Impact of view reduction in CT on radiation dose for patients

Abstract Iterative methods have become a hot topic of research in computed tomography (CT) imaging because of their capacity to resolve the reconstruction problem from a limited number of projections. This allows the reduction of radiation exposure on patients during the data acquisition. The reconstruction time and the high radiation dose imposed on patients are the two major drawbacks in CT. To solve them effectively we adapted the method for sparse linear equations and sparse least squares (LSQR) with soft threshold filtering (STF) and the fast iterative shrinkage-thresholding algorithm (FISTA) to computed tomography reconstruction. The feasibility of the proposed methods is demonstrated numerically.

[1]  S. Deans.  The Radon Transform and Some of Its Applications , 1983 .

[2]  Li Zeng,et al.  A Novel Weighted Total Difference Based Image Reconstruction Algorithm for Few-View Computed Tomography , 2014, PloS one.

[3]  Daniel Kolditz,et al.  Iterative reconstruction methods in X-ray CT. , 2012, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[4]  Gene H. Golub,et al.  Calculating the singular values and pseudo-inverse of a matrix , 2007, Milestones in Matrix Computation.

[5]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[6]  Tao Yang,et al.  GPU based iterative cone-beam CT reconstruction using empty space skipping technique. , 2013, Journal of X-ray science and technology.

[7]  Hengyong Yu,et al.  A soft-threshold filtering approach for reconstruction from a limited number of projections , 2010, Physics in medicine and biology.

[8]  P. Mayo,et al.  Parallel CT image reconstruction based on GPUs , 2014 .

[9]  D. L. Donoho,et al.  Compressed sensing , 2006, IEEE Trans. Inf. Theory.

[10]  Gumersindo Verdú,et al.  Dose reduction using non lineal diffusion and smoothing filters in computed radiography , 2014 .

[11]  Gabor T. Herman,et al.  Fundamentals of Computerized Tomography: Image Reconstruction from Projections , 2009, Advances in Pattern Recognition.

[12]  Bruno De Man,et al.  An outlook on x-ray CT research and development. , 2008, Medical physics.

[13]  Michael A. Saunders,et al.  LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares , 1982, TOMS.

[14]  Günter Lauritsch,et al.  Simulation tools for two-dimensional experiments in x-ray computed tomography using the FORBILD head phantom , 2012, Physics in medicine and biology.