title: Effects of guided random sampling of TCCs on blood flow values in CT perfusion studies of lung tumors.
abstract: OBJECTIVE
  Tissue perfusion is commonly used to evaluate lung tumor lesions through dynamic contrast-enhanced computed tomography (DCE-CT). The aim of this study was to improve the reliability of the blood flow (BF) maps by means of a guided sampling of the tissue time-concentration curves (TCCs).
  
  
  METHODS
  Fourteen selected CT perfusion (CTp) examinations from different patients with lung lesions were considered, according to different degrees of motion compensation. For each examination, two regions of interest (ROIs) referring to the target lesion and the arterial input were manually segmented. To obtain the perfusion parameters, we computed the maximum slope of the Hill equation, describing the pharmacokinetics of the contrast agent, and the TCC was fitted for each voxel. A guided iterative approach based on the Random Sample Consensus method was used to detect and exclude samples arising from motion artifacts through the assessment of the confidence level of each single temporal sample of the TCC compared to the model. Removing these samples permits to refine the model fitting, thus exploiting more reliable data. Goodness-of-fit measures of the fitted TCCs to the original data (eg, root mean square error and correlation distance) were used to assess the reliability of the BF values, so as to preserve the functional structure of the resulting perfusion map. We devised a quantitative index, the local coefficient of variation (lCV), to measure the spatial coherence of perfusion maps, from local to regional and global resolution. The effectiveness of the algorithm was tested under three different degrees of motion yielded by as many alignment procedures.
  
  
  RESULTS
  At pixel level, the proposed approach improved the reliability of BF values, quantitatively assessed through the correlation index. At ROI level, a comparative analysis emphasized how our approach "replaced" the noisy pixels, providing smoother parametric maps while preserving the main functional structure. Moreover, the implemented algorithm provides a more meaningful effect in correspondence of a higher motion degree. This was confirmed both quantitatively, using the lCV, and qualitatively, through visual inspection by expert radiologists.
  
  
  CONCLUSIONS
  Perfusion maps achieved with the proposed approach can now be used as a valid tool supporting radiologists in DCE-CTp studies. This represents a step forward to clinical utilization of these studies for staging, prognosis, and monitoring values of therapeutic regimens.
authors.affiliation: Advanced Research Center on Electronic Systems for Information and Communication Technologies "Ercole De Castro" (ARCES), University of Bologna, Via Toffano 2/2, Bologna 40125, Italy.
authors.forename: Agostino
authors.initials: A
authors.lastname: Gibaldi
authors.affiliation: Radiology Unit, IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST), Meldola (FC), Italy.
authors.forename: Domenico
authors.initials: D
authors.lastname: Barone
authors.affiliation: Radiology Unit, IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST), Meldola (FC), Italy.
authors.forename: Giampaolo
authors.initials: G
authors.lastname: Gavelli
authors.affiliation: Advanced Research Center on Electronic Systems for Information and Communication Technologies "Ercole De Castro" (ARCES), University of Bologna, Via Toffano 2/2, Bologna 40125, Italy; Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Malavasi
authors.affiliation: Advanced Research Center on Electronic Systems for Information and Communication Technologies "Ercole De Castro" (ARCES), University of Bologna, Via Toffano 2/2, Bologna 40125, Italy; Department of Computer Science and Engineering (DISI), University of Bologna, Bologna, Italy. Electronic address: alessandro.bevilacqua@unibo.it.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Bevilacqua
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000792:Angiography / Q000379:methods; D016477:Artifacts; D001783:Blood Flow Velocity; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007479:Iopamidol / Q000031:analogs & derivatives* / Q000493:pharmacokinetics; D008175:Lung Neoplasms / Q000150:complications / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D008297:Male; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D008954:Models, Biological; D009038:Motion; D009389:Neovascularization, Pathologic / Q000000981:diagnostic imaging* / Q000209:etiology / Q000503:physiopathology*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
chemical_list: D003287:Contrast Media; C057937:iomeprol; D007479:Iopamidol
keywords: CT perfusion; TCC; guided RANSAC; lung tumor; reliability