 0.264 ms per patient for a parallelized batch of 24 patients relevant for deep learning training. The presented algorithm demonstrates an alternative rapid IMRT dose calculator that does not rely on training a deep learning model while still being competitive in terms of speed and accuracy making it a compelling choice in cases where repetitive dose calculation is desired.

Comparative Study of Cardiac Radiation Dose With Different Types of Surgery in Breast Cancer Patients

Background: It has been demonstrated that radiation therapy lowers both the death rate from breast cancer and its recurrence. Precise calculation of the radiation dose is crucial for treating the target site as well as protecting vital organs like the heart since large doses of radiation therapy significantly increase patient morbidity and death. Objective: To compare the mean heart dose of radiation in breast cancer patients between breast-conserving surgery versus mastectomy, between different radiotherapy doses and fractionation schedules, and between right and left breast cancer irradiation. Patients and Methods: This is a cross-sectional descriptive retrospective comparative study that was conducted in Baghdad Radiotherapy Center from January 2018 to June 2018, carried on 174 breast cancer patients of different age groups selected randomly and their mean heart dose data collected from their files and database in Baghdad Radiotherapy Center. Results: The overall average of the mean dose was 372 cGy (range from 76.4 to 716.2). The greatest difference in the mean heart dose was between (BCS) patients who received 5000 cGy with regional nodal irradiation and (BCS) patients who received 4005 cGy also with regional nodal irradiation (difference in the mean is 639.8, the P – value &lt;0.001). Regarding the side of breast cancer, the greatest difference in mean heart dose was seen between left and right breast cancer patients who did the same type of surgery (MRM) and received the same dose of radiotherapy (4256 cGy) (difference in the mean is 565cGy and the P – value &lt;0.001). No statistically significant difference in the mean dose between breast-conserving surgery and mastectomy was recorded. Conclusion: The mean heart dose of radiotherapy is significantly increased in left-sided breast cancer irradiation as compared to the right side. A dose of 5000 cGy has the greatest effect on the dose received by the heart, especially in left breast cancer. The type of surgery whether breast-conserving surgery or mastectomy did not affect the mean dose received by the heart. Keywords: Breast cancer, cardiac radiation dose, breast cancer surgery, breast cancer treatment.

An AI dose-influence matrix engine for robust pencil beam scanning protons therapy.

Rapid planning is of tremendous value in proton pencil beam scanning (PBS) therapy in overcoming range uncertainty. However, the dose calculation of the dose influence matrix (Dij) in robust PBS plan optimization is time-consuming and requires substantial acceleration to enhance efficiency. To accelerate the Dij calculations in PBS therapy, we developed an AI-Dij engine integrated into our in-house treatment planning system (TPS). The AI-Dij engine calculates spot dose using a transformer-based spot dose calculation model (SDM), which takes CT volumes (CT-bars, 256 16 16 voxels, 3mm resolution) and energy (a float value) as inputs and outputs the spot dose distribution (256 16 16). The SDM was trained on over 200 000 CT-bars and Monte Carlo (MC) spot dose (spanning energy levels from 70 to 225 MeV). Clinical-implemented treatment plans for the head, lung, and liver, initially created on Raystation, were replanned using our AI-Dij engine under identical gantry angles and uncertainties settings. After optimizing the spot weight, each in-house plan was recalculated using MCsquare for MC dose evaluation. The dose-volume histogram (DVH) metrics from the in-house TPS and Raystation were compared, evaluating both the optimized and MC doses. In optimization, the differences of DVH metrics (%, Valuein-house-ValueRaystation) across all uncertainty scenarios between the in-house and Raystation plans were 0.93±2.04% for clinical target volume (CTV) and -5.94±12.19% for organ at risks (OARs). For the MC doses, the differences were 2.48±2.78% for CTV and -5.47±14.16% for OARs. The time cost of a robust AI-Dij calculation can be within 2s on an RTX3090 GPU. We conducted a feasibility study on AI-Dij engine-based robust PBS plan optimization, demonstrating both high planning speed and quality.

An activity concentration-based proposal for radon management in workplaces involving NORM in Canada.

The Canadian Guidelines for the Management of Naturally Occurring Radioactive Materials (NORM) have been developed to manage radiation doses received in workplaces involving NORM, such as mineral extraction and processing, oil and gas production, metal recycling or water treatment facilities. This management strategy works well for most naturally occurring radioactive materials in workplaces, with the exception of radon. Radon is a naturally occurring radioactive gas generated by the decay of uranium-bearing minerals in rocks and soils. Because radon exists everywhere in varying concentrations, it is not feasible to use incremental radon generated or enhanced by a practice as a means for assessing the need for radon management programs. Drawing from lessons learned through implementing the current NORM Guidelines, we propose decoupling the decision thresholds for NORM management (excluding radon) and radon management so that the two are considered separately, and quantifying decision-points for managing occupational radon exposure as average annual activity concentrations, with no requirement for dose calculations. Proposed application of this approach in the updated Canadian NORM Guidelines is described.

Unsupervised Bayesian generation of synthetic CT from CBCT using patient-specific score-based prior.

Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and incorrect Hounsfield unit (HU) values hinder their application in quantitative tasks such as target and organ segmentations and dose calculation. Therefore, acquiring CT-quality images from the CBCT scans is essential to implement online ART in clinical settings. This work aims to develop an unsupervised learning method using the patient-specific diffusion model for CBCT-based synthetic CT (sCT) generation to improve the image quality of CBCT. The proposed method is in an unsupervised framework that utilizes a patient-specific score-based model as the image prior alongside a customized total variation (TV) regularization to enforce coherence across different transverse slices. The score-based model is unconditionally trained using the same patient's planning CT (pCT) images to characterize the manifold of CT-quality images and capture the unique anatomical information of the specific patient. The efficacy of the proposed method was assessed on images from anatomical sites including head and neck (H&N) cancer, pancreatic cancer, and lung cancer. The performance of the proposed CBCT correction method was evaluated using quantitative metrics, including mean absolute error (MAE), non-uniformity (NU), and structural similarity index measure (SSIM). Additionally, the proposed algorithm was benchmarked against other unsupervised learning-based CBCT correction algorithms. The proposed method significantly reduced various kinds of CBCT artifacts in the studies of H&N, pancreatic, and lung cancer patients. In the lung stereotactic body radiation therapy (SBRT) patient study, the MAE, NU, and SSIM were improved from 47 HU, 45 HU, and 0.58 in the original CBCT images to 13 HU, 14dB, and 0.67 in the generated sCT images. Compared to other unsupervised learning-based algorithms, the proposed method demonstrated superior performance in artifact reduction. The proposed unsupervised method can generate sCT from CBCT with reduced artifacts and precise HU values, enabling CBCT-guided segmentation and replanning for online ART.