Literature DB >> 18042494

Partial volume corrected image derived input functions for dynamic PET brain studies: methodology and validation for [11C]flumazenil.

Jurgen E M Mourik1, Mark Lubberink, Ursula M H Klumpers, Emile F Comans, Adriaan A Lammertsma, Ronald Boellaard.   

Abstract

Extraction of arterial input functions from dynamic brain scans may obviate the need for arterial sampling and would increase the clinical applicability of quantitative PET studies. The aim of the present study was to evaluate applicability and accuracy of image derived input functions (IDIFs) following reconstruction based partial volume correction (PVC). Settings for the PVC ordered subset expectation maximization (PVC-OSEM) reconstruction algorithm were varied. In addition, different methods for defining arterial regions of interest (ROI) in order to extract IDIFs were evaluated. [(11)C]flumazenil data of 10 subjects were used in the present study. Results obtained with IDIFs were compared with those using standard on-line measured arterial input functions. These included areas under the curve (AUC) for peak (1-2 min) and tail (2-60 min), volume of distribution (V(T)) obtained using Logan analysis, and V(T) and K(1) obtained with a basis function implementation of a single tissue compartment model. Best results were obtained with PVC-OSEM using 4 iterations and 16 subsets. Based on (11)C point source measurements, a 4.5 mm FWHM (full width at half maximum) resolution kernel was used to correct for partial volume effects. A ROI consisting of the four hottest pixels per plane (over the carotid arteries) was the best method to extract IDIFs. Excellent peak AUC ratios (0.99+/-0.09) between IDIF and blood sampler input function (BSIF) were found. Furthermore, extracted IDIFs provided V(T) and K(1) values that were very similar to those obtained using BSIFs. The proposed method appears to be suitable for analysing [(11)C]flumazenil data without the need for online arterial sampling.

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Year:  2007        PMID: 18042494     DOI: 10.1016/j.neuroimage.2007.10.022

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  39 in total

Review 1.  Determination of the Input Function at the Entry of the Tissue of Interest and Its Impact on PET Kinetic Modeling Parameters.

Authors:  M'hamed Bentourkia
Journal:  Mol Imaging Biol       Date:  2015-12       Impact factor: 3.488

2.  Kinetic analysis in human brain of [11C](R)-rolipram, a positron emission tomographic radioligand to image phosphodiesterase 4: a retest study and use of an image-derived input function.

Authors:  Paolo Zanotti-Fregonara; Sami S Zoghbi; Jeih-San Liow; Elise Luong; Ronald Boellaard; Robert L Gladding; Victor W Pike; Robert B Innis; Masahiro Fujita
Journal:  Neuroimage       Date:  2010-10-26       Impact factor: 6.556

3.  Image-derived input functions for PET brain studies.

Authors:  Jurgen E M Mourik; Mark Lubberink; Alie Schuitemaker; Nelleke Tolboom; Bart N M van Berckel; Adriaan A Lammertsma; Ronald Boellaard
Journal:  Eur J Nucl Med Mol Imaging       Date:  2008-11-22       Impact factor: 9.236

Review 4.  Image-derived input function for brain PET studies: many challenges and few opportunities.

Authors:  Paolo Zanotti-Fregonara; Kewei Chen; Jeih-San Liow; Masahiro Fujita; Robert B Innis
Journal:  J Cereb Blood Flow Metab       Date:  2011-08-03       Impact factor: 6.200

5.  Image-derived input function estimation on a TOF-enabled PET/MR for cerebral blood flow mapping.

Authors:  Mohammad Mehdi Khalighi; Timothy W Deller; Audrey Peiwen Fan; Praveen K Gulaka; Bin Shen; Prachi Singh; Jun-Hyung Park; Frederick T Chin; Greg Zaharchuk
Journal:  J Cereb Blood Flow Metab       Date:  2017-02-03       Impact factor: 6.200

6.  Noninvasive estimation of the arterial input function in positron emission tomography imaging of cerebral blood flow.

Authors:  Yi Su; Ana M Arbelaez; Tammie L S Benzinger; Abraham Z Snyder; Andrei G Vlassenko; Mark A Mintun; Marcus E Raichle
Journal:  J Cereb Blood Flow Metab       Date:  2012-10-17       Impact factor: 6.200

7.  Structural and practical identifiability of dual-input kinetic modeling in dynamic PET of liver inflammation.

Authors:  Yang Zuo; Souvik Sarkar; Michael T Corwin; Kristin Olson; Ramsey D Badawi; Guobao Wang
Journal:  Phys Med Biol       Date:  2019-09-05       Impact factor: 3.609

8.  Population-based input function and image-derived input function for [¹¹C](R)-rolipram PET imaging: methodology, validation and application to the study of major depressive disorder.

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

9.  Partial volume correction strategies for quantitative FDG PET in oncology.

Authors:  Nikie J Hoetjes; Floris H P van Velden; Otto S Hoekstra; Corneline J Hoekstra; Nanda C Krak; Adriaan A Lammertsma; Ronald Boellaard
Journal:  Eur J Nucl Med Mol Imaging       Date:  2010-04-27       Impact factor: 9.236

10.  Cerebral blood flow with [15O]water PET studies using an image-derived input function and MR-defined carotid centerlines.

Authors:  Edward K Fung; Richard E Carson
Journal:  Phys Med Biol       Date:  2013-02-27       Impact factor: 3.609

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