The MATLAB/C program take [Elektronisk resurs] : a program for simulation of X-ray projections from 3D volume data : demonstration of beam-hardening artefacts in subsequent CT reconstruction / Olle Seger, Maria Magnusson Seger
Seger, Olle, 1948- (författare)
- Magnusson Seger, Maria, 1961- (författare)
Alternativt namn: Seger, Maria Magnusson, 1961-
Se även: Magnusson, Maria, 1961-
- Linköping : Linköpings universitet, 2005
- Engelska 56 s.
Serie: LiTH-ISY-R, 1400-3902 ; 2682
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- The MATLAB/C program take version 3.1 is a program for simulation of X-ray projections from 3D volume data. It is based on an older C version by Muller-Merbach as well as an extended C version by Turbell. The program can simulate 2D X-ray projections from 3D objects. These data can then be input to 3D reconstruction algorithms. Here however, we only demonstrate a couple of 2D reconstruction algorithms, written in MATLAB. Simple MATLAB examples show how to generate the take projections followed by subsequent reconstruction. Compared to the old take version, the C code have been carefully revised. A preliminary, rather untested feature of using a polychromatic X-ray source with different energy levels was already included in the old take version. The current polychromatic feature X-ray is however carefully tested. For example, it has been compared with the results from the program described by Malusek et al. We also demonstrate experiments with a polychromatic X-ray source and a Plexiglass object giving the beam-hardening artefact. Detector sensitivity for different energy levels is not included in take. However, in section~\refsec:realexperiment, we describe a technique to include the detector sensitivity into the energy spectrum. Finally, an experiment with comparison of real and simulated data were performed. The result wasn't completely successful, but we still demonstrate it. Contemporary analytical reconstruction methods for helical cone-beam CT have to be designed to handle the Long Object Problem. Normally, a moderate amount of over-scanning is sufficient for reconstruction of a certain Region-of-interest (ROI). Unfortunately, for iterative methods, it seems that the useful ROI will diminish for every iteration step. The remedies proposed here are twofold. Firstly, we use careful extrapolation and masking of projection data. Secondly, we generate and utilize projection data from incompletely reconstructed volume parts, which is rather counter-intuitive and contradictory to our initial assumptions. The results seem very encouraging. Even voxels close to the boundary in the original ROI are as well enhanced by the iterative loop as the middle part.
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