Posterior Estimation for Dynamic PET imaging using Conditional Variational Inference
This work addresses a computational bottleneck in medical imaging for researchers and clinicians, offering a more efficient alternative to expensive MCMC methods, though it is incremental as it builds on existing deep learning techniques.
The paper tackles the problem of efficiently estimating posterior distributions of kinetic parameters in dynamic PET imaging, proposing a conditional variational autoencoder (CVAE) framework that achieves efficient inference by introducing latent variables to counteract information loss, validated against unbiased MCMC on low-dimensional data.
This work aims efficiently estimating the posterior distribution of kinetic parameters for dynamic positron emission tomography (PET) imaging given a measurement of time of activity curve. Considering the inherent information loss from parametric imaging to measurement space with the forward kinetic model, the inverse mapping is ambiguous. The conventional (but expensive) solution can be the Markov Chain Monte Carlo (MCMC) sampling, which is known to produce unbiased asymptotical estimation. We propose a deep-learning-based framework for efficient posterior estimation. Specifically, we counteract the information loss in the forward process by introducing latent variables. Then, we use a conditional variational autoencoder (CVAE) and optimize its evidence lower bound. The well-trained decoder is able to infer the posterior with a given measurement and the sampled latent variables following a simple multivariate Gaussian distribution. We validate our CVAE-based method using unbiased MCMC as the reference for low-dimensional data (a single brain region) with the simplified reference tissue model.