An automatic pipeline for PET/MRI attenuation correction validation in the brain

Mahdjoub Hamdi; Chunwei Ying; Hongyu An; Richard Laforest

doi:10.1186/s40658-023-00590-3

An automatic pipeline for PET/MRI attenuation correction validation in the brain

EJNMMI Phys. 2023 Nov 14;10(1):71. doi: 10.1186/s40658-023-00590-3.

Authors

Mahdjoub Hamdi¹, Chunwei Ying², Hongyu An^{2

3

4

5}, Richard Laforest²

Affiliations

¹ Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA. hamdi.m@wustl.edu.
² Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
³ Neurology, Washington University in St. Louis, St. Louis, MO, USA.
⁴ Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
⁵ Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA.

Abstract

Purpose: Challenges in PET/MRI quantitative accuracy for neurological uses arise from PET attenuation correction accuracy. We proposed and evaluated an automatic pipeline to assess the quantitative accuracy of four MRI-derived PET AC methods using analytically simulated PET brain lesions and ROIs as ground truth for PET activity.

Methods: Our proposed pipeline, integrating a synthetic lesion insertion tool and the FreeSurfer neuroimaging framework, inserts simulated spherical and brain ROIs into PET projection space, reconstructing them via four PET MRAC techniques. Utilizing an 11-patient brain PET dataset, we compared the quantitative accuracy of four MRACs (DIXON, DIXONbone, UTE AC, and DL-DIXON) against the gold standard PET CTAC, evaluating MRAC to CTAC activity bias in spherical lesions and brain ROIs with and without background activity against original (lesion free) PET reconstructed images.

Results: The proposed pipeline yielded accurate results for spherical lesions and brain ROIs, adhering to the MRAC to CTAC pattern of original brain PET images. Among the MRAC methods, DIXON AC exhibited the highest bias, followed by UTE, DIXONBone, and DL-DIXON showing the least. DIXON, DIXONbone, UTE, and DL-DIXON showed MRAC to CTAC biases of - 5.41%, - 1.85%, - 2.74%, and 0.08% respectively for ROIs inserted in background activity; - 7.02%, - 2.46%, - 3.56%, and - 0.05% for lesion ROIs without background; and - 6.82%, - 2.08%, - 2.29%, and 0.22% for the original brain PET images' 16 FreeSurfer brain ROIs.

Conclusion: The proposed pipeline delivers accurate results for synthetic spherical lesions and brain ROIs, with and without background activity consideration, enabling the evaluation of new attenuation correction approaches without utilizing measured PET emission data. Additionally, it offers a consistent method to generate realistic lesion ROIs, potentially applicable in assessing further PET correction techniques.

Keywords: FreeSurfer brain atlas; PET attenuation correction; PET/CT; PET/MRI; Quantitative brain PET; Virtual synthetic PET imaging.

Abstract

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