Tomosynthesis via total variation minimization reconstruction and prior image constrained compressed sensing (PICCS) on a C-arm system

Brian Nett; Jie Tang; Shuai Leng; Guang-Hong Chen

doi:10.1117/12.771294

18 March 2008 Tomosynthesis via total variation minimization reconstruction and prior image constrained compressed sensing (PICCS) on a C-arm system

Brian Nett, Jie Tang, Shuai Leng, Guang-Hong Chen

Author Affiliations +

Proceedings Volume 6913, Medical Imaging 2008: Physics of Medical Imaging; 69132D (2008) https://doi.org/10.1117/12.771294
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Recently, foundational mathematical theory, compressed sensing (CS), has been developed which enables accurate reconstruction from greatly undersampled frequency information (Candes et. al. and Donoho). Using numerical phantoms it has been demonstrated that CS reconstruction (e.g. minimizing the ℓ₁ norm of the discrete gradient of the image) offers promise for computed tomography. However, when using experimental CT projection data the undersampling factors enabled were smaller than in numerical simulations. An extension to CS has recently been proposed wherein a prior image is utilized as a constraint in the image reconstruction procedure (i.e. Prior Image Constrained Compressed Sensing - PICCS). Experimental results are demonstrated here from a clinical C-arm system, highlighting one application of PICCS in reducing radiation exposure during interventional procedures while preserving high image quality. In this study a range of view angles has been investigated from very limited angle aquisitions (e.g. tomosythesis) to undersampled CT acquisitions.

Citation Download Citation

Brian Nett, Jie Tang, Shuai Leng, and Guang-Hong Chen "Tomosynthesis via total variation minimization reconstruction and prior image constrained compressed sensing (PICCS) on a C-arm system", Proc. SPIE 6913, Medical Imaging 2008: Physics of Medical Imaging, 69132D (18 March 2008); https://doi.org/10.1117/12.771294

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