title: Effects of sparse sampling schemes on image quality in low-dose CT.
abstract: OBJECTIVE
  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.
authors: Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, South Korea.
authors: Sajid
authors: S
authors: Abbas
authors: Taewon
authors: T
authors: Lee
authors: Sukyoung
authors: S
authors: Shin
authors: Rena
authors: R
authors: Lee
authors: Seungryong
authors: S
authors: Cho
mesh_terms: D000465:Algorithms; D000818:Animals; D054893:Cone-Beam Computed Tomography; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D055114:X-Ray Microtomography