A Hybrid Image Registration and Matching Framework for Real-Time Motion Tracking in MRI-Guided Radiotherapy

• Published in: IEEE transactions on biomedical engineering Vol. 65; no. 1; pp. 131 - 139
• Main Authors: Seregni, Matteo, Paganelli, Chiara, Summers, Paul, Bellomi, Massimo, Baroni, Guido, Riboldi, Marco
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Biomedical optical imaging; Computational efficiency; Computer applications; Computing time; Feature extraction; Humans; Image guidance; Image Processing, Computer-Assisted - methods; Image registration; Landmarks; Liver; Liver - diagnostic imaging; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - radiotherapy; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Matching; Motion compensation; motion tracking; Optical flow (image analysis); Optical imaging; Radiation therapy; radiotherapy; Radiotherapy, Image-Guided - methods; Real time; Real-time systems; Registration; Similarity; Tracking; Trajectories; Tumors; Workflow
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2017.2696361

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.