BACKGROUND: Image-derived input function (IDIF) from carotid arteries is an elegant alternative to full arterial blood sampling for brain PET studies. However, a recent study using blood-free IDIFs found that this method is particularly vulnerable to patient motion. The present study used both simulated and clinical [11C](R)-rolipram data to assess the robustness of a blood-based IDIF method (a method that is ultimately normalized with blood samples) with regard to motion artifacts. METHODS: The impact of motion on the accuracy of IDIF was first assessed with an analytical simulation of a high-resolution research tomograph using a numerical phantom of the human brain, equipped with internal carotids. Different degrees of translational (from 1 to 20 mm) and rotational (from 1 to 15°) motions were tested. The impact of motion was then tested on the high-resolution research tomograph dynamic scans of three healthy volunteers, reconstructed with and without an online motion correction system. IDIFs and Logan-distribution volume (VT) values derived from simulated and clinical scans with motion were compared with those obtained from the scans with motion correction. RESULTS: In the phantom scans, the difference in the area under the curve (AUC) for the carotid time-activity curves was up to 19% for rotations and up to 66% for translations compared with the motionless simulation. However, for the final IDIFs, which were fitted to blood samples, the AUC difference was 11% for rotations and 8% for translations. Logan-VT errors were always less than 10%, except for the maximum translation of 20 mm, in which the error was 18%. Errors in the clinical scans without motion correction appeared to be minor, with differences in AUC and Logan-VT always less than 10% compared with scans with motion correction. CONCLUSION: When a blood-based IDIF method is used for neurological PET studies, the motion of the patient affects IDIF estimation and kinetic modeling only minimally.
BACKGROUND: Image-derived input function (IDIF) from carotid arteries is an elegant alternative to full arterial blood sampling for brain PET studies. However, a recent study using blood-free IDIFs found that this method is particularly vulnerable to <span class="Species">patient motion. The present study used both simulated and clinical [11C](R)-rolipram data to assess the robustness of a blood-based IDIF method (a method that is ultimately normalized with blood samples) with regard to motion artifacts. METHODS: The impact of motion on the accuracy of IDIF was first assessed with an analytical simulation of a high-resolution research tomograph using a numerical phantom of the human brain, equipped with internal carotids. Different degrees of translational (from 1 to 20 mm) and rotational (from 1 to 15°) motions were tested. The impact of motion was then tested on the high-resolution research tomograph dynamic scans of three healthy volunteers, reconstructed with and without an online motion correction system. IDIFs and Logan-distribution volume (VT) values derived from simulated and clinical scans with motion were compared with those obtained from the scans with motion correction. RESULTS: In the phantom scans, the difference in the area under the curve (AUC) for the carotid time-activity curves was up to 19% for rotations and up to 66% for translations compared with the motionless simulation. However, for the final IDIFs, which were fitted to blood samples, the AUC difference was 11% for rotations and 8% for translations. Logan-VT errors were always less than 10%, except for the maximum translation of 20 mm, in which the error was 18%. Errors in the clinical scans without motion correction appeared to be minor, with differences in AUC and Logan-VT always less than 10% compared with scans with motion correction. CONCLUSION: When a blood-based IDIF method is used for neurological PET studies, the motion of the patient affects IDIF estimation and kinetic modeling only minimally.
Authors: Sandra M Sanabria-Bohórquez; Alex Maes; Patrick Dupont; Guy Bormans; Tjibbe de Groot; Alexandre Coimbra; WaiSi Eng; Tine Laethem; Inge De Lepeleire; Jay Gambale; Jose M Vega; H Donald Burns Journal: Mol Imaging Biol Date: 2003 Mar-Apr Impact factor: 3.488
Authors: Masahiro Fujita; Sami S Zoghbi; Matthew S Crescenzo; Jinsoo Hong; John L Musachio; Jian-Qiang Lu; Jeih-San Liow; Nicholas Seneca; Dnyanesh N Tipre; Vanessa L Cropley; Masao Imaizumi; Antony D Gee; Jurgen Seidel; Michael V Green; Victor W Pike; Robert B Innis Journal: Neuroimage Date: 2005-07-15 Impact factor: 6.556
Authors: Jurgen E M Mourik; Mark Lubberink; Ursula M H Klumpers; Emile F Comans; Adriaan A Lammertsma; Ronald Boellaard Journal: Neuroimage Date: 2007-11-28 Impact factor: 6.556
Authors: K Chen; D Bandy; E Reiman; S C Huang; M Lawson; D Feng; L S Yun; A Palant Journal: J Cereb Blood Flow Metab Date: 1998-07 Impact factor: 6.200
Authors: Sami S Zoghbi; H Umesha Shetty; Masanori Ichise; Masahiro Fujita; Masao Imaizumi; Jeih-San Liow; Jay Shah; John L Musachio; Victor W Pike; Robert B Innis Journal: J Nucl Med Date: 2006-03 Impact factor: 10.057
Authors: Paolo Zanotti-Fregonara; Christina S Hines; Sami S Zoghbi; Jeih-San Liow; Yi Zhang; Victor W Pike; Wayne C Drevets; Alan G Mallinger; Carlos A Zarate; Masahiro Fujita; Robert B Innis Journal: Neuroimage Date: 2012-08-10 Impact factor: 6.556
Authors: Chul Hyoung Lyoo; Paolo Zanotti-Fregonara; Sami S Zoghbi; Jeih-San Liow; Rong Xu; Victor W Pike; Carlos A Zarate; Masahiro Fujita; Robert B Innis Journal: PLoS One Date: 2014-02-20 Impact factor: 3.240