Effects of ray profile modeling on resolution recovery in clinical CT. Issue 2 (23rd January 2014)

• Record Type: Journal Article
• Authors: Hofmann, Christian; Knaup, Michael; Kachelrieß, Marc
• Is Part Of: Medical physics. Volume 41:Issue 2(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-01-23
• Subjects: Computed tomography -- Numerical approximation and analysis -- Reconstruction -- Artifacts and distortion -- Noise -- Spatial resolution; cardiology -- computerised tomography -- image denoising -- image reconstruction -- image resolution -- image sampling -- iterative methods -- maximum likelihood estimation -- medical image processing -- phantoms -- set theory; ray‐modeling -- resolution recovery -- iterative reconstruction -- clinical‐CT; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; Computed tomography -- Image reconstruction -- Medical imaging -- Medical image reconstruction -- X‐ray detectors -- Medical image noise -- Image sensors -- Particle beam detectors -- Spatial resolution -- Medical image contrast; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4862510
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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 resolution patterns to define a contrast factor (CF) for contrast‐resolution plots, and the authors compare the normalized cross‐correlation (NCC) with respect to the ground truth of the circular resolution patterns. To independently analyze whether RM is of advantage, the authors implemented several iterative reconstruction algorithms: The statistical iterative reconstruction algorithm OSC, the ordered subsets simultaneous algebraic reconstruction technique (OSSART) and another statistical iterative reconstruction algorithm, denoted with ordered subsets maximum likelihood (OSML) algorithm. All algorithms were implemented both without RM (denoted as OSC, OSSART, and OSML) and with RM (denoted as OSC‐RM, OSSART‐RM, and OSML‐RM). Results: : For the unrealistic case of a 5.0 mm focal spot the CF can be improved by a factor of two due to RM: the 4.2 LP/cm bar pattern, which is the first bar pattern that cannot be resolved without RM, can be easily resolved with RM. For the realistic case of a 0.5 mm focus, all results show approximately the same CF. The NCC shows no significant dependency on RM when the source width is smaller than 2.0 mm (as in clinical CT). From 2.0 mm to 5.0 mm focal spot size increasing improvements can be observed with RM. Conclusions: : Geometric RM in iterative reconstruction helps improving spatial resolution, if the ray cross‐section is significantly larger than the ray sampling distance. In clinical CT, however, the ray is not much thicker than the distance between neighboring ray centers, as the focal spot size is small and detector crosstalk is negligible, due to reflective coatings between detector elements. Therefore, RM appears not to be necessary in clinical CT to achieve resolution recovery.