2017/08/04 by Purisha, Zenith, Karhula, Sakari S., Ketola, Juuso +5
#FOS: Mathematics #FOS: Physical sciences #Medical Physics (physics.med-ph) #Numerical Analysis (math.NA)
paper · doi:10.48550/arxiv.1708.02067
X-ray tomography is a reliable tool for determining the inner structure of 3D object with penetrating X-rays. However, traditional reconstruction methods such as FDK require dense angular sampling in the data acquisition phase leading to long measurement times, especially in X-ray micro-tomography to obtain high resolution scans. Acquiring less data using greater angular steps is an obvious way for speeding up the process and avoiding the need to save huge data sets available memory. However, computing 3D reconstruction from such a sparsely sampled dataset is very sensitive to measurement noise and modelling errors. An automatic regularization method is proposed for robust reconstruction, based on enforcing sparsity in the three-dimensional shearlet transform domain. The inputs of the algorithm are the projection data and \it a priori known expected degree of sparsity, denoted 0