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 limitation, we developed a fully automated technique for deriving IDIF with a supervised clustering algorithm (SVCA). To validate this technique, 25 healthy controls and 26 patients with moderate to severe major depressive disorder (MDD) underwent T1-weighted brain magnetic resonance imaging (MRI) and a 90-minute [(11)C](R)-rolipram PET scan. For each subject, metabolite-corrected input function was measured from the radial artery. SVCA templates were obtained from 10 additional healthy subjects who underwent the same MRI and PET procedures. Cluster-IDIF was obtained as follows: 1) template mask images were created for carotid and surrounding tissue; 2) parametric image of weights for blood were created using SVCA; 3) mask images to the individual PET image were inversely normalized; 4) carotid and surrounding tissue time activity curves (TACs) were obtained from weighted and unweighted averages of each voxel activity in each mask, respectively; 5) partial volume effects and radiometabolites were corrected using individual arterial data at four points. Logan-distribution volume (V T/f P) values obtained by cluster-IDIF were similar to reference results obtained using arterial data, as well as those obtained using manual-IDIF; 39 of 51 subjects had a V T/f P error of <5%, and only one had error >10%. With automatic voxel selection, cluster-IDIF curves were less noisy than manual-IDIF and free of operator-related variability. Cluster-IDIF showed widespread decrease of about 20% [(11)C](R)-rolipram binding in the MDD group. Taken together, the results suggest that cluster-IDIF is a good alternative to full arterial input function for estimating Logan-V T/f P in [(11)C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.