Image-Derived Input Function Derived from a Supervised Clustering Algorithm: Methodology and Validation in a Clinical Protocol Using [11C](R)-Rolipram

• Published in: PloS one Vol. 9; no. 2; p. e89101
• Main Authors: Lyoo, Chul Hyoung, Zanotti-Fregonara, Paolo, Zoghbi, Sami S., Liow, Jeih-San, Xu, Rong, Pike, Victor W., Zarate, Carlos A., Fujita, Masahiro, Innis, Robert B.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.02.2014; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Arteries; Automation; Biology; Brain; Brain research; Carbon Radioisotopes; Carotid arteries; Carotid Arteries - diagnostic imaging; Carotid Arteries - physiopathology; Carotid artery; Case-Control Studies; Cluster Analysis; Clustering; Clusters; Computer Science; Depressive Disorder, Major - blood; Depressive Disorder, Major - diagnostic imaging; Depressive Disorder, Major - metabolism; Depressive Disorder, Major - physiopathology; Female; Humans; Image processing; Image Processing, Computer-Assisted - methods; Kinetics; Magnetic resonance; Magnetic resonance imaging; Male; Mathematics; Medical imaging; Medicine; Mental depression; Mental health; Metabolites; Methods; Neuroimaging; Plasma; Positron emission; Positron emission tomography; Radioactive tracers; Rolipram; Rolipram - metabolism; Studies; Tomography; Variability; Veins & arteries; United States > US; Maryland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0089101

Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [(11)C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter- and intra-operator variability. To overcome this limitati...