Multigrid iterative method with adaptive spatial support for computed tomography reconstruction from few-view data

Ping-Chang Lee

doi:10.1117/12.2043214

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19 March 2014 Multigrid iterative method with adaptive spatial support for computed tomography reconstruction from few-view data

Ping-Chang Lee

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Proceedings Volume 9033, Medical Imaging 2014: Physics of Medical Imaging; 90333C (2014) https://doi.org/10.1117/12.2043214
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Computed tomography (CT) plays a key role in modern medical system, whether it be for diagnosis or therapy. As an increased risk of cancer development is associated with exposure to radiation, reducing radiation exposure in CT becomes an essential issue. Based on the compressive sensing (CS) theory, iterative based method with total variation (TV) minimization is proven to be a powerful framework for few-view tomographic image reconstruction. Multigrid method is an iterative method for solving both linear and nonlinear systems, especially when the system contains a huge number of components. In medical imaging, image background is often defined by zero intensity, thus attaining spatial support of the image, which is helpful for iterative reconstruction. In the proposed method, the image support is not considered as a priori knowledge. Rather, it evolves during the reconstruction process. Based on the CS framework, we proposed a multigrid method with adaptive spatial support constraint. The simultaneous algebraic reconstruction (SART) with TV minimization is implemented for comparison purpose. The numerical result shows: 1. Multigrid method has better performance while less than 60 views of projection data were used, 2. Spatial support highly improves the CS reconstruction, and 3. When few views of projection data were measured, our method performs better than the SART+TV method with spatial support constraint.

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Ping-Chang Lee "Multigrid iterative method with adaptive spatial support for computed tomography reconstruction from few-view data", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90333C (19 March 2014); https://doi.org/10.1117/12.2043214

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