Iterative Computed Tomography Reconstruction from Sparse-View Data

Computed tomography plays a crucial role in medical imaging, such as in diagnosis and therapy. The radiation from computed tomography will result in excess radiation exposure to patients, hence the reduction of radiation is essential. In computed tomography, there are many occasions where reconstruction has to be performed with sparse-view data. In sparse-view computed tomography, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing algorithm has shown potential to accurately recover images from the highly under sampled data. In the recent past, total variation based compressed sensing algorithms have been proposed to suppress the streak artifact in computed tomography image reconstruction. In this paper, we propose an efficient algorithm for computed tomography image reconstruction from sparse-view data where we simultaneously minimize parameters: the simultaneous algebraic reconstruction techniques and nonlocal total variation. The main feature of our algorithm is the use of nonlocal total variation for streak artifact removal. Experiments have been conducted using simulated phantoms and clinical data to evaluate the performance of the proposed algorithm. The numerical results show much smaller streak artifacts and reconstruction errors than other conventional methods.