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Deformable MRI-CT registration for breast cancer radiation treatment planning using a sequentially applied semi-physical model regularization method

Purpose. To develop an optimization scheme and semi-physical regularization method for accurate and efficient deformable image registration (DIR) between magnetic resonance imaging (MRI) and computed tomography (CT) for breast cancer radiation therapy planning. Methods and Materials. A sequentially-applied semi-physical model regularization method with bending energy limited diffeomorphism (BELD) was developed to regularize transformation in free-form (B-spline) DIR. Bony structures were first segmented to account for their rigidity during deformation. Feature points were identified and filtered to be used as a penalty term. Rigidity, BELD, and feature point distance penalty terms are applied sequentially during the optimization process based on mutual information metric. Sample MRI and CT images of breast cancer patients were used to demonstrate the registration. Agreements of feature point pairs and contour transfer between the two image modalities were measured to illustrate the accuracy and efficiency for the developed DIR method. Results. The newly developed tool can accurately register MRI to CT with minimum user intervention for situations with moderate to large deformations. For the sample cases tested, the average feature point pair distance was reduced from 6.7 to 3.2 mm by simple B-spline registration and to 1.8 mm by the newly-developed registration method. Compared to simultaneous optimization of multiple penalty terms, the computation time was shortened by 30% and the accuracy was improved by 15%. The bone area warping artifact in deformed MRI disappeared in the proposed method. Considering the resolution of MR images is 1.3 mm, the resulting feature point pair distance is satisfactory. Conclusion. The proposed DIR method of sequentially applying physical model regularization terms during the optimization process can reduce the pitfalls of local extremity, weighting factor adjustment and multi-resolution optimization stages, therefore improving the accuracy and efficiency of MRI to CT registration even with moderate to large deformations commonly seen in radiation treatment planning with multi-modality images.