Linghong Zhou

Radiotherapy (RT) for treating breast cancer can result in incidental dose deposition to the liver, leading to functional impairment. FLASH radiotherapy (F

To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk. A mixed fluence map optimization objective was constructed, incorporating the outcome-based objective and the physical dose constraints. The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure, radiation-induced cardiac toxicity, and radiation pneumonitis considering clinical risk factors. These models were derived using Cox regression analysis or Logistic regression. The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines. The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method (clinical plan), and the dosimetric indicators and predicted prognostic outcomes were compared between different plans. In terms of the dosemetric indicators, D_95% of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% vs 102.57%, P=0.056), and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy (t=4.537, P<0.05) and 8.40 Gy (t=4.104, P<0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% vs 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan. The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.

In knowledge-based treatment planning (KBTP) for intensity-modulated radiation therapy (IMRT), the quality of the plan is dependent on the sophistication of the predicted dosimetric information and its application. In this paper, we propose a KBTP method that based on the effective and reasonable utilization of a three-dimensional (3D) dose prediction on planning optimization. We used an organs-at-risk (OARs) dose distribution prediction model to create a voxel-based dose sequence based optimization objective for OARs doses. This objective was used to reformulate a traditional fluence map optimization model, which involves a tolerable spatial re-assignment of the predicted dose distribution to the OAR voxels based on their current doses' positions at a sorted dose sequencing. The feasibility of this method was evaluated with ten gynecology (GYN) cancer IMRT cases by comparing its generated plan quality with the original clinical plan. Results showed feasible plan by proposed method, with comparable planning target volume (PTV) dose coverage and greater dose sparing of the OARs. Among ten GYN cases, the average V₃₀ and V₄₅ of rectum were decreased by 4%±4% (p = 0.02) and 4%±3% (p30 and V₄₅ of bladder were decreased by 8%±2% (p<0.01) and 3%±2% (p<0.01), respectively. Our predicted dose sequence-based planning optimization method for GYN IMRT offered a flexible use of predicted 3D doses while ensuring the output plan consistency.

Original Article A transmission measurement-based spectrum estimation method incorporating X-ray tube voltage fluctuation Zengxiang Pan, Xu Li, Guoqin Lin, Peishan Qin, Zun Piao, Shuang Huang, Wangjiang Wu, Mengke Qi, Linghong Zhou, Yuan Xu

Purpose: To propose a novel magnetic field dose calculation method based on transformation from pencil beam (PB) to Monte Carlo (MC) distribution for MRI‐Linac online treatment planning.Methods: The novel magnetic field dose calculation algorithm was established by a PB dose engine and a magnetic field with tissue inhomogeneity influence correction network. The correction network was constructed with a Res‐UNet framework, including residual modules and an encoding–decoding path, by inputting three‐dimensional PB dose and patient electron density map, and outputting transformed dose distribution. The influences of magnetic fields and tissue heterogeneity were considered and corrected simultaneously in the correction model. A total of 110 clinically treated static beam IMRT plans were collected, including plans for brain, head‐and‐neck, lung, and rectum cases. A total of 90 cases were used and enhanced to train and validate the model, and the other 20 cases were for test. By comparing the proposed pipeline‐generated dose distribution with original input PB dose and corresponding MC dose, the feasibility and effectiveness of the method was evaluated.Results: Results on both beam dose and plan dose accuracy comparisons on all investigated four tumor sites show great consistency between the cross‐dose‐engine transformation generations and the MC results, with averaged plan mean absolute error of 0.90% ± 0.13% for the voxel‐wise dose difference and 98.33% ± 1.07% gamma passing rate at the 2%/2 mm criteria. The whole PB calculation and transformation process can be completed within second.Conclusions: We have successfully developed a fast novel magnetic field dose calculation pipeline based on transformation from PB distribution to MC distribution for MRI‐Linac online treatment planning.

Objective. To develop a novel deep learning-based 3D in vivo dose reconstruction framework with an electronic portal imaging device (EPID) for magnetic resonance-linear accelerators (MR-LINACs). Approach. The proposed method directly back-projected 2D portal dose into 3D patient coarse dose, which bypassed the complicated patient-to-EPID scatter estimation step used in conventional methods. A pre-trained convolutional neural network (CNN) was then employed to map the coarse dose to the final accurate dose. The electron return effect caused by the magnetic field was captured with the CNN model. Patient dose and portal dose datasets were synchronously generated with Monte Carlo simulation for 96 patients (78 cases for training and validation and 18 cases for testing) treated with fixed-beam intensity-modulated radiotherapy in four different tumor sites, including the brain, nasopharynx, lung, and rectum. Beam angles from the training dataset were further rotated 2–3 times, and doses were recalculated to augment the datasets. Results. The comparison between reconstructed doses and MC ground truth doses showed mean absolute errors γ -passing rates (3%, 2 mm) were 97.42%±2.66% (brain), 98.53%±0.95% (nasopharynx), 99.41%±0.46% (lung), and 98.63%±1.01% (rectum). The dose volume histograms and indices also showed good consistency. The average dose reconstruction time, including back projection and CNN dose mapping, was less than 3 s for each individual beam. Significance. The proposed method can be potentially used for accurate and fast 3D dosimetric verification for online adaptive radiotherapy using MR-LINACs.

A prior image constraint robust principal component analysis reconstruction method for sparse segmental multi-energy computed tomography Bin Li, Ning Luo, Anni Zhong, Yongbao Li, Along Chen, Linghong Zhou, Yuan Xu

Purpose: 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.Methods: 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.Results: 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.Conclusions: 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.