Abstract
Computed tomography (CT) images with a low-dose protocol generally have severe mottle noise and streak artifacts. In this paper, we propose a novel diffusion method named “artifact suppressed nonlinear diffusion filtering (ASNDF),” to process low-dose CT (LDCT) images. Different from other diffusion filtering methods, the proposed ASNDF not only includes image gradient as the main cue to construct a diffusion coefficient function, but also incorporates the local variances of image to be diffused and residual image between two adjacent diffusions. In detail, the classical PM diffusion is first performed to get the initial residual image, and then from the second iteration, the LDCT image is processed according to the ASNDF processing. Simulated data, clinical data and rat data are conducted to evaluate the proposed method, and the comparison experiments with other competing methods show that the proposed ASNDF method makes an improvement in artifact suppression and structure preservation, and offers a sound alternative to process LDCT images from most current CT systems.
Highlights
Low-dose computed tomography (LDCT) was first proposed by Naidich in 1990 for the reason that the radiation doses delivered to patients during X-ray Computed tomography (CT) procedures may lead to potential determinist and stochastic risks [1]
Radiation dose reduction is beneficial for human health, it leads to the filtered back projection (FBP) reconstructed images degraded with serious mottle noise and streak artifacts [7], [8]
MATERIALS AND METHODS To overcome the drawback that only image gradient magnitude is used to be the main cue of edge indicator, and effectively suppress artifacts in LDCT images, we propose the artifact suppressed nonlinear diffusion filtering (ASNDF) method based on PM model
Summary
Low-dose computed tomography (LDCT) was first proposed by Naidich in 1990 for the reason that the radiation doses delivered to patients during X-ray CT procedures may lead to potential determinist and stochastic risks [1]. Many denoising approaches have been proposed to deal with the noisy projection data, such as multi-dimensional adaptive filtering [9], nonlinear filtering [10], penalized weighted least-squares (PWLS) approaches [11], [12], bilateral filtering [13], fuzzy filtering [14], [15], and iterative restoration [16]. Other techniques, such as the bilateral-like filter [17], multi-scale decomposition based method [18], have potentials to process noisy projection data.
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