Analysis of the setup errors of medical image registration-based cone-beam CT for lung cancer

This study aimed to investigate the feasibility of efficiently using a rigid image registration (RIR) algorithm or a deformable image registration (DIR) algorithm to match medical images and evaluate the impact of setup errors on intensity modulated radiation therapy of lung cancer patients.

Ten lung cancer patients were chosen randomly each day and were subjected to image-guided radiotherapy. The clinical registration between cone-beam computed tomography (CBCT) images and treatment planning system CT images was performed by applying both RIR and DIR; the clinical registration was evaluated on the basis of the contour index, including dice similarity coefficient, sensitivity, and positive predictive value; the optimal scheme of image registration was selected to ensure that the actual irradiation isocenter was consistent with the treatment planning isocenter. In each patient, the translational errors in the right-left (x), superior-inferior (y), and anterior-posterior (z) directions and the rotational errors in the u, υ, and w directions formed by the x, y, and z directions were calculated and analyzed daily in the whole course of treatment; margins were calculated according to this equation: M = 2.5∑+ 0.7δ.

The tumors and the surrounding soft tissues of the patients are shown more clearly in the CBCT images than in the CT images. DIR can be applied more efficiently than RIR to determine the morphological and positional changes in the organs shown in the images with the same or different modalities in the different period. The setup errors in translation in the x, y and z axes were 0.05±0.16, 0.09±0.32 and -0.02±0.13 cm, respectively; by contrast, the setup errors in rotation in u, υ and w directions were (0.41±0.64)°, (-0.08±0.57)° and (-0.03±0.62)°, respectively. The setup errors in the x, y and z axes of the patients indicated that the margins expansions were 0.82, 1.15 and 0.72 cm, respectively.

CBCT with DIR can measure and correct the setup errors online; as a result, setup errors in lung cancer treatments can be significantly reduced and the accuracy of radiotherapy can be enhanced.