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Impact of MRI-based Segmentation Artifacts on Amyloid- and FDG-PET Quantitation.
Curr Alzheimer Res. 2016; 13(5):597-607.CA

Abstract

INTRODUCTION

Magnet resonance image (MRI)-based segmentations are widely used for clinical brain research, especially in conjunction with positron-emission-tomography (PET). Although artifacts due to segmentation errors arise commonly, the impact of these artifacts on PET quantitation has not yet been investigated systematically. Therefore, the aim of this study was to assess the effect of segmentation errors on [(18)F]-AV45 and [(18)F]-FDG PET quantitation, with and without correction for partial volume effects (PVE).

MATERIAL AND METHODS

119 subjects with both [(18)F]-AV45, and [(18)F]-FDG PET as well as T1-weighted MRI at baseline and at two-year follow-up were selected from the ADNI cohort, and their MRI brain images were segmented using PMOD 3.5. MRIs with segmentation artifacts were masked with the corresponding [(18)F]-FDG PET standard-uptake-value (SUV) images to elucidate and quantify the impact of artifacts on PET analyses for six defined volumes-of-interest (VOI). Artifact volumes were calculated for each VOI, together with error-[%] and root-mean-square-errors (RMSE) in uncorrected and PVE corrected SUV results for the two PET tracers. We also assessed the bias in longitudinal PET data.

RESULTS

Artifacts occurred most frequently in the parietal cortex VOI. For [(18)F]-AV45 and [(18)F]-FDG PET, the percentage-errors were dependent on artifact volumes. PVEC SUVs were consequently more distorted than were their uncorrected counterparts. In static and longitudinal assessment, a small subgroup of subjects with large artifacts (≥1500 voxels; ≙5.06 cm³) accounted for much of the PET quantitation bias.

CONCLUSION

Large segmentation artifacts need to be detected and resolved as they considerably bias PET quantitation, especially when PVEC is applied to PET data.

Authors+Show Affiliations

No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableDepartment of Nuclear Medicine, University of Munich, Germany. axel.rominger@med.uni-muenchen.de.No affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

27025775

Citation

Högenauer, Marcus, et al. "Impact of MRI-based Segmentation Artifacts On Amyloid- and FDG-PET Quantitation." Current Alzheimer Research, vol. 13, no. 5, 2016, pp. 597-607.
Högenauer M, Brendel M, Delker A, et al. Impact of MRI-based Segmentation Artifacts on Amyloid- and FDG-PET Quantitation. Curr Alzheimer Res. 2016;13(5):597-607.
Högenauer, M., Brendel, M., Delker, A., Därr, S., Weiss, M., Bartenstein, P., Rominger, A., & Alzheimer's Disease Neuroimaging Initiative, . (2016). Impact of MRI-based Segmentation Artifacts on Amyloid- and FDG-PET Quantitation. Current Alzheimer Research, 13(5), 597-607.
Högenauer M, et al. Impact of MRI-based Segmentation Artifacts On Amyloid- and FDG-PET Quantitation. Curr Alzheimer Res. 2016;13(5):597-607. PubMed PMID: 27025775.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Impact of MRI-based Segmentation Artifacts on Amyloid- and FDG-PET Quantitation. AU - Högenauer,Marcus, AU - Brendel,Matthias, AU - Delker,Andreas, AU - Därr,Sonja, AU - Weiss,Mayo, AU - Bartenstein,Peter, AU - Rominger,Axel, AU - Alzheimer's Disease Neuroimaging Initiative,, PY - 2015/02/05/received PY - 2015/10/10/revised PY - 2015/10/16/accepted PY - 2016/3/31/entrez PY - 2016/3/31/pubmed PY - 2016/12/20/medline SP - 597 EP - 607 JF - Current Alzheimer research JO - Curr Alzheimer Res VL - 13 IS - 5 N2 - INTRODUCTION: Magnet resonance image (MRI)-based segmentations are widely used for clinical brain research, especially in conjunction with positron-emission-tomography (PET). Although artifacts due to segmentation errors arise commonly, the impact of these artifacts on PET quantitation has not yet been investigated systematically. Therefore, the aim of this study was to assess the effect of segmentation errors on [(18)F]-AV45 and [(18)F]-FDG PET quantitation, with and without correction for partial volume effects (PVE). MATERIAL AND METHODS: 119 subjects with both [(18)F]-AV45, and [(18)F]-FDG PET as well as T1-weighted MRI at baseline and at two-year follow-up were selected from the ADNI cohort, and their MRI brain images were segmented using PMOD 3.5. MRIs with segmentation artifacts were masked with the corresponding [(18)F]-FDG PET standard-uptake-value (SUV) images to elucidate and quantify the impact of artifacts on PET analyses for six defined volumes-of-interest (VOI). Artifact volumes were calculated for each VOI, together with error-[%] and root-mean-square-errors (RMSE) in uncorrected and PVE corrected SUV results for the two PET tracers. We also assessed the bias in longitudinal PET data. RESULTS: Artifacts occurred most frequently in the parietal cortex VOI. For [(18)F]-AV45 and [(18)F]-FDG PET, the percentage-errors were dependent on artifact volumes. PVEC SUVs were consequently more distorted than were their uncorrected counterparts. In static and longitudinal assessment, a small subgroup of subjects with large artifacts (≥1500 voxels; ≙5.06 cm³) accounted for much of the PET quantitation bias. CONCLUSION: Large segmentation artifacts need to be detected and resolved as they considerably bias PET quantitation, especially when PVEC is applied to PET data. SN - 1875-5828 UR - https://www.unboundmedicine.com/medline/citation/27025775/Impact_of_MRI_based_Segmentation_Artifacts_on_Amyloid__and_FDG_PET_Quantitation_ L2 - http://www.eurekaselect.com/140730/article DB - PRIME DP - Unbound Medicine ER -