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Edge-preserving Noise Reduction in CT based on Identification of CorrelationsComputerized tomography (CT) is one of the most important imaging
modalities in radiological diagnosis. However, the radiation exposure
associated with CT is generally regarded to be the main disadvantage of
the method. With respect to patients care the limitation of exposure is
definitely desirable. The problem arising from the demand for dose
reduction is its direct impact on image quality. Halving the radiation
dose increases pixel noise in the images by a factor of square root of
two. For a reliable diagnosis the ratio between relevant tissue
contrasts and the noise amplitude must be sufficiently large. Thus the
dose of radiation cannot be reduced arbitrarily. The topic of this
project is to develop a method for edge-preserving noise reduction based
on correlation analysis in order to reduce noise in CT data. The goal is
to achieve either improved image quality at constant dose or to reduce
the dose of radiation without impairing image quality. Up to now a wavelet transformation based method has been investigated,
in order to reduce noise in the reconstructed slices. In contrast to
other common methods for noise reduction the algorithm takes two or more
datasets as its input. The input images are spatially identical but
taken at different points in time, leading to uncorrelated noise in the
images. Such data can for example be generated by separate
reconstruction each with only every second projection. Correlation
analysis based on the input images or rather their
wavelet-representation allow the differentiation between structure and
noise. Several two-dimensional Wavelet transformations (dyadic, stationary,
à-trous and quin-cunx) as well as different Wavelets were used for the
local frequency analysis and compared to each other. Furthermore,
different methods for correlation analysis were investigated. The
methods were evaluated with respect to their achieved noise reduction
rate and the preservation of edges. In order to achieve an anisotropic noise reduction, the wavelet
coefficients need to be analyzed direction dependent. Therefore, a new
method was developed for estimating the standard deviation of noise from
the differences of the wavelet coefficients of the separately
reconstructed images. The so computed direction dependent weights allow
an anisotropic denoising. Furthermore, the method was extended to work
in 3D. This resulted in improved image quality, visually and
quantitatively. This project is financed by Siemens Medical Solutions. On one hand the
close cooperation enables a knowledge transfer concerning
state-of-the-art research and on the other hand it provides access to
the newest generation of medical devices.
| Project manager: Prof. Dr.-Ing. Joachim Hornegger, Dr. rer. nat. Rainer Raupach (Siemens Med. Sol.)
Project participants: Dr.-Ing. Anja Borsdorf
Keywords: CT; noise reduction; correlation analysis
Duration: 1.1.2006 - 30.6.2009
Sponsored by: Siemens Medical Solutions
Contact: Borsdorf, Anja E-Mail: borsdorf@informatik.uni-erlangen.de
| Publications |
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Mayer, Markus ; Borsdorf, Anja ; Köstler, Harald ; Hornegger, Joachim ; Rüde, Ulrich: Nonlinear Diffusion Noise Reduction in CT Using Correlation Analysis. In: Hornegger, Joachim ; Mayr, Ernst W. ; Schookin, Sergey ; Feußner, Hubertus ; Navab, Nassir ; Gulyaev, Yuri V. ; Höller, Kurt ; Ganzha, Victor (Ed.) : 3rd Russian-Bavarian Conference on Biomedical Engineering (3rd Russian-Bavarian Conference on Biomedical Engineering Erlangen 2.-3.07.2007). Vol. 1. Erlangen : Union aktuell, 2007, pp 155-159. | Borsdorf, Anja ; Raupach, R. ; Hornegger, Joachim: Separate CT-Reconstruction for Orientation and Position Adaptive Wavelet Denoising. In: Horsch, Alexander ; Deserno, Thomas M. ; Handels, Heinz ; Meinzer, Hans-Peter ; Tolxdoff, Thomas (Ed.) : Bildverarbeitung für die Medizin 2007 (BVM 2007 München 25.-27.03.2007). Berlin : Springer, 2007, pp 232-236. - ISBN 978-3-540-71090-5 | Mayer, Markus ; Borsdorf, Anja ; Köstler, Harald ; Hornegger, Joachim ; Rüde, Ulrich: Nonlinear Diffusion vs. Wavelet Based Noise Reduction in CT Using Correlation Analysis. In: Lensch, H.P.A. ; Rosenhahn, B. ; Seidel, H.-P. ; Slusallek, P. ; Weickert, J. (Ed.) : Vision, Modelling, and Visualisation 2007 (Vision, Modelling, and Visualisation 2007 saarbrücken 7.-9.11.2007). 1. Edition Saarbrücken : Max-Planck-Institut fuer Informatik, 2007, pp 223-232. | Borsdorf, Anja ; Raupach, Rainer ; Hornegger, Joachim: Separate CT-Reconstruction for 3D Wavelet Based Noise Reduction Using Correlation Analysis. In: Yu, Bo (Ed.) : IEEE NSS/MIC Conference Record (IEEE Nuclear Science Symposium and Medical Imaging Conference Honolulu, USA 27.10.-03.11.2007). 2007, pp 2633-2638. | Borsdorf, Anja ; Raupach, Rainer ; Hornegger, Joachim: Wavelet based Noise Reduction by Identification of Correlation. In: Franke, Katrin ; Müller, Klaus-Robert ; Nickolay, Bertram ; Schäfer, Ralf (Ed.) : Pattern Recognition (DAGM 2006), Lecture Notes in Computer Science (28th DAGM Symposium Berlin 12.-14.09.2006). Vol. 4174. Berlin : Springer, 2006, pp 21-30. - ISBN 3-540-44412-2 | Borsdorf, Anja ; Raupach, Rainer ; Hornegger, Joachim: Multiple CT-reconstructions for locally adaptive anisotropic wavelet denoising. In: International Journal of Computer Assisted Radiology and Surgery 2 (2008), No. 5, pp 255-264 | Borsdorf, Anja ; Raupach, R. ; Flohr, T. ; Hornegger, Joachim: Wavelet based Noise Reduction in CT-Images Using Correlation Analysis. In: IEEE Transactions on Medical Imaging 27 (2008), No. 12, pp 1685-1703 |
Institution: Chair of Computer Science 5 (Pattern Recognition)
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