Dose reduction for synaptic density PET imaging in Parkinson's disease.

OBJECTIVE

Dynamic PET imaging with 11C-UCB-J enables in vivo quantification of synaptic vesicle glycoprotein 2A (SV2A), with prior reports of lower synaptic density in areas such as the brainstem nuclei and substantia nigra (SN) in Parkinson's disease (PD). Lowering PET dose reduces radiation exposure but increases noise and compromises quantification. This study evaluated a self-supervised two-step deep image prior (TS-DIP) denoising method for SV2A PET using 1/10 of the standard dose.

METHODS

Thirty healthy controls (HCs) and 30 PD patients underwent 60-minute PET scans to acquire full-count list-mode data, later down-sampled into ten independent 1/10-count dynamic datasets. TS-DIP was applied to denoise reduced-count frame images, and binding potential (BPND) maps were estimated. Performance was assessed by comparing group differences and correlations with motor severity against full-count results.

RESULTS

Full-count data showed significant lower BPND in SN (-39%, p = 0.003) and red nucleus (RN; -27%, p = 0.009) in PDs versus HCs. With 1/10-count data, SN differences remained significant, but RN differences were inconsistent. TS-DIP introduced minimal bias, restored statistical significance across all noise realizations, and improved recovery of correlations with motor scores (SN: r = -0.43 ± 0.02; RN: r = -0.42 ± 0.04) compared with those from unprocessed 1/10-count data.

CONCLUSIONS

Dynamic SV2A PET imaging at substantially reduced doses is feasible when combined with advanced DL-based denoising techniques such as TS-DIP, supporting its potential for broader clinical application.