Validation of a CT-based motion model with in-situ fluoroscopy for lung surface deformation estimation

CT respiratory motion synthesis using joint supervised and adversarial learning

Objective.Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery.Approach.In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude.Main results.Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim).Significance.This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found athttps://github.com/cyiheng/Dynagan.

Image preprocessing to improve the accuracy and robustness of mutual-information-based automatic image registration in proton therapy

PURPOSE

We propose a method that potentially improves the outcome of mutual-information-based automatic image registration by using the contrast enhancement filter (CEF).

METHODS

Seventy-six pairs of two-dimensional X-ray images and digitally reconstructed radiographs for 20 head and neck and nine lung cancer patients were analyzed retrospectively. Automatic image registration was performed using the mutual-information-based algorithm in VeriSuite®. Images were preprocessed using the CEF in VeriSuite®. The correction vector for translation and rotation error was calculated and manual image registration was compared with automatic image registration, with and without CEF. In addition, the normalized mutual information (NMI) distribution between two-dimensional images was compared, with and without CEF.

RESULTS

In the correction vector comparison between manual and automatic image registration, the average differences in translation error were < 1 mm in most cases in the head and neck region. The average differences in rotation error were 0.71 and 0.16 degrees without and with CEF, respectively, in the head and neck region; they were 2.67 and 1.64 degrees, respectively, in the chest region. When used with oblique projection, the average rotation error was 0.39 degrees with CEF. CEF improved the NMI by 17.9 % in head and neck images and 18.2 % in chest images.

CONCLUSIONS

CEF preprocessing improved the NMI and registration accuracy of mutual-information-based automatic image registration on the medical images. The proposed method achieved accuracy equivalent to that achieved by experienced therapists and it will significantly contribute to the standardization of image registration quality.