Impact of setup errors on the robustness of linac-based single-isocenter coplanar and non-coplanar VMAT plans for multiple brain metastases.

PURPOSE: Patient setup errors have been a primary concern impacting the dose delivery accuracy in radiation therapy. A robust treatment plan might mitigate the effects of patient setup errors. In this reported study, we aimed to evaluate the impact of translational and rotational errors on the robustness of linac-based, single-isocenter, coplanar, and non-coplanar volumetric modulated arc therapy treatment plans for multiple brain metastases. METHODS: Fifteen patients were retrospectively selected for this study with a combined total of 49 gross tumor volumes (GTVs). Single-isocenter coplanar and non-coplanar plans were generated first with a prescribed dose of 40 Gy in 5 fractions or 42 Gy in 7 fractions to cover 95% of planning target volume (PTV). Next, four setup errors (+1  and +2 mm translation, and +1° and +2° rotation) were applied individually to generate modified plans. Different plan quality evaluation metrics were compared between coplanar and non-coplanar plans. 3D gamma analysis (3%/2 mm) was performed to compare the modified plans (+2 mm and +2° only) and the original plans. Paired t-test was conducted for statistical analysis. RESULTS: After applying setup errors, variations of all plan evaluation metrics were similar (p > 0.05). The worst case for V100% to GTV was 92.07% ± 6.13% in the case of +2 mm translational error. 3D gamma pass rates were > 90% for both coplanar (+2 mm and +2°) and the +2 mm non-coplanar groups but was 87.40% ± 6.89% for the +2° non-coplanar group. CONCLUSION: Translational errors have a greater impact on PTV and GTV dose coverage for both planning methods. Rotational errors have a greater negative impact on gamma pass rates of non-coplanar plans. Plan evaluation metrics after applying setup errors showed that both coplanar and non-coplanar plans were robust and clinically acceptable.

Improving radiotherapy safety and efficiency with the customized ARIA oncology information system.

OBJECTIVE: To improve safety and efficiency of radiotherapy process by customizing a Varian ARIA oncology information system following the guidelines provided in AAPM TG-100 report. METHODS: First, failure mode and effects analysis (FMEA) and quality management program were implemented for radiotherapy process. We have customized the visual care path in the ARIA system and set up a series of templates for simulation, prescription, contouring, treatment planning, and multiple checklists. Average time of activities' completion and amount of planning errors were compared before and after the use of the customized ARIA to evaluate its impact on the efficiency and safety of radiotherapy. RESULTS: Completion time and on-time completion rate of the key activities in the care path are improved. The time of OAR/targets contouring decreases from (1.94±1.51) days to (1.64±1.07) days (p = 0.003), with the on-time completion rate increases from 77.4%to 83.3%(p = 0.048). Treatment planning time decreases from (0.81±0.65) days to (0.55±0.51) days (p < 0.001), with the on-time completion rate increases from 96.6%to 98.3%(p = 0.163). Waiting time of patients decreases from (4.50±1.83) days to (4.04±1.34) days (p < 0.001), with the on-time completion rate increases from 81.9%to 89.7%(p = 0.003). In addition, the average plan error rate decreases from 5.5%(2.9%for safety errors and 2.6%for non-normative errors) to 2.4%(1.6%for safety errors and 0.8%for non-normative errors) (p = 0.029). CONCLUSION: Our study demonstrates that the customized ARIA system has the potential to promote efficiency and safety in radiotherapy process management. It is beneficial to organize and accelerate the treatment process with more effective communications and fewer errors.

Stroboscopic Surface Thermal Lensing for Fast Detection of Thermal Defects in Large-Scaled Coating Films.

A stroboscopic surface thermal lensing (SSTL) system for the fast detection of thermal-induced defects in large-scaled optical coating films was constructed. The SSTL signal was generated by a set of double-modulators and captured by a high speed matrix camera, respectively. The spot size of both pump laser and probe laser expanded for larger detection area was finished in a single step. Based on the STL technique, both the mapping of amplitude and the phase of SSTL signal on the whole area of the coatings can be achieved simultaneously.

High-transmittance subwavelength metal grating with relief structure composed of multiple steps.

A new kind of subwavelength metal grating with relief structure is designed and analyzed, in which the shape of the grating lines is no longer a single rectangle, but a relief structure with multiple steps. GsolverV52 was used to determine the optimal values of the grating period, groove depth, and the number of steps. The optical performance of the novel structure is evaluated and compared in terms of the transmission efficiency and extinction ratio over the visible and near-infrared wavelength spectrum. It is shown that, in the near-infrared band, the maximum transmittance can be increased about 15% compared to the traditional metal grating under the same parameters. With the unique characteristics, the metal grating is expected to find applications in liquid crystal display fields, polarization imaging, optical communication, and so on.

Pseudo-medical image-guided technology based on 'CBCT-only' mode in esophageal cancer radiotherapy.

Purpose To minimize the various errors introduced by image-guided radiotherapy (IGRT) in the application of esophageal cancer treatment, this study proposes a novel technique based on the 'CBCT-only' mode of pseudo-medical image guidance. Methods The framework of this technology consists of two pseudo-medical image synthesis models in the CBCT→CT and the CT→PET direction. The former utilizes a dual-domain parallel deep learning model called AWM-PNet, which incorporates attention waning mechanisms. This model effectively suppresses artifacts in CBCT images in both the sinogram and spatial domains while efficiently capturing important image features and contextual information. The latter leverages tumor location and shape information provided by clinical experts. It introduces a PRAM-GAN model based on a prior region aware mechanism to establish a non-linear mapping relationship between CT and PET image domains.  As a result, it enables the generation of pseudo-PET images that meet the clinical requirements for radiotherapy. Results The NRMSE and multi-scale SSIM (MS-SSIM) were utilized to evaluate the test set, and the results were presented as median values with lower quartile and upper quartile ranges. For the AWM-PNet model, the NRMSE and MS-SSIM values were 0.0218 (0.0143, 0.0255) and 0.9325 (0.9141, 0.9410), respectively. The PRAM-GAN model produced NRMSE and MS-SSIM values of 0.0404 (0.0356, 0.0476) and 0.9154 (0.8971, 0.9294), respectively. Statistical analysis revealed significant differences (p < 0.05) between these models and others. The numerical results of dose metrics, including D98 %, Dmean, and D2 %, validated the accuracy of HU values in the pseudo-CT images synthesized by the AWM-PNet. Furthermore, the Dice coefficient results confirmed statistically significant differences (p < 0.05) in GTV delineation between the pseudo-PET images synthesized using the PRAM-GAN model and other compared methods. Conclusion The AWM-PNet and PRAM-GAN models have the capability to generate accurate pseudo-CT and pseudo-PET images, respectively. The pseudo-image-guided technique based on the 'CBCT-only' mode shows promising prospects for application in esophageal cancer radiotherapy.

Deep learning architecture with shunted transformer and 3D deformable convolution for voxel-level dose prediction of head and neck tumors.

Intensity-modulated radiation therapy (IMRT) has been widely used in treating head and neck tumors. However, due to the complex anatomical structures in the head and neck region, it is challenging for the plan optimizer to rapidly generate clinically acceptable IMRT treatment plans. A novel deep learning multi-scale Transformer (MST) model was developed in the current study aiming to accelerate the IMRT planning for head and neck tumors while generating more precise prediction of the voxel-level dose distribution. The proposed end-to-end MST model employs the shunted Transformer to capture multi-scale features and learn a global dependency, and utilizes 3D deformable convolution bottleneck blocks to extract shape-aware feature and compensate the loss of spatial information in the patch merging layers. Moreover, data augmentation and self-knowledge distillation are used to further improve the prediction performance of the model. The MST model was trained and evaluated on the OpenKBP Challenge dataset. Its prediction accuracy was compared with three previous dose prediction models: C3D, TrDosePred, and TSNet. The predicted dose distributions of our proposed MST model in the tumor region are closest to the original clinical dose distribution. The MST model achieves the dose score of 2.23 Gy and the DVH score of 1.34 Gy on the test dataset, outperforming the other three models by 8%-17%. For clinical-related DVH dosimetric metrics, the prediction accuracy in terms of mean absolute error (MAE) is 2.04% for D 99 , 1.54% for D 95 , 1.87% for D 1 , 1.87% for D mean , 1.89% for D 0.1 c c , respectively, superior to the other three models. The quantitative results demonstrated that the proposed MST model achieved more accurate voxel-level dose prediction than the previous models for head and neck tumors. The MST model has a great potential to be applied to other disease sites to further improve the quality and efficiency of radiotherapy planning.

Pseudo-CT synthesis in adaptive radiotherapy based on a stacked coarse-to-fine model: Combing diffusion process and spatial-frequency convolutions.

BACKGROUND: Cone beam computed tomography (CBCT) provides critical anatomical information for adaptive radiotherapy (ART), especially for tumors in the pelvic region that undergo significant deformation. However, CBCT suffers from inaccurate Hounsfield Unit (HU) values and lower soft tissue contrast. These issues affect the accuracy of pelvic treatment plans and implementation of the treatment, hence requiring correction. PURPOSE: A novel stacked coarse-to-fine model combining Denoising Diffusion Probabilistic Model (DDPM) and spatial-frequency domain convolution modules is proposed to enhance the imaging quality of CBCT images. METHODS: The enhancement of low-quality CBCT images is divided into two stages. In the coarse stage, the improved DDPM with U-ConvNeXt architecture is used to complete the denoising task of CBCT images. In the fine stage, the deep convolutional network model jointly constructed by fast Fourier and dilated convolution modules is used to further enhance the image quality in local details and global imaging. Finally, the accurate pseudo-CT (pCT) images consistent with the size of the original data are obtained. Two hundred fifty paired CBCT-CT images from cervical and rectal cancer, combined with 200 public dataset cases, were used collectively for training, validation, and testing. RESULTS: To evaluate the anatomical consistency between pCT and real CT, we have used the mean(std) of structure similarity index measure (SSIM), peak signal to noise ratio (PSNR), and normalized cross-correlation (NCC). The numerical results for the above three metrics comparing the pCT synthesized by the proposed model against real CT for cervical cancer cases were 87.14% (2.91%), 34.02 dB (1.35 dB), and 88.01% (1.82%), respectively. For rectal cancer cases, the corresponding results were 86.06% (2.70%), 33.50 dB (1.41 dB), and 87.44% (1.95%). The paired t-test analysis between the proposed model and the comparative models (ResUnet, CycleGAN, DDPM, and DDIM) for these metrics revealed statistically significant differences (p < 0.05). The visual results also showed that the anatomical structures between the real CT and the pCT synthesized by the proposed model were closer. For the dosimetric verification, mean absolute error of dosimetry (MAEdoes) values for the maximum dose (Dmax), the minimum dose (Dmin), and the mean dose (Dmean) in the planning target volume (PTV) were analyzed, with results presented as mean (lower quartile, upper quartile). The experimental results show that the values of the above three dosimetry indexes (Dmin, Dmax, and Dmean) for the pCT images synthesized by the proposed model were 0.90% (0.48%, 1.29%), 0.82% (0.47%, 1.17%), and 0.57% (0.44%, 0.67%). Compared with 10 cases of the original CBCT image by Mann-Whitney test (p < 0.05), it also proved that pCT can significantly improve the accuracy of HU values for the dose calculation. CONCLUSION: The pCT synthesized by the proposed model outperforms the comparative models in numerical accuracy and visualization, promising for ART of pelvic cancers.

Characterization and commissioning of a new collaborative multi-modality radiotherapy platform.

TaiChi, a new multi-modality radiotherapy platform that integrates a linear accelerator, a focusing gamma system, and a kV imaging system within an enclosed O-ring gantry, was introduced into clinical application. This work aims to assess the technological characteristics and commissioning results of the TaiChi platform. The acceptance testing and commissioning were performed following the manufacturer's customer acceptance tests (CAT) and several AAPM Task Group (TG) reports/guidelines. Regarding the linear accelerator (linac), all applicable validation measurements recommended by the MPPG 5.a (basic photon beam model validation, intensity-modulated radiotherapy (IMRT)/volumetric-modulated arc therapy (VMAT) validation, end-to-end(E2E) tests, and patient-specific quality assurance (QA)) were performed. For the focusing gamma system, the absorbed doses were measured using a PTW31014 ion chamber (IC) and PTW60016 diode detector. EBT3 films and a PTW60016 diode detector were employed to measure the relative output factors (ROFs). The E2E tests were performed using PTW31014 IC and EBT3 films. The coincidences between the imaging isocenter and the linac/gamma mechanical isocenter were investigated using EBT3 films. The image quality was evaluated regarding the contrast-to-noise ratio (CNR), spatial resolution, and uniformity. All tests included in the CAT met the manufacturer's specifications. All MPPG 5.a measurements complied with the tolerances. The confidence limits for IMRT/VMAT point dose and dose distribution measurements were achieved according to TG-119. The point dose differences were below 1.68% and gamma passing rates (3%/2 mm) were above 95.1% for the linac E2E tests. All plans of patient-specific QA had point dose differences below 1.79% and gamma passing rates above 96.1% using the 3%/2 mm criterion suggested by TG-218. For the focusing gamma system, the differences between the calculated and measured absorbed doses were below 1.86%. The ROFs calculated by the TPS were independently confirmed within 2% using EBT3 films and a PTW60016 detector. The point dose differences were below 2.57% and gamma passing rates were above 95.3% using the 2%/1 mm criterion for the E2E tests. The coincidences between the imaging isocenter and the linac/gamma mechanical isocenter were within 0.5 mm. The image quality parameters fully complied with the manufacturer's specifications regarding the CNR, spatial resolution, and uniformity. The multi-modality radiotherapy platform complies with the CAT and AAPM commissioning criteria. The commissioning results demonstrate that this platform performs well in mechanical and dosimetry accuracy.