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Med Phys. 2014 Feb;41(2):021718. doi: 10.1118/1.4861816.

The potential of positron emission tomography for intratreatment dynamic lung tumor tracking: a phantom study.

Author information

1
Department of Electrical Engineering, Stanford University, Stanford, California 94305 and Department of Radiation Oncology, Stanford University, Stanford, California 94305.
2
Department of Radiation Oncology, University of California Davis, Sacramento, California 95817.
3
RefleXion Medical, Inc., Burlingame, California 94010.
4
Department of Radiation Oncology, Stanford University, Stanford, California 94305.
5
Radiation Physics Laboratory, University of Sydney, Sydney, NSW 2006, Australia.

Abstract

PURPOSE:

This study aims to evaluate the potential and feasibility of positron emission tomography for dynamic lung tumor tracking during radiation treatment. The authors propose a center of mass (CoM) tumor tracking algorithm using gated-PET images combined with a respiratory monitor and investigate the geometric accuracy of the proposed algorithm.

METHODS:

The proposed PET dynamic lung tumor tracking algorithm estimated the target position information through the CoM of the segmented target volume on gated PET images reconstructed from accumulated coincidence events. The information was continuously updated throughout a scan based on the assumption that real-time processing was supported (actual processing time at each frame ≈ 10 s). External respiratory motion and list-mode PET data were acquired from a phantom programmed to move with measured respiratory traces (external respiratory motion and internal target motion) from human subjects, for which the ground truth target position was known as a function of time. The phantom was cylindrical with six hollow sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). The measured respiratory traces consisted of two sets: (1) 1D-measured motion from ten healthy volunteers and (2) 3D-measured motion from four lung cancer patients. The authors evaluated the geometric accuracy of the proposed algorithm by quantifying estimation errors (Euclidean distance) between the actual motion of targets (1D-motion and 3D-motion traces) and CoM trajectories estimated by the proposed algorithm as a function of time.

RESULTS:

The time-averaged error of 1D-motion traces over all trajectories of all targets was 1.6 mm. The error trajectories decreased with time as coincidence events were accumulated. The overall error trajectory of 1D-motion traces converged to within 2 mm in approximately 90 s. As expected, more accurate results were obtained for larger targets. For example, for the 37 mm target, the average error over all 1D-motion traces was 1.1 mm; and for the 10 mm target, the average error over all 1D-motion traces was 2.8 mm. The overall time-averaged error of 3D-motion traces was 1.6 mm, which was comparable to that of the 1D-motion traces. There were small variations in the errors between the 3D-motion traces, although the motion trajectories were very different. The accuracy of the estimates was consistent for all targets except for the smallest.

CONCLUSIONS:

The authors developed an algorithm for dynamic lung tumor tracking using list-mode PET data and a respiratory motion signal, and demonstrated proof-of-principle for PET-guided lung tumor tracking. The overall tracking error in phantom studies is less than 2 mm.

PMID:
24506609
PMCID:
PMC3977800
DOI:
10.1118/1.4861816
[Indexed for MEDLINE]
Free PMC Article

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