title: Time-resolved computed tomography of the liver: retrospective, multi-phase image reconstruction derived from volumetric perfusion imaging.
abstract: OBJECTIVE
  To assess feasibility and image quality (IQ) of a new post-processing algorithm for retrospective extraction of an optimised multi-phase CT (time-resolved CT) of the liver from volumetric perfusion imaging.
  
  
  METHODS
  Sixteen patients underwent clinically indicated perfusion CT using 4D spiral mode of dual-source 128-slice CT. Three image sets were reconstructed: motion-corrected and noise-reduced (MCNR) images derived from 4D raw data; maximum and average intensity projections (time MIP/AVG) of the arterial/portal/portal-venous phases and all phases (total MIP/ AVG) derived from retrospective fusion of dedicated MCNR split series. Two readers assessed the IQ, detection rate and evaluation time; one reader assessed image noise and lesion-to-liver contrast.
  
  
  RESULTS
  Time-resolved CT was feasible in all patients. Each post-processing step yielded a significant reduction of image noise and evaluation time, maintaining lesion-to-liver contrast. Time MIPs/AVGs showed the highest overall IQ without relevant motion artefacts and best depiction of arterial and portal/portal-venous phases respectively. Time MIPs demonstrated a significantly higher detection rate for arterialised liver lesions than total MIPs/AVGs and the raw data series.
  
  
  CONCLUSIONS
  Time-resolved CT allows data from volumetric perfusion imaging to be condensed into an optimised multi-phase liver CT, yielding a superior IQ and higher detection rate for arterialised liver lesions than the raw data series.
  
  
  CONCLUSIONS
  • Four-dimensional computed tomography is limited by motion artefacts and poor image quality. • Time-resolved-CT facilitates 4D-CT data visualisation, segmentation and analysis by condensing raw data. • Time-resolved CT demonstrates better image quality than raw data images. • Time-resolved CT improves detection of arterialised liver lesions in cirrhotic patients.
authors.affiliation: Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, 14186, Stockholm, Sweden, michael.fischer@ki.se.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Fischer
authors.forename: Bertil
authors.initials: B
authors.lastname: Leidner
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Kartalis
authors.forename: Anders
authors.initials: A
authors.lastname: Svensson
authors.forename: Peter
authors.initials: P
authors.lastname: Aspelin
authors.forename: Nils
authors.initials: N
authors.lastname: Albiin
authors.forename: Torkel B
authors.initials: TB
authors.lastname: Brismar
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016477:Artifacts; D006528:Carcinoma, Hepatocellular / Q000000981:diagnostic imaging*; D005240:Feasibility Studies; D005260:Female; D005500:Follow-Up Studies; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008099:Liver / Q000000981:diagnostic imaging*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D036542:Tomography, Spiral Computed / Q000379:methods*