Angular oversampling with temporally offset layers on multilayer detectors in computed tomography. Issue 6 (30th November 2016)

• Record Type: Journal Article
• Authors: Sjölin, Martin; Danielsson, Mats
• Is Part Of: Medical physics. Volume 43:Issue 6(2016)
• Page Start: 2877
• Page End: 2883
• Publication Date: 2016-11-30
• Subjects: biomedical equipment -- computerised tomography -- Gaussian processes -- image reconstruction -- medical image processing; Computed tomography; 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; computed tomography -- oversampling -- multilayer detector -- double‐layer -- edge‐on; Computed tomography -- Image reconstruction -- Image sensors -- Multilayers -- Time measurement -- X‐ray detectors -- Medical image artifacts -- Medical image reconstruction -- Silicon detectors; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4948505
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Today's computed tomography (CT) scanners operate at an increasingly high rotation speed in order to reduce motion artifacts and to fulfill the requirements of dynamic acquisition, e.g., perfusion and cardiac imaging, with lower angular sampling rate as a consequence. In this paper, a simple method for obtaining angular oversampling when using multilayer detectors in continuous rotation CT is presented. Methods: By introducing temporal offsets between the measurement periods of the different layers on a multilayer detector, the angular sampling rate can be increased by a factor equal to the number of layers on the detector. The increased angular sampling rate reduces the risk of producing aliasing artifacts in the image. A simulation of a detector with two layers is performed to prove the concept. Results: The simulation study shows that aliasing artifacts from insufficient angular sampling are reduced by the proposed method. Specifically, when imaging a single point blurred by a 2D Gaussian kernel, the method is shown to reduce the strength of the aliasing artifacts by approximately an order of magnitude. Conclusions: The presented oversampling method is easy to implement in today's multilayer detectors and has the potential to reduce aliasing artifacts in the reconstructed images.