Literature DB >> 23046730

Anatomically guided voxel-based partial volume effect correction in brain PET: impact of MRI segmentation.

Daniel Gutierrez1, Marie-Louise Montandon, Frédéric Assal, Mohamed Allaoua, Osman Ratib, Karl-Olof Lövblad, Habib Zaidi.   

Abstract

Partial volume effect is still considered one of the main limitations in brain PET imaging given the limited spatial resolution of current generation PET scanners. The accuracy of anatomically guided partial volume effect correction (PVC) algorithms in brain PET is largely dependent on the performance of MRI segmentation algorithms partitioning the brain into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of four brain MRI segmentation algorithms bundled in the successive releases of Statistical Parametric Mapping (SPM) package (SPM99, SPM2, SPM5, SPM8) using clinical neurological examinations was performed. Subsequently, their impact on PVC in (18)F-FDG brain PET imaging was assessed. The principle of the different variants of the image segmentation algorithm is to spatially normalize the subject's MR images to a corresponding template. PET images were corrected for partial volume effect using GM volume segmented from coregistered MR images. The PVC approach aims to compensate for signal dilution in non-active tissues such as CSF, which becomes an important issue in the case of tissue atrophy to prevent a misinterpretation of decrease of metabolism owing to PVE. The study population consisted of 19 patients suffering from neurodegenerative dementia. Image segmentation performed using SPM5 was used as reference. The comparison showed that previous releases of SPM (SPM99 and SPM2) result in larger gray matter regions (~20%) and smaller white matter regions (between -17% and -6%), thus introducing non-negligible bias in PVC PET activity estimates (between 30% and 90%). In contrary, the more recent release (SPM8) results in similar results (<1%). It was concluded that the choice of the segmentation algorithm for MRI-guided PVC in PET plays a crucial role for the accurate estimation of PET activity concentration. The segmentation algorithm embedded within the latest release of SPM satisfies the requirement of robust and accurate segmentation for MRI-guided PVC in brain PET imaging. Published by Elsevier Ltd.

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Year:  2012        PMID: 23046730     DOI: 10.1016/j.compmedimag.2012.09.001

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  6 in total

1.  Image coregistration: quantitative processing framework for the assessment of brain lesions.

Authors:  Hannu Huhdanpaa; Darryl H Hwang; Gregory G Gasparian; Michael T Booker; Yong Cen; Alexander Lerner; Orest B Boyko; John L Go; Paul E Kim; Anandh Rajamohan; Meng Law; Mark S Shiroishi
Journal:  J Digit Imaging       Date:  2014-06       Impact factor: 4.056

2.  Whole-brain circuit dissection in free-moving animals reveals cell-specific mesocorticolimbic networks.

Authors:  Michael Michaelides; Sarah Ann R Anderson; Mala Ananth; Denis Smirnov; Panayotis K Thanos; John F Neumaier; Gene-Jack Wang; Nora D Volkow; Yasmin L Hurd
Journal:  J Clin Invest       Date:  2013-11-15       Impact factor: 14.808

3.  Healthy brain ageing assessed with 18F-FDG PET and age-dependent recovery factors after partial volume effect correction.

Authors:  Stijn Bonte; Pieter Vandemaele; Stijn Verleden; Kurt Audenaert; Karel Deblaere; Ingeborg Goethals; Roel Van Holen
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-11-23       Impact factor: 9.236

4.  Nicotinic Acetylcholine Receptor Density in the "Higher-Order" Thalamus Projecting to the Prefrontal Cortex in Humans: a PET Study.

Authors:  Valentina Garibotto; Michael Wissmeyer; Zoi Giavri; Osman Ratib; Fabienne Picard
Journal:  Mol Imaging Biol       Date:  2020-04       Impact factor: 3.488

5.  Feasibility of simultaneous whole-brain imaging on an integrated PET-MRI system using an enhanced 2-point Dixon attenuation correction method.

Authors:  Udunna C Anazodo; Jonathan D Thiessen; Tracy Ssali; Jonathan Mandel; Matthias Günther; John Butler; William Pavlosky; Frank S Prato; R Terry Thompson; Keith S St Lawrence
Journal:  Front Neurosci       Date:  2015-01-05       Impact factor: 4.677

Review 6.  MRI-Driven PET Image Optimization for Neurological Applications.

Authors:  Yuankai Zhu; Xiaohua Zhu
Journal:  Front Neurosci       Date:  2019-07-31       Impact factor: 4.677

  6 in total

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