The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed

BACKGROUND AND PURPOSE: For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non‐correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial‐, region‐, and intensity‐based) were jointly used in DIR algorithm for improved accuracy. MATERIALS AND METHODS: Three types of metrics were combined to form a single‐objective function in DIR algorithm. Fiducial‐based metric was used to minimize the distance between the corresponding point sets of two images. Region‐based metric was used to improve the overlap between the corresponding areas of two images. Intensity‐based metric was used to maximize the correlation between the corresponding voxel intensities of two images. The two CT images, one before surgery and the other after surgery, were acquired from the same patient in the same radiotherapy treatment position. Twenty patients who underwent breast‐conserving surgery and postoperative radiotherapy were enrolled in this study. RESULTS: For target registration error, the difference between the proposed and the conventional registration methods was statistically significant for soft tissue (2.06 vs. 7.82, p = 0.00024 < 0.05) and body boundary (3.70 vs. 6.93, p = 0.021 < 0.05). For visual assessment, the proposed method achieved better matching result for soft tissue and body boundary. CONCLUSIONS: Comparing to the conventional method, the registration accuracy of the proposed method was significantly improved. This method provided a feasible way for target volume delineation of tumor bed in postoperative radiotherapy of breast cancer patients.