ArticleEJNMMI research2025
Non-invasive arterial input function estimation using an MRA atlas and machine learning.
Article in EJNMMI research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Image-derived input functions for [18F]LW223 and [18F]SynVesT-1 PET in the rodent determined using an autoencoder (IDIF-AE).Physics in medicine and biology · 2026Article
- Automated extraction of the arterial input function from brain images for parametric PET studies.EJNMMI research · 2024Article
- ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages.EJNMMI research · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundQuantifying biological parameters of interest through dynamic positron emission tomography (PET) requires an arterial input function (AIF) conventionally obtained from arterial blood samples. The AIF can also be non-invasively estimated from blood pools in PET images, often identified using co-registered MRI images. Deploying methods without blood sampling or the use of MRI generally requires total body PET systems with a long axial field-of-view (LAFOV) that includes a large cardiovascular blood pool. However, the number of such systems in clinical use is currently much smaller than that of short axial field-of-view (SAFOV) scanners. We propose a data-driven approach for AIF estimation for SAFOV PET scanners, which is non-invasive and does not require MRI or blood sampling using brain PET scans. The proposed method was validated using dynamic
resultsThe estimated AIF using brain PET images (IDIF-Brain) was compared to that obtained using data from the descending aorta of the heart (IDIF-DA). Kinetic rate constants (K
conclusionThis study introduces a new approach for AIF estimation in dynamic PET using brain PET images, a probabilistic vascular atlas, and machine learning techniques. The findings demonstrate the feasibility of non-invasive and subject-specific AIF estimation for SAFOV scanners.
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