Effective lung ventilation estimation based on 4D CT image registration and supervoxels

Abstract

Most computed tomography (CT)-derived ventilation estimation methods rely on deformation fields of image registration to track the volume variations of each voxel, which are easily affected by image registration results and motion artifacts of four-dimensional (4D) CT images. To attack these problems, a lung ventilation estimation method based on 4D CT image registration and supervoxels is proposed in this paper. In order to avoid estimation errors caused by image artifacts and noises in the methods of directly estimating voxel-wise ventilation, images corresponding to maximum exhale phase are represented by multi-level supervoxels. Then, according to the relationship between registered CT values in Hounsfield units (HU) and regional volume change, a ventilation estimation method is designed to calculate the whole ventilation of each supervoxel. To accurately recover the ventilation of each voxel from the supervoxel region, a linear programming model is established to obtain the ventilation of each voxel, and the final estimated lung ventilation image is obtained by averaging the recovered results of all supervoxels layers. To evaluate our proposed ventilation estimation method, we calculate voxel-wise Spearman coefficient rs and Dice similarity coefficient (DSC for low function lung DSClow and high function lung DSChigh) of various CT-derived ventilation methods on VAMPIRE dataset. The experimental results on VAMPIRE dataset show that the average rs values between our proposed method can achieve 0.38 (-0.17–0.70). Accordingly, the average DSClow and DSChigh values of our proposed method are 0.40 (0.17–0.67) and 0.36 (0.08–0.61) respectively.