Paper
3 July 2001 Fast image filters as an alternative to reconstruction kernels in computed tomography
Thomas Flohr, Stefan Schaller, Alexander Stadler, Wolfgang Brandhuber, Matthias U. Niethammer, Klaus W. Klingenbeck-Regn, Peter Steffen
Author Affiliations +
Abstract
In Computed Tomography, axial resolution is determined by the slice collimation and the spiral algorithm, while in-plane resolution is determined by the reconstruction kernel. Both choices select a tradeoff between image resolution (sharpness) and pixel noise. We investigated an alternative approach using default settings for image reconstruction which provide narrow reconstructed slice-width and high in-plane resolution. If smoother images are desired, we filter the original (sharp) images, instead of performing a new reconstruction with a smoother kernel. A suitable filter function in the frequency domain is the ratio of smooth and original (sharp) kernel. Efficient implementation was achieved by a Fourier transform of this ratio to the spatial domain. Separating the 2D spatial filtering into two subsequent 1D filtering stages in x- and y-direction further reduces computational complexity. Using this approach, arbitrarily oriented multi-planar reformats (MPRs) can be treated in exactly the same way as axial images. Due to efficient implementation, interactive modification of the filter settings becomes possible, which completely replace the variety of different reconstruction kernels. We implemented a further promising application of the method to thorax imaging, where different regions of the thorax (lungs and mediastinum) are jointly presented in the same images using different filter settings and different windowing.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas Flohr, Stefan Schaller, Alexander Stadler, Wolfgang Brandhuber, Matthias U. Niethammer, Klaus W. Klingenbeck-Regn, and Peter Steffen "Fast image filters as an alternative to reconstruction kernels in computed tomography", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.430965
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Cited by 2 scholarly publications.
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KEYWORDS
Image filtering

Convolution

Lung

Computed tomography

Fourier transforms

Image resolution

X-ray computed tomography

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