A Novel 4D-CT Sorting Method Based on Combined Mutual Information and Edge Gradient

Although mutual information is a general method usually being used to measure the similarity of two images, the robustness is questionable due to the absence of spatial information. The purpose of this study is to develop a feasible sorting technique for 4D-CT. A novel sorting algorithm named mutual information and edge gradient (MIEG), which includes spatial information by combining mutual information with a term based on the edge gradient of the image, was proposed to sort sequential CT images. The edge of image was extracted by calculating the wavelet transform modulus maxima, and the gradient similarity coefficient of the edge image was calculated and used to multiply by mutual information to form the final similarity metric. This sorting technique was validated by comparing the 4D-CTs reconstructed using MIEG and Real-time Position Management system (Varian Medical Systems, Inc., Palo Alto, CA). Tumor motion trajectories derived from 4D-CTs were analyzed in three orthogonal directions. Their correlation coefficients (CC) and differences in tumor motion magnitude (Ds) were determined. In addition, Dice similarity coefficient (DSC) was used to measure how well the tumor volumes segmented from the two 4D datasets overlapped with each other. For all patients, the mean CC values were >0.95 in all directions. The mean Ds were <; 0.64 mm in all directions. In addition, good overlapping was achieved between the segmented volumes with the mean DSC = 0.97 at end- and median-inspiration phases, respectively. This study proposed a feasible sorting method for 4D-CT, and its usefulness in imaging accurate tumor motion has been demonstrated.