Abstract
A linear denoising filter is usually of lowpass type, and the main parameter in a lowpass filter is the cutoff frequency. The lowpass filters are normally shift invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain. This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model. An example of such a linear filter is presented for low-dose X-ray computed tomography (CT).
Highlights
Nowadays, the iterative image reconstruction algorithms have become more popular than the analytic filtered backprojection (FBP) algorithm [1,2,3,4,5]
The projections were first filtered by the proposed filter using Eq (2) and the conventional FBP algorithm was used to reconstruct the final image
The imaging geometry was cone-beam, the X-ray source trajectory was a circle of radius 600 mm
Summary
The iterative image reconstruction algorithms have become more popular than the analytic filtered backprojection (FBP) algorithm [1,2,3,4,5]. The main motivation of using an iterative algorithm is that it is much easier to incorporate the noise model into the iterative algorithm than into an analytic algorithm such as FBP [6, 7]. [9], a non-linear adaptive filter was proposed, in which a threshold value was assigned to determine whether the measurements were required to be filtered. [11, 12], a linear FBP algorithm was proposed to mimic the iterative Landweber algorithm [13] and to incorporate the projection noise in a window function in the Fourier domain. The 10 versions of the filtered measurements were combined into one and used in the backprojector
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