TY - JOUR A1 - Wieczorek, Matthias A1 - Frikel, Jürgen A1 - Vogel, Jakob A1 - Eggl, Elena A1 - Kopp, Felix A1 - Noël, Peter B. A1 - Pfeiffer, Franz A1 - Demaret, Laurent A1 - Lasser, Tobias T1 - X-ray computed tomography using curvelet sparse regularization JF - Medical physics N2 - PURPOSE Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. METHODS In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. RESULTS Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. CONCLUSIONS The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features. Y1 - 2015 U6 - https://doi.org/10.1118/1.4914368 SN - 0094-2405 SN - 2473-4209 VL - 42 IS - 4 SP - 1555 EP - 1565 PB - American Association of Physicists in Medicine ER -