Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification

L Presotto, Leonardo Iaccarino, A Sala, EG Vanoli, C Muscio, A Nigri, MG Bruzzone, F Tagliavini, L Gianolli, D Perani, V Bettinardi

Research output: Contribution to journalArticle

Abstract

The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N = 34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (18F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global18F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVrCTSUVrMRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVrCTand SUVrMRIglobal uptake scores showed negligible differences (mean ± sd 0.01 ± 0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1 mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine analysis. © 2018
Original languageEnglish
Pages (from-to)153-160
Number of pages8
JournalNeuroImage: Clinical
Volume20
DOIs
Publication statusPublished - 2018

Fingerprint

Amyloid
Positron-Emission Tomography
Tomography
Magnetic Resonance Imaging
Research
Dementia
Clinical Trials
Positron Emission Tomography Computed Tomography
Brain

Cite this

Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification. / Presotto, L; Iaccarino, Leonardo; Sala, A; Vanoli, EG; Muscio, C; Nigri, A; Bruzzone, MG; Tagliavini, F; Gianolli, L; Perani, D; Bettinardi, V.

In: NeuroImage: Clinical, Vol. 20, 2018, p. 153-160.

Research output: Contribution to journalArticle

Presotto, L ; Iaccarino, Leonardo ; Sala, A ; Vanoli, EG ; Muscio, C ; Nigri, A ; Bruzzone, MG ; Tagliavini, F ; Gianolli, L ; Perani, D ; Bettinardi, V. / Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification. In: NeuroImage: Clinical. 2018 ; Vol. 20. pp. 153-160.
@article{4bc9da71fd784228b6220eb5482a5e3f,
title = "Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification",
abstract = "The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N = 34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (18F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global18F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVrCTSUVrMRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVrCTand SUVrMRIglobal uptake scores showed negligible differences (mean ± sd 0.01 ± 0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90{\%} within 1 mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine analysis. {\circledC} 2018",
author = "L Presotto and Leonardo Iaccarino and A Sala and EG Vanoli and C Muscio and A Nigri and MG Bruzzone and F Tagliavini and L Gianolli and D Perani and V Bettinardi",
year = "2018",
doi = "10.1016/j.nicl.2018.07.013",
language = "English",
volume = "20",
pages = "153--160",
journal = "NeuroImage: Clinical",
issn = "2213-1582",
publisher = "ELSEVIER SCI LTD",

}

TY - JOUR

T1 - Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification

AU - Presotto, L

AU - Iaccarino, Leonardo

AU - Sala, A

AU - Vanoli, EG

AU - Muscio, C

AU - Nigri, A

AU - Bruzzone, MG

AU - Tagliavini, F

AU - Gianolli, L

AU - Perani, D

AU - Bettinardi, V

PY - 2018

Y1 - 2018

N2 - The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N = 34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (18F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global18F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVrCTSUVrMRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVrCTand SUVrMRIglobal uptake scores showed negligible differences (mean ± sd 0.01 ± 0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1 mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine analysis. © 2018

AB - The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N = 34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (18F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global18F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVrCTSUVrMRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVrCTand SUVrMRIglobal uptake scores showed negligible differences (mean ± sd 0.01 ± 0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1 mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine analysis. © 2018

U2 - 10.1016/j.nicl.2018.07.013

DO - 10.1016/j.nicl.2018.07.013

M3 - Article

VL - 20

SP - 153

EP - 160

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

SN - 2213-1582

ER -