Browsing by Author "Ng, Kah Kee"

Purpose: This project aimed to establish a method for computing 3D cardiac motion given continuous 2D-Cine MRI frames as the inputs. This approach would be useful for continuously monitoring cardiac and respiratory motion during MR-guided cardiac radiation therapy, and thus supporting radiation delivery guidance and gating.Methods: 5D-MRI datasets of seven patients, with each consisting of 3D spatial volumes of the cardiac cycle and respiratory cycle, were used for quantitative evaluation of the heart motion due to respiratory and cardiac movements. This was achieved through deformable image registration (DIR). Subsequently, principal component analysis (PCA) was performed on the computed deformation vector fields (DVF) to extract scores that effectively represent the characteristics of the DVFs. A deep learning model was then trained to predict the cardiac motion PCA scores given the inputs of 2D-Cine MRI. The predicted PCA scores were then transformed into 3D DVFs, which were then used to track 3D target motion. Results: The model’s performance was quantitatively evaluated on ground truth data that were withheld from model training. Across all 7 subjects, the average 3D DVF prediction errors for the heart region consistently remained around 0.3 ± 0.1mm. The predicted target motion, computed from the predicted DVFs, was visually evaluated, and found to be satisfactory. Conclusion: The developed method demonstrated promising potential in accurately computing and tracking real-time 3D cardiac motion given 2D-Cine MRI inputs. This approach presents a viable solution for continuously monitoring the 3D cardiac and respiratory motion of the heart during MR-guided cardiac radiation therapy.