Statistical noise reduction with projection space multiscale decomposition and penalized weighted least square

Shaojie Tang; Yi Yang; Xiangyang Tang

doi:10.1117/12.911122

3 March 2012 Statistical noise reduction with projection space multiscale decomposition and penalized weighted least square

Shaojie Tang, Yi Yang, Xiangyang Tang

Author Affiliations +

Proceedings Volume 8313, Medical Imaging 2012: Physics of Medical Imaging; 83133G (2012) https://doi.org/10.1117/12.911122
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

A statistical noise reduction approach is proposed for x-ray computed tomography imaging. The isotropic diffusion partial differential equation is derived from image space to projection space in the equi-angular fan-beam geometry and then used to obtain a projection space multi-scale decomposition by iteratively approximating a Gaussian convolution kernel function with an expected variance. Subsequently, the penalized weighted least square methods with three different objective functions are developed and implemented to reduce quantum noise in the projection data. Experimental results of computer simulated projection data have demonstrated the performance of the proposed approach.

Citation Download Citation

Shaojie Tang, Yi Yang, and Xiangyang Tang "Statistical noise reduction with projection space multiscale decomposition and penalized weighted least square", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83133G (3 March 2012); https://doi.org/10.1117/12.911122

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