title: Interior micro-CT with an offset detector.
abstract: OBJECTIVE
  The size of field-of-view (FOV) of a microcomputed tomography (CT) system can be increased by offsetting the detector. The increased FOV is beneficial in many applications. All prior investigations, however, have been focused to the case in which the increased FOV after offset-detector acquisition can cover the transaxial extent of an object fully. Here, the authors studied a new problem where the FOV of a micro-CT system, although increased after offset-detector acquisition, still covers an interior region-of-interest (ROI) within the object.
  
  
  METHODS
  An interior-ROI-oriented micro-CT scan with an offset detector poses a difficult reconstruction problem, which is caused by both detector offset and projection truncation. Using the projection completion techniques, the authors first extended three previous reconstruction methods from offset-detector micro-CT to offset-detector interior micro-CT. The authors then proposed a novel method which combines two of the extended methods using a frequency split technique. The authors tested the four methods with phantom simulations at 9.4%, 18.8%, 28.2%, and 37.6% detector offset. The authors also applied these methods to physical phantom datasets acquired at the same amounts of detector offset from a customized micro-CT system.
  
  
  RESULTS
  When the detector offset was small, all reconstruction methods showed good image quality. At large detector offset, the three extended methods gave either visible shading artifacts or high deviation of pixel value, while the authors' proposed method demonstrated no visible artifacts and minimal deviation of pixel value in both the numerical simulations and physical experiments.
  
  
  CONCLUSIONS
  For an interior micro-CT with an offset detector, the three extended reconstruction methods can perform well at a small detector offset but show strong artifacts at a large detector offset. When the detector offset is large, the authors' proposed reconstruction method can outperform the three extended reconstruction methods by suppressing artifacts and maintaining pixel values.
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061.
authors.forename: Kriti
authors.initials: K
authors.lastname: Sen Sharma
authors.affiliation: VT-WFU School of Biomedical Engingeering and Sciences, Virginia Tech, Blacksburg, Virginia 24061.
authors.forename: Hao
authors.initials: H
authors.lastname: Gong
authors.affiliation: Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061.
authors.forename: Omid
authors.initials: O
authors.lastname: Ghasemalizadeh
authors.affiliation: VT-WFU School of Biomedical Engineering and Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina 27157.
authors.forename: Hengyong
authors.initials: H
authors.lastname: Yu
authors.affiliation: Biomedical Imaging Center/Cluster CBIS/BME, Rensselaer Polytechnic Institute, Troy, New York 12180.
authors.forename: Ge
authors.initials: G
authors.lastname: Wang
authors.affiliation: VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia 24061.
authors.forename: Guohua
authors.initials: G
authors.lastname: Cao
mesh_terms: D016477:Artifacts; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D019047:Phantoms, Imaging; D055114:X-Ray Microtomography / Q000295:instrumentation* / Q000379:methods*