Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2021-06, Vol.233, p.117928-117928, Article 117928
• Main Authors: Sudarshan, Viswanath P., Li, Shenpeng, Jamadar, Sharna D., Egan, Gary F., Awate, Suyash P., Chen, Zhaolin
• Format: Article
• Language: English
• Subjects: Brain mapping; Brain metabolism; Denoising; Dynamic FDG-PET; fPET; functional PET; Glucose; Glucose metabolism; ICA; Magnetic resonance imaging; Metabolic response; Metabolism; Methods; MRI-PET; Neuroimaging; Partial volume error; Positron emission tomography; Temporal resolution; Visual stimuli
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.117928

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.