Abstract
Low dose computed tomography (CT) has drawn much attention in the medical imaging field because of its ability to reduce the radiation dose. Recently, statistical iterative reconstruction (SIR) with total variation (TV) penalty has been developed to low dose CT image reconstruction. Nevertheless, the TV penalty has the drawback of creating blocky effects in the reconstructed images. To overcome the limitations of TV, in this paper we firstly introduce the structure tensor total variation (STV1) penalty into SIR framework for low dose CT image reconstruction. Then, an accelerated fast iterative shrinkage thresholding algorithm (AFISTA) is developed to minimize the objective function. The proposed AFISTA reconstruction algorithm was evaluated using numerical simulated low dose projection based on two CT images and realistic low dose projection data of a sheep lung CT perfusion. The experimental results demonstrated that our proposed STV1-based algorithm outperform FBP and TV-based algorithm in terms of removing noise and restraining blocky effects.
Highlights
The X-ray computer tomography (CT) has been extensively utilized in industry nondestructive testing and medical diagnosis
Inspired by the above research, in this paper we introduce the structure tensor total variation (STV) penalty into the statistical iterative reconstruction (SIR) framework for low dose CT reconstruction, and develop an accelerated fast iterative shrinkage thresholding algorithm (AFISTA) to minimize the associated objective function
A series of numerical simulation experiments were designed to evaluate the performance of the proposed method in CT image reconstruction from a low dose situation
Summary
The X-ray computer tomography (CT) has been extensively utilized in industry nondestructive testing and medical diagnosis. The voltage values (kilovolt (kV)) is the most straightforward way to reduce the radiation dose because it does not need to change the scanning structure of existing CT equipment. This method will result in insufficient number of x-ray photons detected at the detector and upgrade the quantum noise level on the sinogram. In this situation, for most current commercial CT scanners, the often used Feldkamp-Davis-Kress algorithm (or its variants) will lead to severe image quality degradation due to noisy projection. It is highly desirable to develop a new method to reconstruct the high-quality image for LDCT imaging
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