Exact image reconstruction from a limited number of projections

A new method for the exact reconstruction of any gray-scale image from its projections is proposed. The original image is projected into several view angles and the projection samples are stored in an accumulator array. In order to reconstruct the image, the accumulator array is considered as an accumulation of sinusoidal contributions each one corresponding to a certain pixel of the original image. The proposed method defines conditions for the necessary number of projections and the density of ray samples on the projection axis. These conditions insure that, for each pixel, there is at least one sample in the accumulator array where only this particular pixel contributes. This characteristic projection sample is used during the reconstruction phase to determine the coordinates and the gray-scale value of the corresponding image pixel. A variation of the method is also proposed where the reconstruction is performed using a limited number of projection samples in certain view angles. Specifically, the number of necessary samples equals at most the overall number of pixels in the original image. This approach leads to a significant reduction of memory and processing time requirements since it provides exact image reconstruction using one projection sample per pixel.

[1]  P. Lauterbur,et al.  Principles of magnetic resonance imaging : a signal processing perspective , 1999 .

[2]  Laurence Wolsey,et al.  Strong formulations for mixed integer programming: A survey , 1989, Math. Program..

[3]  Donald Ludwig,et al.  The radon transform on euclidean space , 2010 .

[4]  Jorge Llacer,et al.  Matrix-Based Image Reconstruction Methods for Tomography , 1985, IEEE Transactions on Nuclear Science.

[5]  Peter W. Michor,et al.  75 years of Radon transform : proceedings of the conference held at the Erwin Schrödinger International Institute for Mathematical Physics in Vienna, August 31-September 4, 1992 , 1994 .

[6]  M. Sezan,et al.  Tomographic Image Reconstruction from Incomplete View Data by Convex Projections and Direct Fourier Inversion , 1984, IEEE Transactions on Medical Imaging.

[7]  R. M. Mersereau,et al.  Digital reconstruction of multidimensional signals from their projections , 1974 .

[8]  Nikos Papamarkos,et al.  Block decomposition and segmentation for fast Hough transform evaluation , 1999, Pattern Recognit..

[9]  P. V. Remoortere Linear and combinatorial programming : K.G. Murty: 1976,J. Wiley, New York, 310 pp , 1979 .

[10]  Avinash C. Kak,et al.  Principles of computerized tomographic imaging , 2001, Classics in applied mathematics.

[11]  Nikos Papamarkos,et al.  On the gray-scale inverse Hough transform , 2000, Image Vis. Comput..

[12]  G T Herman,et al.  Performance evaluation of an iterative image reconstruction algorithm for positron emission tomography. , 1991, IEEE transactions on medical imaging.

[13]  B. F. Logan,et al.  The Fourier reconstruction of a head section , 1974 .

[14]  H. Barrett,et al.  Computerized tomography: taking sectional x rays , 1977 .

[15]  Nikos Papamarkos,et al.  On the Inverse Hough Transform , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  J. M. Ollinger,et al.  Positron Emission Tomography , 2018, Handbook of Small Animal Imaging.

[17]  Stanley R. Deans,et al.  Hough Transform from the Radon Transform , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.