title: Technical analysis of volume-rendering algorithms: application in low-contrast structures using liver vascularisation as a model.
abstract: OBJECTIVE
  To assess the influence of pre-set volume-rendering opacity curves (OC) on image quality and to identify which absolute parameters (density of aorta, hepatic parenchyma and portal vein) affect visualization of portal vascular structures (low-contrast structures).
  
  
  METHODS
  Twenty-two patients underwent a dual-phase spiral CT with the following parameters: collimation 3 mm, pitch 2, increment 1 mm. Three scans were performed: one without contrast medium and the latter two after the injection of contrast material (conventionally identified as ''arterial'' and ''portal''). The images were sent to a workstation running on an NT platform equipped with post-processing software allowing three-dimensional (3D) reconstructions to generate volume-rendered images of the vascular supply to the liver. Correlation between the absolute values of aorta, liver and portal vein density, OC parameters, and image quality were assessed.
  
  
  RESULTS
  3D images generated using pre-set OC obtained a much lower overall quality score than those produced with OC set by the operator. High contrast between the liver and the portal vein, for example during the portal vascular phase, allows wider windows, thus improving image quality. Conversely, the OC in the parenchymal phase scans must have a high gradient in order to better differentiate between the vascular structures and the surrounding hepatic parenchyma.
  
  
  CONCLUSIONS
  Image features considered to be of interest by the operator cannot be simplified by the mean of pre-set OC. Due to their strong individual variability automatic 3D algorithms cannot be universally applied: they should be adapted to both image and patient characteristics.
authors.affiliation: Dipartimento di Radiologia, Erasmus Medical Center, Rotterdam (Olanda). filippocademartiri@hotmail.com
authors.forename: Filippo
authors.initials: F
authors.lastname: Cademartiri
authors.forename: Giacomo
authors.initials: G
authors.lastname: Luccichenti
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Runza
authors.forename: Massimo
authors.initials: M
authors.lastname: Gualerzi
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Brambilla
authors.forename: Paolo
authors.initials: P
authors.lastname: Coruzzi
authors.forename: Paolo
authors.initials: P
authors.lastname: Soliani
authors.forename: Mario
authors.initials: M
authors.lastname: Sianesi
authors.forename: Tommaso Vincenzo
authors.initials: TV
authors.lastname: Bartolotta
authors.forename: Massimo
authors.initials: M
authors.lastname: Midiri
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008099:Liver / Q000098:blood supply* / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D011859:Radiography