
Proceedings Paper
A practical reconstruction algorithm for CT noise variance maps using FBP reconstructionFormat | Member Price | Non-Member Price |
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Paper Abstract
Accurate prediction of reconstructed noise in computed tomography (CT) images is important for purposes of
system design, optimization and evaluation. A large body of work describes noise prediction methods for CT,
the vast majority of which assume stationarity of both noise and signal processes. Consequently, these methods
are usually applied to and evaluated using simple phantoms, and only a portion of the image is scrutinized.
In this work, we derive a practical method for reconstructing CT noise variance maps for arbitrary objects.
Photon Poisson noise and system electronic noise are considered. The final formula has the same structure as
that of the filtered backprojection (FBP) formula, but with different weighting factors and convolution kernels.
The algorithm is verified using computer simulations of the Shepp-Logan phantom, and a good match is found
between the predicted noise map from one single noisy scan and the measured noise using 128 noisy scans.
As compared to other proposed noise models, our complementary work provides a method of noise prediction
by simple adaptation of FBP reconstruction algorithms. The result is a tool that can be useful for system
optimization and evaluation tasks as well as the design of reconstruction filters.
Paper Details
Date Published: 14 March 2007
PDF: 8 pages
Proc. SPIE 6510, Medical Imaging 2007: Physics of Medical Imaging, 651023 (14 March 2007); doi: 10.1117/12.713692
Published in SPIE Proceedings Vol. 6510:
Medical Imaging 2007: Physics of Medical Imaging
Jiang Hsieh; Michael J. Flynn, Editor(s)
PDF: 8 pages
Proc. SPIE 6510, Medical Imaging 2007: Physics of Medical Imaging, 651023 (14 March 2007); doi: 10.1117/12.713692
Show Author Affiliations
Lei Zhu, Stanford Univ. (United States)
Josh StarLack, Varian Medical Systems (United States)
Published in SPIE Proceedings Vol. 6510:
Medical Imaging 2007: Physics of Medical Imaging
Jiang Hsieh; Michael J. Flynn, Editor(s)
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