Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks

Abstract

Objective. Reducing dose in positron emission tomography (PET) imaging increases noise in reconstructed dynamic frames, which inevitably results in higher noise and possible bias in subsequently estimated images of kinetic parameters than those estimated in the standard dose case. We report the development of a spatiotemporal denoising technique for reduced-count dynamic frames through integrating a cascade artificial neural network (ANN) with the highly constrained back-projection (HYPR) scheme to improve low-dose parametric imaging. Approach. We implemented and assessed the proposed method using imaging data acquired with 11C-UCB-J, a PET radioligand bound to synaptic vesicle glycoprotein 2A (SV2A) in the human brain. The patch-based ANN was trained with a reduced-count frame and its full-count correspondence of a subject and was used in cascade to process dynamic frames of other subjects to further take advantage of its denoising capability. The HYPR strategy was then applied to the spatial ANN processed image frames to make use of the temporal information from the entire dynamic scan. Main results. In all the testing subjects including healthy volunteers and Parkinson’s disease patients, the proposed method reduced more noise while introducing minimal bias in dynamic frames and the resulting parametric images, as compared with conventional denoising methods. Significance. Achieving 80% noise reduction with a bias of −2% in dynamic frames, which translates into 75% and 70% of noise reduction in the tracer uptake (bias, −2%) and distribution volume (bias, −5%) images, the proposed ANN+HYPR technique demonstrates the denoising capability equivalent to a 11-fold dose increase for dynamic SV2A PET imaging with 11C-UCB-J.