Image reconstruction framework for helical cone-beam CT by combining compressed sensing and deep learning

Abstract

Compressed sensing (CS) image reconstruction in CT suffers from the drawbacks such as 1) appearance of staircase artifacts and 2) loss in image textures and smooth intensity changes. These drawbacks stem from the fact that CS is based on approximating the image by a piecewise-constant function. To overcome this drawback, we have already proposed a framework to improve image quality in CS using deep learning. In this framework, FBP reconstructed image and CS (TV or Nonlocal TV) reconstructed image are inputted to CNN with two input channels and single output channel, and a final reconstructed image is obtained by the output of CNN. Parameters (weight and bias) of CNN together with a regularization parameter of CS are estimated by minimizing an average least-squares loss function by using learning data, i.e. a set of triplet of degraded FBP reconstruction, CS reconstruction, and answer image. In this paper, this framework is extended to 3-D image reconstruction in helical cone-beam CT operated with lowdose scanning protocol. Parameters (weight and bias) of CNN together with a regularization parameter of CS are estimated by minimizing an average least-squares loss function by using learning data, i.e. a set of triplet of degraded FBP reconstruction, CS reconstruction, and answer image. In this paper, this framework was extended to 3-D image reconstruction in helical cone-beam CT operated with lowdose scanning protocol. The extension was done in the following way. First, we prepare N different 2-D denoising CNN (CNN₁, CNN₂, . . . , CNNN ) dependent on the slice position n. Each slice of the short-scan FDK reconstruction without denoising y_i and with 3-D TV (or Nonlocal TV) denoising z_i are inputted to CNNn with the closest slice index n, which yields a corresponding output image for each slice x_i . The final reconstructed image is obtained by stacking every slice x_i (i = 1, 2, . . . , I).