title: Image-derived input function with factor analysis and a-priori information.
abstract: BACKGROUND
  Quantitative PET studies often require the cumbersome and invasive procedure of arterial cannulation to measure the input function. This study sought to minimize the number of necessary blood samples by developing a factor-analysis-based image-derived input function (IDIF) methodology for dynamic PET brain studies.
  
  
  METHODS
  IDIF estimation was performed as follows: (a) carotid and background regions were segmented manually on an early PET time frame; (b) blood-weighted and tissue-weighted time-activity curves (TACs) were extracted with factor analysis; (c) factor analysis results were denoised and scaled using the voxels with the highest blood signal; (d) using population data and one blood sample at 40 min, whole-blood TAC was estimated from postprocessed factor analysis results; and (e) the parent concentration was finally estimated by correcting the whole-blood curve with measured radiometabolite concentrations. The methodology was tested using data from 10 healthy individuals imaged with [(11)C](R)-rolipram. The accuracy of IDIFs was assessed against full arterial sampling by comparing the area under the curve of the input functions and by calculating the total distribution volume (VT).
  
  
  RESULTS
  The shape of the image-derived whole-blood TAC matched the reference arterial curves well, and the whole-blood area under the curves were accurately estimated (mean error 1.0±4.3%). The relative Logan-V(T) error was -4.1±6.4%. Compartmental modeling and spectral analysis gave less accurate V(T) results compared with Logan.
  
  
  CONCLUSIONS
  A factor-analysis-based IDIF for [(11)C](R)-rolipram brain PET studies that relies on a single blood sample and population data can be used for accurate quantification of Logan-V(T) values.
authors.affiliation: aJožef Stefan Institute, Ljubljana bThe Centre of Excellence for Biosensors, Instrumentation and Process Control - COBIK, Ajdovščina, Slovenia cUniversity of Bordeaux, CNRS, INCIA, UMR 5287, Talence, France dMolecular Imaging Branch, National Institute of Mental Health, National Institute of Health, Bethesda, Maryland, USA.
authors.forename: Urban
authors.initials: U
authors.lastname: Simončič
authors.forename: Paolo
authors.initials: P
authors.lastname: Zanotti-Fregonara
mesh_terms: D019540:Area Under Curve; D001158:Arteries / Q000000981:diagnostic imaging; D001921:Brain / Q000098:blood supply / Q000000981:diagnostic imaging; D002250:Carbon Radioisotopes; D005163:Factor Analysis, Statistical; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007700:Kinetics; D049268:Positron-Emission Tomography; D020889:Rolipram
chemical_list: D002250:Carbon Radioisotopes; D020889:Rolipram