Spatial domain filtering for fast modification of the tradeoff between image sharpness and pixel noise in computed tomography

Abstract

In computed tomography (CT), selection of a convolution kernel determines the tradeoff between image sharpness and pixel noise. For certain clinical applications it is desirable to have two or more sets of images with different settings. So far, this typically requires reconstruction of several sets of images. We present an alternative approach using default reconstruction of sharp images and online filtering in the spatial domain allowing modification of the sharpness-noise tradeoff in real time. A suitable smoothing filter function in the frequency domain is the ratio of smooth and original (sharp) kernel. Efficient implementation can be achieved by a Fourier transform of this ratio to the spatial domain. Separating the two-dimensional spatial filtering into two subsequent one-dimensional filtering stages in the x and y directions using a Gaussian approximation for the convolution kernel further reduces computational complexity. Due to efficient implementation, interactive modification of the filter settings becomes possible, which can completely replace the variety of different reconstruction kernels.

Computer-Aided Measurements of Pulmonary Emphysema in Chest Multidetector-Row Spiral Computed Tomography

“…These results fitted perfect to the theoretical considerations in previous studies. 10,18,19 A sharp algorithm amplifies the contrast that leads to an overenhancing between different voxels. Therefore, the pattern of the low attenuation values of the voxels leads to decreased attenuation, resulting in an increased pulmonary EV.…”

High speed interpolator with fast adder

“…The sharpening spatial filter [3], [4] is a kind of high pass filter that passes signals above a cutoff value. This filter is actually a mask of weights arranged in rectangular pattern.…”

Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT

“…8,9 Iterative reconstruction methods can achieve significant denoising but at the expense of very long computation times. 10 Techniques based entirely on image space have also been described, taking advantage of the image structure to smooth noise while preserving edges but suffering from the complicated properties of noise in image space in CT. [11][12][13] In this work, we investigated a locally adaptive method for noise control in CT. This method is based on bilateral filtering, 14 which smooths the sinogram by using a weighted average in a local neighborhood, with the weights determined according to both the spatial proximity and intensity similarity between the center pixel and the neighboring pixels.…”