Effects of sparse sampling schemes on image quality in low‐dose CT. Issue 11 (21st October 2013)

• Record Type: Journal Article
• Authors: Abbas, Sajid; Lee, Taewon; Shin, Sukyoung; Lee, Rena; Cho, Seungryong
• Is Part Of: Medical physics. Volume 40:Issue 11(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-10-21
• Subjects: Computed tomography -- Dosimetry/exposure assessment -- Reconstruction; compressed sensing -- computerised tomography -- dosimetry -- image reconstruction -- image sampling -- medical image processing; computed tomography (CT) -- compressive sensing (CS) -- incoherence -- sampling density -- low‐dose; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Methods or arrangements for processing data by operating upon the order or content of the data handled -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Scintigraphy; Medical imaging -- Medical image reconstruction -- Image reconstruction -- Computed tomography -- Medical image quality -- Collimators -- Dosimetry -- Data analysis -- Image analysis -- Image guided radiation therapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4825096
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Various scanning methods and image reconstruction algorithms are actively investigated for low‐dose computed tomography (CT) that can potentially reduce a health‐risk related to radiation dose. Particularly, compressive‐sensing (CS) based algorithms have been successfully developed for reconstructing images from sparsely sampled data. Although these algorithms have shown promises in low‐dose CT, it has not been studied how sparse sampling schemes affect image quality in CS‐based image reconstruction. In this work, the authors present several sparse‐sampling schemes for low‐dose CT, quantitatively analyze their data property, and compare effects of the sampling schemes on the image quality. Methods: : Data properties of several sampling schemes are analyzed with respect to the CS‐based image reconstruction using two measures: sampling density and data incoherence. The authors present five different sparse sampling schemes, and simulated those schemes to achieve a targeted dose reduction. Dose reduction factors of about 75% and 87.5%, compared to a conventional scan, were tested. A fully sampled circular cone‐beam CT data set was used as a reference, and sparse sampling has been realized numerically based on the CBCT data. Results: : It is found that both sampling density and data incoherence affect the image quality in the CS‐based reconstruction. Among the sampling schemes the authors investigated, the sparse‐view, many‐view undersampling (MVUS)‐fine, and MVUS‐moving cases have shown promising results. These sampling schemes produced images with similar image quality compared to the reference image and their structure similarity index values were higher than 0.92 in the mouse head scan with 75% dose reduction. Conclusions: : The authors found that in CS‐based image reconstructions both sampling density and data incoherence affect the image quality, and suggest that a sampling scheme should be devised and optimized by use of these indicators. With this strategic approach, one can acquire optimally sampled sparse data so that the CS‐based algorithms can best perform in terms of image quality.