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Background: The multi‐leaf collimator (MLC) is an advanced device utilized for beam shaping and intensity modulation in radiotherapy. With the framework of the contemporary single‐layer MLC featuring a rounded leaf tip, the leaf tip transmission and leakage exert a considerable influence on radiotherapy. Purpose: To scale down the leakage and transmission from the leaf gap when the opposite leaves are closed. Methods: This study proposes a new design of MLC, named dynamic leaf machine (DLM), specifically engineered to diminish the transmission and leakage from the MLC leaf tip. The DLM incorporates an innovative leaf configuration that involves a combination of fixed and movable layers in a single MLC leaf named fixed and movable (FM) layers leaf, which is advantageous in dealing with transmission and leakage from the leaf tip by employing a staggered arrangement of the fixed and movable layer when the opposite leaves are closed. Subsequently, the DLM is assembled on the Elekta VERSA HD accelerator to assess its performance, focusing on aspects of mechanical characters, transmission, leakage, and penumbra. Results: In comparison to conventional MLC leaves, the FM leaf design in the DLM has achieved a remarkable reduction in the leakage between opposed closed leaves, decreasing it from 48.41% to 0.41%. Additionally, the transmission between the adjacent leaves has been measured at 0.59%, and the penumbra in 16 mm × 32 mm field is 2.82 mm, aligning with the performance of conventional MLC. Conclusions: The DLM with FM leaf performs a significant reduction in transmission from the opposite leaf tip in comparison to conventional MLC while maintaining minimal penumbra, effectively mitigating the transmission and leakage problem between opposing leaves, thereby enhancing the effect of radiotherapy. [ABSTRACT FROM AUTHOR]

Background: In pursuit of precise dose calculation and verification, the importance of beam modelling cannot be overstated, as it ensures an accurate distribution of particles incident upon the human body. The virtual source model, as one of the beam modelling methods, offers the advantage of not requiring detailed accelerator information. Although various virtual source models exist, manual adjustment to these models demands a substantial investment of time and computational resources. There has long been a desire to develop an efficient and automated approach for model commissioning. Purpose: To develop an automatic commissioning method for the virtual source model to customize the accelerator model for independent Monte Carlo dose verification. Methods: Initially, the accelerator model is established using the virtual source model and self‐developed Jaw and MLC models. Then, a fully automated iteration process is employed to adjust the parameters of the virtual source model. Three types of objective functions are designed to represent differences from water tank measurements. Each objective function is paired with a specific parameter for adjustment, and their effectiveness is demonstrated through physical evidence. In each iteration, parameters with the highest objective function percentage are chosen for adjustment, and step length is determined based on current objective function values. Iteration is terminated when changes in any direction from the optimal solution no longer produce an improvement. Dose verification model for nine accelerators has been accomplished using this method. Additionally, under the same initial conditions, verification models for Versa HD accelerator (FF and FFF modes) are established using this method, Nelder‐Mead Simplex optimization method, and the Bayesian optimization method to compare the efficiency and quality of these three iterative approaches. Results: Iterations for all nine accelerators are completed within 30 iterations. The relative dose differences in dose fall‐off region compared to water tank measurements are all less than 2%, and the average gamma passing rates (3%/2 mm) for ArcCHECK measurements in QA plans are all higher than 97%. For Versa HD accelerator in FFF and FF modes, the proposed method achieves an average relative dose difference below 1% within 11 and 13 iterations, respectively. In contrast, the Simplex optimization reached 1% within 78 iterations in FFF mode. Furthermore, the Simplex optimization in FF mode and Bayesian optimization in both modes failed to achieve a 1% difference within 100 iterations. Conclusions: The proposed iterative method achieves fast and automated commissioning of dose verification models, contributing to accurate and reliable clinical dose verification. [ABSTRACT FROM AUTHOR]

Background: Patients may undergo anatomical changes during radiotherapy, leading to an underdosing of the target or overdosing of the organs at risk (OARs). Purpose: This study developed a deep‐learning method to predict the tumor response of patients with nasopharyngeal carcinoma (NPC) during treatment. This method can predict the anatomical changes of a patient. Methods: The participants included 230 patients with NPC. The data included planning computed tomography (pCT) and routine cone‐beam CT (CBCT) images. The CBCT image quality was improved to the CT level using an advanced method. A long short‐term memory network‐generative adversarial network (LSTM‐GAN) is proposed, which can harness the forecasting ability of LSTM and the generation ability of GAN. Four models were trained to predict the anatomical changes that occurred in weeks 3–6 and named LSTM‐GAN‐week 3 to LSTM‐GAN‐week 6. The pCT and CBCT were used as input, and the tumor target volumes (TVs) and OARs were delineated on the predicted and real images (ground truth). Finally, the models were evaluated using contours and dosimetry parameters. Results: The proposed method predicted the anatomical changes, with a dice similarity coefficient above 0.94 and 0.90 for the TVs and surrounding OARs, respectively. The dosimetry parameters were close between the prediction and ground truth. The deviations in the prescription, minimum, and maximum doses of the tumor targets were below 0.5 Gy. For serial organs (brain stem and spinal cord), the deviations in the maximum dose were below 0.6 Gy. For parallel organs (bilateral parotid glands), the deviations in the mean dose were below 0.8 Gy. Conclusion: The proposed method can predict the tumor response to radiotherapy in the future such that adaptation can be scheduled on time. This study provides a proactive mechanism for planning adaptation, which can enable personalized treatment and save clinical time by anticipating and preparing for treatment strategy adjustments. [ABSTRACT FROM AUTHOR]

Background: In prostate radiotherapy, the intrafractional target motion negatively affects treatment accuracy. Generating internal target volume (ITV) using four‐dimensional (4D) images may resolve the issue of intrafractional target motion induced by bladder filling and bowel movement. However, no 4D imaging techniques suitable for the prostate are currently available in clinical practice. Purpose: This study aimed to determine the ITV based on cine magnetic resonance imaging (MRI) sequence for intrafractional target motion management in prostate MRI‐guided radiotherapy. Materials and Methods: A reference ITV was generated in simulation process. Then, the reference ITV was adapted with cine MRI sequence before online planning in each fraction. Finally, the reference ITV was updated with the cine MRI sequence acquired during beam delivery after each fraction. Cine MRI sequences and positioning three‐dimensional (3D) MRI from 35 patients were retrospectively collected. Clinical target volume (CTV) coverage was computed according to the two‐dimensional contour of CTV and ITV on cine MRI images. Relative target size was calculated as the ratio of the volume of ITV and CTV. Isotropic planning target volume (PTV; 5 mm margin) and anisotropic PTV (3 mm margin in the posterior direction and 5 mm margin in other directions) were generated for comparison. Results: The CTV coverage rate of the proposed ITV had a mean value of 98.61% ± 0.51%, whereas the CTV coverage rates of the isotropic and anisotropic PTVs were 97.43% ± 0.41% and 96.58% ± 0.73%, respectively. The proposed ITV had a relative target size of 1.79 ± 0.17, whereas the anisotropic and isotropic PTVs had relative target sizes of 1.92 ± 0.12 and 2.21 ± 0.19, respectively. For both the CTV coverage rate and target relative size, significant differences were observed between the proposed ITV and the other two PTVs (p < 0.05). Conclusion: The ITV achieved higher CTV coverage with smaller size than conventional isotropic and anisotropic PTVs, indicating that it can effectively deal with the intrafractional movement of the prostate. [ABSTRACT FROM AUTHOR]

Background: Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel‐wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention. Purpose: This study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel‐wise consistency for deep learning‐based MRI‐to‐CT synthesis. The method was expected to assist the multi‐modality studies for radiomics. Methods: The study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre‐procession. We proposed a gray‐level co‐occurrence matrix (GLCM)‐based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning‐based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function. Results: Compared with the baseline, the proposed method improved the pixel‐wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78 ± 0.12 to 0.82 ± 0.11 (p < 0.05 for paired t‐test). Generally, > 90% (22/24) of the GLCM‐based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs. Conclusions: The proposed method reproduced texture features for MRI‐to‐CT synthesis, which would benefit radiomics studies based on image multi‐modality synthesis. [ABSTRACT FROM AUTHOR]

Background: It is necessary to contour regions of interest (ROIs) for online magnetic resonance imaging (MRI)‐guided adaptive radiotherapy (MRIgART). These updated contours are used for online replanning to obtain maximum dosimetric benefits. Contouring can be accomplished using deformable image registration (DIR) and deep learning (DL)‐based autosegmentation methods. However, these methods may require considerable manual editing and thus prolong treatment time. Purpose: The present study aimed to improve autosegmentation performance by integrating patients' pretreatment information in a DL‐based segmentation algorithm. It is expected to improve the efficiency of current MRIgART process. Methods: Forty patients with prostate cancer were enrolled retrospectively. The online adaptive MR images, patient‐specific planning computed tomography (CT), and contours in CT were used for segmentation. The deformable registration of planning CT and MR images was performed first to obtain a deformable CT and corresponding contours. A novel DL network, which can integrate such patient‐specific information (deformable CT and corresponding contours) into the segmentation task of MR images was designed. We performed a four‐fold cross‐validation for the DL models. The proposed method was compared with DIR and DL methods on segmentation of prostate cancer. The ROIs included the clinical target volume (CTV), bladder, rectum, left femur head, and right femur head. Dosimetric parameters of automatically generated ROIs were evaluated using a clinical treatment planning system. Results: The proposed method enhanced the segmentation accuracy of conventional procedures. Its mean value of the dice similarity coefficient (93.5%) over the five ROIs was higher than both DIR (87.5%) and DL (87.2%). The number of patients (n = 40) that required major editing using DIR, DL, and our method were 12, 18, and 7 (CTV); 17, 4, and 1 (bladder); 8, 11, and 5 (rectum); 2, 4, and 1 (left femur head); and 3, 7, and 1 (right femur head), respectively. The Spearman rank correlation coefficient of dosimetry parameters between the proposed method and ground truth was 0.972 ± 0.040, higher than that of DIR (0.897 ± 0.098) and DL (0.871 ± 0.134). Conclusion: This study proposed a novel method that integrates patient‐specific pretreatment information into DL‐based segmentation algorithm. It outperformed baseline methods, thereby improving the efficiency and segmentation accuracy in adaptive radiotherapy. [ABSTRACT FROM AUTHOR]

Background: The application of cone‐beam computed tomography (CBCT) in image‐guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality. Purpose: In this study, we aim to develop a deep learning framework to generate high‐quality CBCT images for therapeutic applications. Methods: The synthetic CT (sCT) generation from the CBCT was proposed using a transformer‐based network with a hybrid loss function. The network was trained and validated using the data from 176 patients to produce a general model that can be extensively applied to enhance CBCT images. After the first therapy, each patient can receive paired CBCT/planning CT (pCT) scans, and the obtained data were used to fine‐tune the general model for further improvement. For subsequent treatment, a patient‐specific, personalized model was made available. In total, 34 patients were examined for general model testing, and another six patients who underwent rescanned pCT scan were used for personalized model training and testing. Results: The general model decreased the mean absolute error (MAE) from 135 HU to 59 HU as compared to the CBCT. The hybrid loss function demonstrated superior performance in CT number correction and noise/artifacts reduction. The proposed transformer‐based network also showed superior power in CT number correction compared to the classical convolutional neural network. The personalized model showed improvement based on the general model in some details, and the MAE was reduced from 59 HU (for the general model) to 57 HU (p < 0.05 Wilcoxon signed‐rank test). Conclusion: We established a deep learning framework based on transformer for clinical needs. The deep learning model demonstrated potential for continuous improvement with the help of a suggested personalized training strategy compatible with the clinical workflow. [ABSTRACT FROM AUTHOR]

Purpose: Respiration has a major impact on the accuracy of radiation treatment for thorax and abdominal tumours. Instantaneous volumetric imaging could provide precise knowledge of tumour and normal organs' three‐dimensional (3D) movement, which is the key to reducing the negative effect of breathing motion. Therefore, this study proposed a real‐time 3D MRI reconstruction method from cine‐MRI using an unsupervised network. Methods and materials: Cine‐MRI and setup 3D‐MRI from eight patients with liver cancer were utilized to establish and validate the deep learning network for 3D‐MRI reconstruction. Unlike previous methods that required 4D‐MRI for network training, the proposed method utilized a reference 3D‐MRI and cine‐MRI to generate the training data. Then, a network was trained in an unsupervised manner to estimate the relationship between the cine‐MRI acquired on coronal plane and deformation vector field (DVF) that describes the patient's breathing motion. After the training process, the coronal cine‐MRI were inputted into the network, and the corresponding DVF was obtained. By wrapping the reference 3D‐MRI with the generated DVF, the 3D‐MRI could be reconstructed. Results: The reconstructed 3D‐MRI slices were compared with the corresponding phase‐sorted cine‐MRI using dice similarity coefficients (DSCs) of liver contours and blood vessel localization error. In all patients, the liver DSC had mean value >96.1% and standard deviation < 1.3%; the blood vessel localization error had mean value <2.6 mm, and standard deviation was <2.0 mm. Moreover, the time for 3D‐MRI reconstruction was approximately 100 ms. These results indicated that the proposed method could accurately reconstruct the 3D‐MRI in real time. Conclusions: The proposed method could accurately reconstruct the 3D‐MRI from cine‐MRI in real time. This method has great potential in improving the accuracy of radiotherapy for moving tumours. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to evaluate the performance of a multileaf collimator (MLC) system for the high magnetic field of the Elekta Unity magnetic resonance linear accelerator (MR‐linac). Methods: Performance was evaluated as recommended in AAPM Report Nos. 72 and 142, and MLC motor radiofrequency (RF) shielding was also evaluated. Fields of 99.8 × 99.8 mm2 and 199.6 × 199.6 mm2 were examined to calculate the leaf width. Profiles of 28.4 × 28.4 mm2, 57.0 × 57.0 mm2, 99.8 × 99.8 mm2, and 199.6 × 199.6 mm2 fields at center and off‐axis positions were measured using a microDiamond detector and BEAMSCAN® MR water phantom to determine the field penumbra. EBT3 film was exposed to a 190.0 × 220.0 mm2 field composed of two strip segments to assess the tongue‐and‐groove (T&G) effect. Outputs of 100.0 × 100.0 mm2 open and blocked fields at center and off‐axis positions were measured using a PTW TW31021 ion chamber and a film with the full MLC‐blocked field to determine transmission. The minimum gap fields were also exposed on EBT3 film at center and off‐axis positions to assess leakage between leaf tips in the "closed" position. The picket fence test was performed using a megavoltage imager to analyze leaf positional reproducibility. RF shielding was measured via real‐time MR imaging of a homogeneous phantom and quantified using the signal‐to‐noise ratio (SNR). Results: The leaf widths were 7.00 ± 0.00 and 7.10 ± 0.00 mm for 99.8 × 99.8 and 199.6 × 199.6 mm2 fields, respectively. The leaf side penumbra ranged from 5.11 ± 0.08 to 11.28 ± 0.29 mm and the leaf tips penumbra ranged from 4.47 ± 0.03 to 6.25 ± 0.15 mm for the field size range of 28.4 × 28.4 mm2 to 199.6 × 199.6 mm2. The T&G effect resulted in an underdose of up to 23.41 ± 0.88%. The max leaf transmission was 0.33 ± 0.00% when MLCs were all closed and diaphragms were all opened, and this value was reduced to 0.18 ± 0.00% when the diaphragm blocked the field with a minimum MLC gap. The leaf tip leakage rates were 60.08 ± 0.05% at field center and 2.94 ± 0.00% approximately 100 mm off‐axis. At 6 months, the short‐term leaf position reproducibility was 0.11 mm (2σ), and the long‐term leaf position reproducibility was 0.19 mm (2σ). Leaf motion led to small fluctuations of the SNR of up to 1.64%. Conclusion: The new MLC system has a comparable T&G effect, low radiation leakage, high positioning reproducibility, and good RF shielding for the Elekta Unity MR‐linac. However, more attention is required for the positional change of the leaf side penumbra. [ABSTRACT FROM AUTHOR]

Purpose: To develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity‐modulated radiation therapy treatment plans. Methods: Eighty cases of early‐stage nasopharyngeal cancer (NPC) were included in the study. Seventy cases were chosen randomly as the training set and the remaining as the test set. The inputs were the images with structures, with each target and organs at risk (OARs) assigned a unique label. The outputs were dose maps, including coarse dose maps and converted fine dose maps (FDM) from convolution. Two types of input images with structures were used in the model building. One type of input included the images (with associated structures) without manipulation. The second type of input involved modifying the image gray label with information from radiation beam geometry. ResNet101 was chosen as the deep learning network for both. The accuracy of predicted dose distributions was evaluated against the corresponding dose as used in the clinic. A global three‐dimensional gamma analysis was calculated for the evaluation. Results: The proposed model trained with the two different sets of input images and structures could both predict patient‐specific dose distributions accurately. For the out‐of‐field dose distributions, the model obtained from the input with radiation geometry performed better (dose difference in %, 4.7 ± 6.1% vs 5.5 ± 7.9%, P < 0.05). The mean Gamma pass rates of dose distributions predicted with both types of input were comparable for most OARs (P > 0.05), except for the bilateral optic nerves and the optic chiasm. Conclusions: The proposed system with radiation geometry added to the input is a promising method to generate patient‐specific dose distributions for radiotherapy. It can be applied to obtain the dose distributions slice‐by‐slice for planning quality assurance and for guiding automated planning. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real‐time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image‐guided adaptive RT systems, the use of deep learning has been considered.This study proposed a modified generative adversarial network (GAN) for predicting cine‐MR images in real time.Sagittal cine magnetic resonance (cine‐MR) images of 15 patients with liver cancer who received RT were collected. The image series length of each patient was 300, and each series was divided into training, validation, and test sets. The datasets were further divided using a sliding window size of 10 and a stride of 1. A pix2pix GAN with the generator replaced by convolutional long short‐term memory (ConvLSTM) was proposed herein. A five‐frame cine‐MR image series was inputted into the network, which predicted the next five frames. The proposed network was compared with three advanced networks: ConvLSTM, Eidetic 3D LSTM (E3D–LSTM), and SwinLSTM. Personalized models were trained for each patient. The peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), visual information fidelity (VIF), Pearson correlation coefficient (Pearson corr), and respiratory motion accuracy of the predicted images were used to evaluate the methods.The proposed network demonstrated optimal performance in the four networks across various indicators. The proposed method provided better SSIM values than ConvLSTM at time steps 1, 2, 3, and 4, and outperformed E3DLSTM at all time steps. In terms of the VIF, the proposed method outperformed E3D–LSTM at all time steps and SwinLSTM at time steps 2, 3, 4, and 5. The proposed method was not significantly different from other methods in terms of Pearson correlation values except that it outperformed E3DLSTM at time step 1. In terms of the Pearson corr, the proposed method consistently achieves better values, especially in the high‐frequency components. Low average landmark tracking errors were provided by the proposed method at time steps 4 and 5 (2.42 ± 0.91 and 2.44 ± 0.96 mm, respectively).The GAN–ConvLSTM network can generate high‐acutance real‐time cine‐MR images and predict respiratory motion with better accuracy. [ABSTRACT FROM AUTHOR]

Background: Recently, patient rotating devices for gantry-free radiotherapy, a new approach to implement external beam radiotherapy, have been introduced. When a patient is rotated in the horizontal position, gravity causes anatomic deformation. For treatment planning, one feasible method is to acquire simulation images at different horizontal rotation angles., Purpose: This study aimed to investigate the feasibility of synthesizing magnetic resonance (MR) images at patient rotation angles of 180° (prone position) and 90° (lateral position) from those at a rotation angle of 0° (supine position) using deep learning., Methods: This study included 23 healthy male volunteers. They underwent MR imaging (MRI) in the supine position and then in the prone (23 volunteers) and lateral (16 volunteers) positions. T1-weighted fast spin echo was performed for all positions with the same parameters. Two two-dimensional deep learning networks, pix2pix generative adversarial network (pix2pix GAN) and CycleGAN, were developed for synthesizing MR images in the prone and lateral positions from those in the supine position, respectively. For the evaluation of the models, leave-one-out cross-validation was performed. The mean absolute error (MAE), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were used to determine the agreement between the prediction and ground truth for the entire body and four specific organs., Results: For pix2pix GAN, the synthesized images were visually bad, and no quantitative evaluation was performed. The quantitative evaluation metrics of the body outlines calculated for the synthesized prone and lateral images using CycleGAN were as follows: MAE, 35.63 ± 3.98 and 40.45 ± 5.83, respectively; DSC, 0.97 ± 0.01 and 0.94 ± 0.01, respectively; and HD (in pixels), 16.74 ± 3.55 and 31.69 ± 12.03, respectively. The quantitative metrics of the bladder and prostate performed were also promising for both the prone and lateral images, with mean values >0.90 in DSC (p > 0.05). The mean DSC and HD values of the bilateral femur for the prone images were 0.96 and 3.63 (in pixels), respectively, and 0.78 and 12.65 (in pixels) for the lateral images, respectively (p < 0.05)., Conclusions: The CycleGAN could synthesize the MRI at lateral and prone positions using images at supine position, and it could benefit gantry-free radiation therapy., (© 2022 American Association of Physicists in Medicine.)