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Local Computerized Tomography Using Wavelets

Local Computerized Tomography Using Wavelets. Chih-ting Wu. Wavelet Reconstruction from projection in 2 D. Motivation. Filtered Backprojection. Problem: The nonlocality of Radon trnasform in even dimension Goal: To reduce exposure to radiation Methods: 3-D tomography

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Local Computerized Tomography Using Wavelets

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  1. Local Computerized Tomography Using Wavelets Chih-ting Wu Wavelet Reconstruction from projection in 2 D Motivation Filtered Backprojection • Problem: The nonlocality of Radon trnasform in even dimension • Goal: To reduce exposure to radiation • Methods: 3-D tomography • Local tomography • Fourier slice theorem • Fourier Transform of the projections • Inversion • Filtered Backprojection • Filter step • : Hilbert transform • Backprojection step Algorithm [3] • Image : R, ROI: ri, ROE: re=ri+rm+rr, N evenly spaces angles • ROE of each projection is filtered by scaling and wavelet ramp filters at N angles. The complexity is 9/2N re(log re) (using FFT) • . Extrapolate 4 re pixels at N/2angles( Bandwidth is reduced by half after step1. ) The complexity is 3N (4re)(log 4re) (using FFT) • 3. Using backprojection to obtain the wavelet coefficients at resolution 2-1. The remaining points are set to zero. The complexity is (7re/2)(ri+2rr)2(using linear interpolation) • 4. Reconstruct image from the wavelet and scaling coefficients. The complexity of filtering is 4(2ri)2(3rr) Background • Radon transform • Region of interest • Interior Radon Transform The Nonlocality of Radon Transform Results • Hilbert Transform of a compactly supported function can never be compactly supported, because it composes a discontinuity in the derivative of the Fourier transform of any function at the origin. • The imposition of discontinuity at origin in frequency domain will spread the supported functions in time domain, i.e., local basis will not remain local after filtering [3] F. Rashid-Farrokhi, K.J.R. Liu, C. A. Berenstein and D. Walnut: Wavelet-based Multiresolution Local Tomography, IEEE Transactions on Image Processing, 6(1997), pp. 1412-1430. [4]A. C. Kak and Malcolm Slaney, Principles of Computerized Tomographic Imaging, IEEE Press, 1988. Why wavelets? [5] S. Zhao, G. Welland, G. Wang, Wavelet Sampling and Localization Schemes for the Radon Transform in Two Dimensions, 1997 Society for Industrial and Applied Mathematics. • Compactly supported function • Many vanishing moments References [1] T. Olson, J. DeStefano, Wavelet localization of the Radon Transform, IEEE Tr. Signal Proc.42(8): 2055-2067 (1994). [2] C.A. Berenstein, D.F. Walnut, Local inversion Radon transform in even dimensions using wavelets, 75 years of Radon transform (Vienna, 1992), S, Gindikin, P. Michor (eds.), pp. 45-69, International Press, (1994).

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