Preliminary Assessment of Reference Region Quantification and Reduced Scanning Times for [¹⁸F]SynVesT-1 PET in Parkinson’s Disease

Abstract

Synaptic density in the central nervous system can be measured in vivo using PET with [18F]SynVesT-1. While [18F]SynVesT-1 has been proven to be a powerful radiopharmaceutical for PET imaging of neurodegenerative disorders such as Parkinson’s disease (PD), its currently validated acquisition and quantification protocols are invasive and technically challenging in these populations due to the arterial sampling and relatively long scanning times. The objectives of this work were to evaluate a noninvasive (reference tissue) quantification method for [18F]SynVesT-1 in PD patients and to determine the minimum scan time necessary for accurate quantification. [18F]SynVesT-1 PET scans were acquired in 5 patients with PD and 3 healthy control subjects for 120 min with arterial blood sampling. Quantification was performed using the one-tissue compartment model (1TCM) with arterial input function, as well as with the simplified reference tissue model (SRTM) to estimate binding potential ( B P ND ) using centrum semiovale (CS) as a reference region. The SRTM2 method was used with k 2 ′ fixed to either a sample average value (0.037 min-1) or a value estimated first through coupled fitting across regions for each participant. Direct SRTM estimation and the Logan reference region graphical method were also evaluated. There were no significant group differences in CS volume, radiotracer uptake, or efflux ( p s > 0.47 ). Each fitting method produced B P ND estimates in close agreement with those derived from the 1TCM (subject R 2 s > 0.98 , bias < 10 % ), with no difference in bias between the control and PD groups. With SRTM2, B P ND estimates from truncated scan data as short as 80 min produced values in excellent agreement with the data from the full 120 min scans ( bias < 6 % ). While these are preliminary results from a small sample of patients with PD ( n = 5 ), this work suggests that accurate synaptic density quantification may be performed without blood sampling and with scan time under 90 minutes. If further validated, these simplified procedures for [18F]SynVesT-1 PET quantification can facilitate its application as a clinical research imaging technology and allow for larger study samples and include a broader scope of patients including those with neurodegenerative diseases.

PET imaging of synaptic density in Parkinsonian disorders

“…As previously validated for HC (Naganawa et al, 2022), the simplified reference tissue model 2 (SRTM2) coupled fit is a good estimate thanks to a greater difference between k 2 values in CS and target regions. Within PD cohort, non‐displaceable binding potential (BP ND ) calculated via the SRTM2 has shown strong similarities with 1TCM (Smart et al, 2023). Most importantly, preliminary data on healthy controls, suggest that the initial 2 h‐acquisition could be reduced to 60 min to obtain stable V T with SynVesT‐1 (Naganawa et al, 2021).…”

“…Most importantly, preliminary data on healthy controls, suggest that the initial 2 h‐acquisition could be reduced to 60 min to obtain stable V T with SynVesT‐1 (Naganawa et al, 2021). In addition, Smart et al (2023) demonstrated that SV2A quantification via SynVesT‐1 in a cohort of PD patients can be acquired without arterial line and via a shortened scan of 80 min rather than 2 h. This will improve the comfort of participants from a patient population and simplify data collection for future studies using 18 F‐SynVesT‐1) (Smart, Uribe, et al, 2023).…”

Challenges and innovations in brain PET analysis of neurodegenerative disorders: a mini-review on partial volume effects, small brain region studies, and reference region selection

“…Arterial sampling has been used for kinetic modeling in many studies involving several tracers, including PE2I ( Jucaite et al, 2006 ), FEOBV ( Petrou et al, 2014 ), DTBZ ( Chan et al, 1999 ), and others. However, the use of arterial sampling to obtain an AIF is invasive, and can be particularly challenging for patients ( Smart et al, 2023 ). Data driven approaches present a promising path forward for non-invasive quantification methods.…”