title: Wavelet-based calculation of cerebral angiographic data from time-resolved CT perfusion acquisitions.
abstract: OBJECTIVE
  To evaluate a new approach for reconstructing angiographic images by application of wavelet transforms on CT perfusion data.
  
  
  METHODS
  Fifteen consecutive patients with suspected stroke were examined with a multi-detector CT acquiring 32 dynamic phases (∆t = 1.5s) of 99 slices (total slab thickness 99mm) at 80kV/200mAs. Thirty-five mL of iomeprol-350 was injected (flow rate = 4.5mL/s). Angiographic datasets were calculated after initial rigid-body motion correction using (a) temporally filtered maximum intensity projections (tMIP) and (b) the wavelet transform (Paul wavelet, order 1) of each voxel time course. The maximum of the wavelet-power-spectrum was defined as the angiographic signal intensity. The contrast-to-noise ratio (CNR) of 18 different vessel segments was quantified and two blinded readers rated the images qualitatively using 5pt Likert scales.
  
  
  RESULTS
  The CNR for the wavelet angiography (501.8 ± 433.0) was significantly higher than for the tMIP approach (55.7 ± 29.7, Wilcoxon test p < 0.00001). Image quality was rated to be significantly higher (p < 0.001) for the wavelet angiography with median scores of 4/4 (reader 1/reader 2) than the tMIP (scores of 3/3).
  
  
  CONCLUSIONS
  The proposed calculation approach for angiography data using temporal wavelet transforms of intracranial CT perfusion datasets provides higher vascular contrast and intrinsic removal of non-enhancing structures such as bone.
  
  
  CONCLUSIONS
  • Angiographic images calculated with the proposed wavelet-based approach show significantly improved contrast-to-noise ratio. • CT perfusion-based wavelet angiography is an alternative method for vessel visualization. • Provides intrinsic removal of non-enhancing structures such as bone.
authors.affiliation: Josef-Lissner-Laboratory for Biomedical Imaging, Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Marchioninistr. 15, 81377, Munich, Germany, lukas.havla@med.uni-muenchen.de.
authors.forename: Lukas
authors.initials: L
authors.lastname: Havla
authors.forename: Kolja M
authors.initials: KM
authors.lastname: Thierfelder
authors.forename: Sebastian E
authors.initials: SE
authors.lastname: Beyer
authors.forename: Wieland H
authors.initials: WH
authors.lastname: Sommer
authors.forename: Olaf
authors.initials: O
authors.lastname: Dietrich
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002533:Cerebral Angiography / Q000379:methods*; D003287:Contrast Media; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007479:Iopamidol / Q000031:analogs & derivatives; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography / Q000379:methods*; D011856:Radiographic Image Enhancement; D020521:Stroke / Q000000981:diagnostic imaging*; D055815:Young Adult
chemical_list: D003287:Contrast Media; C057937:iomeprol; D007479:Iopamidol