Evaluation of template matching for tumor motion management with cine-MR images in lung cancer patients.
OBJECTIVE
Accurate determination of tumor position is crucial for successful application of motion compensated radiotherapy in lung cancer patients. This study tested the performance of an automated template matching algorithm in tracking the tumor position on cine-MR images by examining the tracking error and further comparing the tracking error to the interoperator variability of three human reviewers.


METHODS
Cine-MR images of 12 lung cancer patients were analyzed. Tumor positions were determined both automatically with template matching and manually by a radiation oncologist and two additional reviewers trained by the radiation oncologist. Performance of the automated template matching was compared against the ground truth established by the radiation oncologist. Additionally, the tracking error of template matching, defined as the difference in the tumor positions determined with template matching and the ground truth, was investigated and compared to the interoperator variability for all patients in the anterior-posterior (AP) and superior-inferior (SI) directions, respectively.


RESULTS
The median tracking error for ten out of the 12 patients studied in both the AP and SI directions was less than 1 pixel (= 1.95 mm). Furthermore, the median tracking error for seven patients in the AP direction and nine patients in the SI direction was less than half a pixel (= 0.975 mm). The median tracking error was positively correlated with the tumor motion magnitude in both the AP (R = 0.55, p = 0.06) and SI (R = 0.67, p = 0.02) directions. Also, a strong correlation was observed between tracking error and interoperator variability (y = 0.26 + 1.25x, R = 0.84, p < 0.001) with the latter larger.


CONCLUSIONS
Results from this study indicate that the performance of template matching is comparable with or better than that of manual tumor localization. This study serves as preliminary investigations towards developing online motion tracking techniques for hybrid MRI-Linac systems. Accuracy of template matching makes it a suitable candidate to replace the labor intensive manual tumor localization for obtaining the ground truth when testing other motion management techniques.
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201.
Xiutao
X
Shi
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201.
Tejan
T
Diwanji
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201.
Karen E
KE
Mooney
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201.
Jolinta
J
Lin
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201.
Steven
S
Feigenberg
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201.
Warren D
WD
D'Souza
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201.
Nilesh N
NN
Mistry
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology* / Q000532:radiotherapy*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000473:pathology* / Q000532:radiotherapy*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D015203:Reproducibility of Results