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Published on: October 27, 2023
A Hybrid Image Registration and Matching Framework for Real-Time Motion Tracking in MRI-Guided Radiotherapy
Objective:
MRI-guided radiotherapy (MRIgRT) is an emerging treatment technique where anatomical and pathological structures are imaged through integrated MR-radiotherapy units. This work aims 1) at assessing the accuracy of optical-flow-based motion tracking in liver cine-MRI sequences; and 2) at testing a MRIgRT workflow combining similarity-based image matching with image registration.
Methods:
After an initialization stage, a set of template images is collected and registered to the first frame of the cine-MRI sequence. Subsequent incoming frames are either matched to the most similar template image or registered to the first frame when the similarity index is lower than a given threshold. The tracking accuracy was evaluated by considering ground-truth liver landmarks trajectories, as obtained through the scale-invariant features transform (SIFT).
Results:
Results on a population of 30 liver subjects show that the median difference between SIFT- and optical flow-based landmarks trajectories is 1.0 mm, i.e., lower than the cine-MRI pixel size (1.28 mm). The computational time of the motion tracking workflow (<50 ms) is suitable for real-time motion compensation in MRIgRT. Such time could be further reduced to ≍30 ms with limited loss of accuracy by the combined image matching/registration approach.
Conclusion:
The reported workflow allows us to track liver motion with accuracy comparable to robust feature matching. Its computational time is suitable for online motion monitoring.
Significance:
Real-time feedback on the patient anatomy is a crucial requirement for the treatment of mobile tumors using advanced motion mitigation strategies.
