Abstract
Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.
Highlights
Computed tomography (CT) uses precisely collimated X-rays, gamma rays, ultrasonic waves, or other types of beams in concert with highly sensitive detectors to sequentially scan individual sections of the human body
A non-training set low-dose CT (LDCT) image is used as the input image, combined with the training mapping relationship and a new computed tomography (CT) image is obtained by reconstructed by coupled dictionary learning
The CT image reconstructed by the method of the present invention is compared with the input LDCT image, the original high-dose CT (HDCT) image and the image reconstructed by the conventional interpolation method
Summary
Computed tomography (CT) uses precisely collimated X-rays, gamma rays, ultrasonic waves, or other types of beams in concert with highly sensitive detectors to sequentially scan individual sections of the human body. Many methods currently exist for reducing radiation doses, such as reducing the voltage, the current, the clinical scanning time and so on. According to the principle of compressed sensing [29,30] and sparse representation [31], an image vector x can be represented as a sparse linear combination of a dictionary D and it is mathematically expressed as follows:. Where α is the sparse representation coefficient and the content ||α||0 K, where K is the dimension of x, represents an image block. That is, where the number of atoms n is larger than the dimension of the image block. K, is often used for sparse representation; the sparse coefficient α can be obtained by an optimized estimation of the cost function. The sparse representation is extended to the SR problem via the following function:
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