Friedrich-Alexander-Universität Erlangen-Nürnberg

CT reconstruction using compressed sensing

CT Reconstruction Using Compressed Sensing

Computed tomography (CT) is considered as a radiation-intensive procedure, yet it is widely used in
clinical practice. The risk of radiation induced disease makes a strong need to reconstruct CT images with practically useful quality using as low radiation as possible. One strategy is to use limited number of projections. However, the under sampling and noise in the measurements (e.g. X-ray scattering) make the reconstruction problem ill-posed. The reconstruction algorithm used by existing products, which is called FBP, can not reconstruct a satisfactory image (Fig. 1). Recently, Candes, Romberg and Tao proposed a new mathematical theory, compressed sensing (CS), which can perform nearly exact image reconstruction with only few measurements. The aim of the project is to apply CS in CT reconstruction to reduce the radiation dose as much as possible while reserve the image quality. 

Fig. 1 Reconstructed result. The left shows the original image and the right is the image reconstructed by FBP which is widely used by the existing products.


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