Quantitative analysis of the impact of respiratory state on the heartbeat-induced movements of the heart and its substructures.

PURPOSE: This study seeks to examine the influence of the heartbeat on the position, volume, and shape of the heart and its substructures during various breathing states. The findings of this study will serve as a valuable reference for dose-volume evaluation of the heart and its substructures in radiotherapy for treating thoracic tumors. METHODS: Twenty-three healthy volunteers were enrolled in this study, and cine four-dimensional magnetic resonance images were acquired during periods of end-inspiration breath holding (EIBH), end-expiration breath holding (EEBH), and deep end-inspiration breath holding (DIBH). The MR images were used to delineate the heart and its substructures, including the heart, pericardium, left ventricle (LV), left ventricular myocardium, right ventricle (RV), right ventricular myocardium (RVM), ventricular septum (VS), atrial septum (AS), proximal and middle portions of the left anterior descending branch (pmLAD), and proximal portion of the left circumflex coronary branch (pLCX). The changes in each structure with heartbeat were compared among different respiratory states. RESULTS: Compared with EIBH, EEBH increased the volume of the heart and its substructures by 0.25-3.66%, while the average Dice similarity coefficient (DSC) increased by - 0.25 to 8.7%; however, the differences were not statistically significant. Conversely, the VS decreased by 0.89 mm in the left-right (LR) direction, and the displacement of the RV in the anterior-posterior (AP) direction significantly decreased by 0.76 mm (p < 0.05). Compared with EIBH and EEBH, the average volume of the heart and its substructures decreased by 3.08-17.57% and 4.09-20.43%, respectively, during DIBH. Accordingly, statistically significant differences (p < 0.05) were observed in the volume of the heart, pericardium, LV, RV, RVM, and AS. The average DSC increased by 0-37.04% and - 2.6 to 32.14%, respectively, with statistically significant differences (p < 0.05) found in the right ventricular myocardium and interatrial septum. Furthermore, the displacements under DIBH decreased in the three directions (i.e.,- 1.73 to 3.47 mm and - 0.36 to 2.51 mm). In this regard, the AP displacement of the heart, LV, RV, RVM, LR direction, LV, RV, and AS showed statistically significant differences (p < 0.05). The Hausdorff distance (HD) of the heart and its substructures under the three breathing states are all greater than 11 mm. CONCLUSION: The variations in the displacement and shape alterations of the heart and its substructures during cardiac motion under various respiratory states are significant. When assessing the dose-volume index of the heart and its substructures during radiotherapy for thoracic tumors, it is essential to account for the combined impacts of cardiac motion and respiration.

Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators.

BACKGROUND: The goal was to investigate the feasibility of the registration generative adversarial network (RegGAN) model in image conversion for performing adaptive radiation therapy on the head and neck and its stability under different cone beam computed tomography (CBCT) models. METHODS: A total of 100 CBCT and CT images of patients diagnosed with head and neck tumors were utilized for the training phase, whereas the testing phase involved 40 distinct patients obtained from four different linear accelerators. The RegGAN model was trained and tested to evaluate its performance. The generated synthetic CT (sCT) image quality was compared to that of planning CT (pCT) images by employing metrics such as the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Moreover, the radiation therapy plan was uniformly applied to both the sCT and pCT images to analyze the planning target volume (PTV) dose statistics and calculate the dose difference rate, reinforcing the model's accuracy. RESULTS: The generated sCT images had good image quality, and no significant differences were observed among the different CBCT modes. The conversion effect achieved for Synergy was the best, and the MAE decreased from 231.3 ± 55.48 to 45.63 ± 10.78; the PSNR increased from 19.40 ± 1.46 to 26.75 ± 1.32; the SSIM increased from 0.82 ± 0.02 to 0.85 ± 0.04. The quality improvement effect achieved for sCT image synthesis based on RegGAN was obvious, and no significant sCT synthesis differences were observed among different accelerators. CONCLUSION: The sCT images generated by the RegGAN model had high image quality, and the RegGAN model exhibited a strong generalization ability across different accelerators, enabling its outputs to be used as reference images for performing adaptive radiation therapy on the head and neck.