Effects of ray profile modeling on resolution recovery in clinical CT

• Published in: Medical physics (Lancaster) 2014-02, Vol.41 (2), p.021907-n/a
• Main Authors: Hofmann, Christian, Knaup, Michael, Kachelrieß, Marc
• Format: Article
• Language: English
• Subjects: ALGORITHMS; APPROXIMATIONS; Artifacts and distortion; Biological material, e.g. blood, urine; Haemocytometers; cardiology; clinical‐CT; Computed tomography; Computerised tomographs; computerised tomography; COMPUTERIZED TOMOGRAPHY; Digital computing or data processing equipment or methods, specially adapted for specific applications; GEOMETRY; HEART; Humans; Image data processing or generation, in general; image denoising; Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; IMAGE PROCESSING; Image Processing, Computer-Assisted - methods; image reconstruction; image resolution; image sampling; Image sensors; ITERATIVE METHODS; iterative reconstruction; maximum likelihood estimation; MAXIMUM-LIKELIHOOD FIT; Medical image contrast; Medical image noise; medical image processing; Medical image reconstruction; Medical imaging; Models, Theoretical; Noise; Numerical approximation and analysis; Particle beam detectors; PHANTOMS; Phantoms, Imaging; Radiography, Thoracic; RADIOLOGY AND NUCLEAR MEDICINE; ray‐modeling; Reconstruction; REFLECTIVE COATINGS; resolution recovery; set theory; SIMULATION; SPATIAL RESOLUTION; Tomography, X-Ray Computed - methods; X‐ray detectors
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4862510

Purpose: Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise (and thereby patient dose), to reduce artifacts, or to improve spatial resolution. However, among vendors and researchers, there is no consensus of how to best achieve these goals. The authors are focusing on the aspect of geometric ray profile modeling, which is realized by some algorithms, while others model the ray as a straight line. The authors incorporate ray-modeling (RM) in nonregularized iterative reconstruction. That means, instead of using one simple single needle beam to represent the x-ray, the authors evaluate the double integral of attenuation path length over the finite source distribution and the finite detector element size in the numerical forward projection. Our investigations aim at analyzing the resolution recovery (RR) effects of RM. Resolution recovery means that frequencies can be recovered beyond the resolution limit of the imaging system. In order to evaluate, whether clinical CT images can benefit from modeling the geometrical properties of each x-ray, the authors performed a 2D simulation study of a clinical CT fan-beam geometry that includes the precise modeling of these geometrical properties. Methods: All simulations and reconstructions are performed in native fan-beam geometry. A water phantom with resolution bar patterns and a Forbild thorax phantom with circular resolution patterns representing calcifications in the heart region are simulated. An FBP reconstruction with a Ram–Lak kernel is used as a reference reconstruction. The FBP is compared to iterative reconstruction techniques with and without RM: An ordered subsets convex (OSC) algorithm without any RM (OSC), an OSC where the forward projection is modeled concerning the finite focal spot and detector size (OSC-RM) and an OSC with RM and with a matched forward and backprojection pair (OSC-T-RM, T for transpose). In all cases, noise was matched to be able to focus on comparing spatial resolution. The authors use two different simulation settings. Both are based on the geometry of a typical clinical CT system (0.7 mm detector element size at isocenter, 1024 projections per rotation). Setting one has an exaggerated source width of 5.0 mm. Setting two has a realistically small source width of 0.5 mm. The authors also investigate the transition from setting one to two. To quantify image quality, the authors analyze line profiles through the resol