An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.
Biomed Phys Eng Express
; 10(5)2024 Aug 19.
Article
in En
| MEDLINE
| ID: mdl-39094595
ABSTRACT
Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET scans. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain
/
Fluorodeoxyglucose F18
/
Positron-Emission Tomography
/
Deep Learning
Limits:
Female
/
Humans
/
Male
Language:
En
Journal:
Biomed Phys Eng Express
Year:
2024
Document type:
Article
Affiliation country:
United States
Country of publication:
United kingdom