Noise Properties of Chord-Image Reconstruction

Recently, there has been much progress in algorithm development for image reconstruction in cone-beam computed tomography (CT). Current algorithms, including the chord-based algorithms, now accept minimal data sets for obtaining images on volume regions-of-interest (ROIs) thereby potentially allowing for reduction of X-ray dose in diagnostic CT. As these developments are relatively new, little effort has been directed at investigating the response of the resulting algorithm implementations to physical factors such as data noise. In this paper, we perform an investigation on the noise properties of ROI images reconstructed by using chord-based algorithms for different scanning configurations. We find that, for the cases under study, the chord-based algorithms yield images with comparable quality. Additionally, it is observed that, in many situations, large data sets contain extraneous data that may not reduce the ROI-image variances.

[1]  Xiaochuan Pan,et al.  Image reconstruction in peripheral and central regions-of-interest and data redundancy. , 2005, Medical physics.

[2]  Xiaochuan Pan,et al.  Exact image reconstruction on PI-lines from minimum data in helical cone-beam CT. , 2004, Physics in medicine and biology.

[3]  Michael Grass,et al.  The n-PI-method for helical cone-beam CT , 2000, IEEE Transactions on Medical Imaging.

[4]  Ge Wang,et al.  Filtered backprojection formula for exact image reconstruction from cone-beam data along a general scanning curve. , 2004, Medical physics.

[5]  R. Proksa,et al.  A quasiexact reconstruction algorithm for helical CT using a 3-Pi acquisition. , 2003, Medical physics.

[6]  Rolf Clackdoyle,et al.  Cone-beam reconstruction using the backprojection of locally filtered projections , 2005, IEEE Transactions on Medical Imaging.

[7]  Hiroyuki Kudo,et al.  Image reconstruction from fan-beam projections on less than a short scan , 2002, Physics in medicine and biology.

[8]  Xiaochuan Pan,et al.  A unified analysis of FBP-based algorithms in helical cone-beam and circular cone- and fan-beam scans. , 2004, Physics in medicine and biology.

[9]  Guang-Hong Chen.  An alternative derivation of Katsevich's cone-beam reconstruction formula. , 2003, Medical physics.

[10]  Xiaochuan Pan,et al.  Image reconstruction with shift-variant filtration and its implication for noise and resolution properties in fan-beam computed tomography. , 2003, Medical physics.

[11]  F. Noo,et al.  A two-step Hilbert transform method for 2D image reconstruction. , 2004, Physics in medicine and biology.

[12]  E. Sidky,et al.  Minimum data image reconstruction algorithms with shift-invariant filtering for helical, cone-beam CT , 2005, Physics in medicine and biology.

[13]  S. Leng,et al.  Fan-beam and cone-beam image reconstruction via filtering the backprojection image of differentiated projection data , 2004, Physics in medicine and biology.

[14]  Hengyong Yu,et al.  A unified framework for exact cone-beam reconstruction formulas. , 2005, Medical physics.

[15]  F. Noo,et al.  Cone-beam reconstruction using 1D filtering along the projection of M-lines , 2005 .

[16]  M. Defrise,et al.  A solution to the long-object problem in helical cone-beam tomography. , 2000, Physics in medicine and biology.

[17]  A. Katsevich.  Analysis of an exact inversion algorithm for spiral cone-beam CT. , 2002, Physics in medicine and biology.

[18]  Xiaochuan Pan,et al.  Image reconstruction on PI-lines by use of filtered backprojection in helical cone-beam CT. , 2004, Physics in medicine and biology.

[19]  Xiaochuan Pan,et al.  An extended data function and its generalized backprojection for image reconstruction in helical cone-beam CT. , 2004, Physics in medicine and biology.

[20]  Xiaochuan Pan,et al.  Theory and algorithms for image reconstruction on chords and within regions of interest. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.