Resolution enhancement for lung 4D-CT based on transversal structures by using multiple Gaussian process regression learning.

Abstract

Four-dimensional computed tomography (4D-CT) plays a useful role in many clinical situations. However, due to the hardware limitation of system, dense sampling along superior-inferior direction is often not practical. In this paper, we develop a novel multiple Gaussian process regression model to enhance the superior-inferior resolution for lung 4D-CT based on transversal structures. The proposed strategy is based on the observation that high resolution transversal images can recover missing pixels in the superior-inferior direction. Based on this observation and motived by random forest algorithm, we employ multiple Gaussian process regression model learned from transversal images to improve superior-inferior resolution. Specifically, we first randomly sample 3×3 patches from original transversal images. The central pixel of these patches and the eight-neighbour pixels of their corresponding degraded versions form the label and input of training data, respectively. Multiple Gaussian process regression model is then built on the basis of multiple training subsets obtained by random sampling. Finally, the central pixel of the patch is estimated based on the proposed model, with the eight-neighbour pixels of each 3×3 patch from interpolated superior-inferior direction images as inputs. The performance of our method is extensively evaluated using simulated and publicly available datasets. Our experiments show the remarkable performance of the proposed method. In this paper, we propose a new approach to improve the 4D-CT resolution, which does not require any external data and hardware support, and can produce clear coronal/sagittal images for easy viewing.