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Anatomically guided voxel-based partial volume effect correction in brain PET: impact of MRI segmentation.
Comput Med Imaging Graph. 2012 Dec; 36(8):610-9.CM

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.

Authors+Show Affiliations

Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23046730

Citation

Gutierrez, Daniel, et al. "Anatomically Guided Voxel-based Partial Volume Effect Correction in Brain PET: Impact of MRI Segmentation." Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society, vol. 36, no. 8, 2012, pp. 610-9.
Gutierrez D, Montandon ML, Assal F, et al. Anatomically guided voxel-based partial volume effect correction in brain PET: impact of MRI segmentation. Comput Med Imaging Graph. 2012;36(8):610-9.
Gutierrez, D., Montandon, M. L., Assal, F., Allaoua, M., Ratib, O., Lövblad, K. O., & Zaidi, H. (2012). Anatomically guided voxel-based partial volume effect correction in brain PET: impact of MRI segmentation. Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society, 36(8), 610-9. https://doi.org/10.1016/j.compmedimag.2012.09.001
Gutierrez D, et al. Anatomically Guided Voxel-based Partial Volume Effect Correction in Brain PET: Impact of MRI Segmentation. Comput Med Imaging Graph. 2012;36(8):610-9. PubMed PMID: 23046730.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Anatomically guided voxel-based partial volume effect correction in brain PET: impact of MRI segmentation. AU - Gutierrez,Daniel, AU - Montandon,Marie-Louise, AU - Assal,Frédéric, AU - Allaoua,Mohamed, AU - Ratib,Osman, AU - Lövblad,Karl-Olof, AU - Zaidi,Habib, Y1 - 2012/10/06/ PY - 2012/05/11/received PY - 2012/08/07/revised PY - 2012/09/07/accepted PY - 2012/10/11/entrez PY - 2012/10/11/pubmed PY - 2013/4/30/medline SP - 610 EP - 9 JF - Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society JO - Comput Med Imaging Graph VL - 36 IS - 8 N2 - 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. SN - 1879-0771 UR - https://www.unboundmedicine.com/medline/citation/23046730/Anatomically_guided_voxel_based_partial_volume_effect_correction_in_brain_PET:_impact_of_MRI_segmentation_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0895-6111(12)00154-1 DB - PRIME DP - Unbound Medicine ER -