ArticleEJNMMI physics2023
An automatic pipeline for PET/MRI attenuation correction validation in the brain.
Article in EJNMMI physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).EJNMMI physics · 2026Article
- A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease.NPJ digital medicine · 2026Article
- Assessment of lesion insertion tool in pelvis PET/MR data with applications to attenuation correction method development.Journal of applied clinical medical physics · 2024Article
- FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool.Physics in medicine and biology · 2024Article
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Abstract
purposeChallenges 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.
methodsOur 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.
resultsThe 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.
conclusionThe 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.
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