title: Improved delineation of arteries in the posterior fossa of the brain by model-based iterative reconstruction in volume-rendered 3D CT angiography.
abstract: OBJECTIVE
  Improved CTA delineation of arteries and unruptured aneurysms is clinically desired in the posterior fossa. We present a novel model-based iterative reconstruction that models system statistics and optics to improve CT image quality. We investigated the utility of MBIR for improving delineation of arteries in the posterior fossa on 3D brain CTA.
  
  
  METHODS
  Using filtered back-projection with a standard kernel and MBIR, we reconstructed axial images of 0.625-mm thickness of 28 consecutive patients (14 men; mean age, 58.6 ± 14.6 years) who underwent 64-detector brain CTA. We placed regions of interest on the axial images, measured the mean CT value in the basilar artery and the value and SD in the pons and bilateral cerebellar hemispheres, and calculated the contrast-to-noise ratio of the brain arteries in the posterior fossa. Using volume-rendered CTA and a 4-point scale, 2 radiologists independently graded delineation of the BA, bilateral vertebral artery, superior cerebellar artery, and anterior and posterior inferior cerebellar arteries. We compared the results between FBP and MBIR by using paired t and Wilcoxon signed-rank tests.
  
  
  RESULTS
  Compared with FBP, MBIR significantly improved the contrast-to-noise ratio (P < .0001) and subjective delineation of all arteries in the posterior fossa except the BA (VA, SCA, AICA, and PICA; P < .05 for all). The mean visual score by MBIR was 3.0 or higher for all those arteries except the AICA assessed by reader 1 (2.6 ± 0.7).
  
  
  CONCLUSIONS
  With 3D brain CTA, contrast-to-noise ratio and arterial delineation of the VA, SCA, AICA, and PICA in the posterior fossa are better with MBIR than FBP.
authors.affiliation: Department of Radiology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan. machira@dnh.twmu.ac.jp
authors.forename: H
authors.initials: H
authors.lastname: Machida
authors.forename: H
authors.initials: H
authors.lastname: Takeuchi
authors.forename: I
authors.initials: I
authors.lastname: Tanaka
authors.forename: R
authors.initials: R
authors.lastname: Fukui
authors.forename: Y
authors.initials: Y
authors.lastname: Shen
authors.forename: E
authors.initials: E
authors.lastname: Ueno
authors.forename: S
authors.initials: S
authors.lastname: Suzuki
authors.forename: X-Z
authors.initials: XZ
authors.lastname: Lin
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002533:Cerebral Angiography / Q000379:methods*; D002536:Cerebral Arteries / Q000000981:diagnostic imaging*; D002561:Cerebrovascular Disorders / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*