The Potential of Positron Emission Tomography (PET) for Intra-Treatment Dynamic Tumor Tracking During Radiotherapy: A Phantom Study
J Yang1,2*, T Yamamoto2,3, S Mazin4, J Cui1, E Graves2, P Keall5, (1) Department of Electrical Engineering, Stanford University, Stanford, CA, (2) Department of Radiation Oncology, Stanford University, Stanford, CA, (3) Department of Radiation Oncology, University of California Davis School of Medicine Sacramento, CA, (4) Reflexion Medical, Burlingame, CA, (5) Radiation Physics Laboratory, University of Sydney, Sydney, NSW, AustraliaTH-A-WAB-9 Thursday 8:00AM - 9:55AM Room: Wabash Ballroom
Purpose: This study aimed to evaluate the potential and feasibility of positron emission tomography (PET) for dynamic tumor tracking during radiation treatment. We have proposed a center of mass (CoM) tumor tracking algorithm and investigated the geometric accuracy of the algorithm.
Methods: The proposed PET dynamic tumor tracking algorithm estimated the target position information through the CoM of the segmented target volume on gated PET images which were continuously updated throughout a scan. External respiratory motion and list-mode PET data were acquired from a phantom programmed to move with measured respiratory traces. The phantom was cylindrical with six sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). To test the algorithm on respiratory motion with clinical variability, respiratory traces of higher than average magnitudes and complexity were selected from a large database. The traces were 3D-measured motion representing typical motion (T1), baseline variations (T2), predominantly left-right motion (T3), and high-frequency breathing (T4). Estimation errors were quantified through Euclidean distances between the motion traces and estimated CoM trajectories.
Results: The overall time-averaged error was 1.6 mm over all traces (T1-T4) of all targets. There were small variations in the errors between the traces, although they were very distinct. The overall time-averaged error of each trace (T1-T4) was 1.3, 1.3, 1.7, and 2.2 mm, respectively. The accuracy of the estimates was consistent for all targets except the smallest one: For the 17 mm target, the overall time-averaged errors of T1-T4 were 1.2, 1.2, 1.4, and 1.7 mm, respectively; For the 10 mm target, those of T1-T4 were 2.3, 2.2, 2.6, and 3.9 mm, respectively.
Conclusion: We developed an algorithm using list-mode PET data and external respiratory motion data for dynamic tumor tracking in a combined PET-linac system. The overall tracking error in phantom studies was less than 2 mm.
Funding Support, Disclosures, and Conflict of Interest: Research supported by the Kwanjeong Educational Foundation, NIH/NCI R01 93626, Stanford Bio-X, NHMRC Australia Fellowship, NIH/NCI through SBIR with RefleXion Medical R43CA153466.
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