title: Ultra-low dose lung CT perfusion regularized by a previous scan.
abstract: OBJECTIVE
  A previous scan-regularized reconstruction (PSRR) method was proposed to reduce radiation dose and applied to lung perfusion studies. Normal and ultra-low-dose lung computed tomographic perfusion studies were compared in terms of the estimation accuracy of pulmonary functional parameters.
  
  
  METHODS
  A sequence of sheep lung scans were performed in three prone, anesthetized sheep at normal and ultra-low doses. A scan protocol was developed for the ultra-low-dose studies with electrocardiographic gating: time point 1 for a normal x-ray dose scan (100 kV, 150 mAs) and time points 2 to 21 for low-dose scans (80 kV, 17 mAs). A nonlinear diffusion-based post-filtering method was applied to the difference images between the low-dose images and the high-quality reference image. The final images at 20 time points were generated by fusing the reference image with the filtered difference images.
  
  
  RESULTS
  The power spectra of perfusion images and coherences in the normal scans showed a great improvement in image quality of the ultra-low-dose scans with PSRR relative to those without RSRR. The gamma variate fitting and the repeatability of the measurements of the mean transit time demonstrated that the key parameters of lung functions can be reliably accessed using PSRR. The variability of the ultra-low-dose scan results obtained using PSRR was not substantially different from that between two normal-dose scans.
  
  
  CONCLUSIONS
  This study demonstrates that an approximate 90% reduction in radiation dose is achievable using PSRR without compromising quantitative computed tomographic measurements of regional lung function.
authors.affiliation: Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Science, Virginia Tech, 1880 Pratt Drive, Blacksburg, VA 24061, USA. hengyongyu@vt.edu
authors.forename: Hengyong
authors.initials: H
authors.lastname: Yu
authors.forename: Shiying
authors.initials: S
authors.lastname: Zhao
authors.forename: Eric A
authors.initials: EA
authors.lastname: Hoffman
authors.forename: Ge
authors.initials: G
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001822:Body Burden; D003287:Contrast Media; D007472:Iohexol; D008168:Lung / Q000000981:diagnostic imaging*; D011829:Radiation Dosage; D011832:Radiation Injuries / Q000209:etiology / Q000517:prevention & control; D011835:Radiation Protection / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012756:Sheep; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000009:adverse effects / Q000379:methods*
chemical_list: D003287:Contrast Media; D007472:Iohexol