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Performing X-ray computed tomography (CT) examinations with less radiationhas recently received increasing interest: in medical imaging this means less(potentially harmful) radiation for the patient; in non-destructive testing ofmaterials/objects such as testing jet engines, the redution of the number ofprojection angles (which for large objects is in general high) leads to asubstantial decreasing of the experiment time. In the experiment, lessradiation is usually achieved by either (1) reducing the radiation dose used ateach projection angle or (2) using sparse view X-ray CT, which meanssignificantly less projection angles are used during the examination. In thiswork, we study the performance of the recently proposed sinogram-basediterative reconstruction algorithm in sparse view X-ray CT and show that itprovides, in some cases, reconstruction accuracy better than that obtained bysome of the Total Variation regularization techniques. The provided accuracy isobtained with computation times comparable to other techniques. An importantfeature of the sinogram-based iterative reconstruction algorithm is that it hasno parameters to be set.

Comparison of Sinogram-based Iterative Reconstruction with Compressed Sensing Techniques in X-ray CT