A Survey on Different Types of CT Image Reconstruction

Francy P F, Beena M V

Compressive sampling, half-threshold filtering, discrete gradient transform, pseudo-inverse transform.

Image renovation in CT is a mathematical process that creates images from X-ray projection data gain at many different angles around the patient. Image rebuilding has a basic impact on image worth and therefore on radiation dose. Many techniques have been used to reconstruct the image and the commonly used algorithms are L1 and L1/2. L1 regularization algorithm has been normally used to solve the sparsity constrained problems. To enhance the sparsity constraint for better imaging performance, a promising route is to use the lp norm (0 < p < 1) and solve the lp minimization problem. ½ has been used widely as a replace with for p. In this paper survey the various methods in reconstruction of CT images are discussed.

10.22161/ijaers/3.10.35