Abstract
Convolutional neural network (CNN)-based deep learning techniques have enjoyed many successful applications in the field of medical imaging. However, the complicated between-manifold projection from the projection domain to the spatial domain hinders the direct application of CNN techniques in computed tomography (CT) reconstruction. In this work, we proposed a novel CT reconstruction framework based on a CNN, i.e., an intelligent back-projection network (iBP-Net). The proposed iBP-Net method fused a pre-CNN, a back-projection layer, and a post-CNN into an end-to-end network. The pre-CNN adopted CNN techniques to model a filtering operation in the projection domain. In the back-projection layer, a back-projection operation was employed to perform between-manifold projection. Based on CNN techniques, the post-CNN worked together with the pre-CNN to recover reconstructed images from the outputs of the back-projection layer in the spatial domain while maintaining high visual sensitivity. The experimental results demonstrate the feasibility of the proposed iBP-Net framework for CT reconstruction.
Highlights
X-ray computed tomography (CT) is one of the most valuable and widely used imaging techniques in clinical, industrial, and other applications [1]
EVALUATION METRICS We evaluated the quantitative performance of intelligent back-projection network (iBP-Net) and several comparison models by adopting three popular indexes, i.e., the peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), and structural similarity index measure (SSIM) [36]
In this work, we proposed a novel CT reconstruction framework based on Convolutional neural network (CNN), named iBP-Net
Summary
X-ray computed tomography (CT) is one of the most valuable and widely used imaging techniques in clinical, industrial, and other applications [1]. The main problem in tomography is the process of reconstructing unknown images from their projections. In the past 30 years, with the wide application of tomography techniques in many fields, researchers have dedicated major efforts to the development of CT image reconstruction methods. Because CT image reconstruction is a typical example of an ill-posed inverse problem, it is difficult to find accurate reconstructions in practice. Current solutions for improving the quality of reconstructions can be roughly divided into three categories: analytical methods, iterative reconstruction (IR) methods and artificial neural network methods. An analytical method is a direct reconstruction method based on the central slice theorem, which provides the relationship between the Radon transform of an object and its two-dimensional Fourier transform.
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