arXiv subjects

Qinlin Gu

Publications and source records attributed to Qinlin Gu.

3 recordsLinked to original sources

arXiv2026

A General-Purpose Adaptable Foundation Model for Total-Body PET Kinetic Modelling

Voxel-level kinetic modeling in total-body dynamic positron emission tomography (TB-PET) enables precise physiological quantification but complicates parameter uncertainty estimation. While generative consistency models (CMs) can rapidly estimate Bayesian posterior distributions, they are constrained by type of tracers, kinetic models, and protocols. Training them from scratch whenever a new tracer, model or protocol is used can be computationally intensive. To address this, we introduce LoRACM, a parameter-efficient framework utilizing low-rank adaptation (LoRA) to transfer a pretrained foundation CM to novel TB-PET scenarios. We evaluated three adaptation scenarios using simulated and human data: scanner and protocol transfer for FDG two-tissue compartment modeling; FP-CIT simplified reference tissue modeling; and high-temporal-resolution FDG blood flow imaging via the adiabatic approximation to tissue homogeneity model. Each adaptation was trained in less than 3 hours on a single NVIDIA A100 GPU using 50,000 simulated pairs. In simulations, LoRACM showed strong agreement with results from Markov Chain Monte Carlo (MCMC, which we use as reference posterior distributions), yielding median Kullback-Leibler divergences of 0.05-0.11 and median absolute percentage errors below 1.2% for each kinetic parameter of interest. In human data, LoRACM generated total-body K_i maps comparable to the ones made by non-negative nonlinear least squares (NNLS) fitting and a consistency model trained from scratch. Striatal dopamine binding potential estimates predicted by LoRACM correlated with NNLS estimates (r = 0.968-0.988). LoRACM produced total-body blood flow imaging with FDG matching the one made by NNLS. These underscore the potential of adapting a pretrained CM with LoRA for fast and reliable posterior parameter inference for kinetic models across distinct TB-PET applications.

physics.med-ph↗

arXiv2026

vPET-ABC: Fast Voxelwise Approximate Bayesian Inference for Kinetic Modeling in PET

Dynamic PET kinetic modeling increasingly demands voxelwise uncertainty quantification and robust model selection. Yet total-body PET (TB-PET) data volumes make conventional Bayesian approaches, such as per-voxel MCMC, computationally impractical, while deep models typically require retraining and careful revalidation when tracers, protocols, or kinetic models change, without necessarily improving inference speed. Vectorized voxelwise approximate Bayesian computation (vPET-ABC) is introduced as a likelihood-free, model-agnostic posterior inference framework for dynamic PET kinetic modeling at total-body scale. The method replaces explicit likelihood evaluation with forward simulations and a discrepancy test, then exploits full vectorization to transform voxelwise inference into an embarrassingly parallel workload suited to modern GPUs. In simulation, vPET-ABC produced posterior summaries with small divergence from sequential Monte Carlo baselines, and posterior mean estimates significantly more accurate than non-negative least squares (NNLS). For model selection between the linear parametric neurotransmitter model (lp-ntPET) and the multilinear reference tissue model, vPET-ABC maintained high sensitivity under high noise with moderate loss of specificity, whereas NNLS+Bayesian information criteria exhibited the opposite trade-off with near-zero sensitivity. In a human cigarette smoking dataset, vPET-ABC yielded denser probabilistic activation maps than lp-ntPET with effective number of parameters. On a 50 min total-body [18F]FDG study, vPET-ABC generated high quality whole volume K_i parametric images within practical runtimes on a single GPU, while also preserved local spatial correlation better than NNLS. Overall, vPET-ABC delivers fast, training-free, uncertainty-aware inference that scales to TB-PET and remains portable across tracers and kinetic models.

stat.AP↗

arXiv2026

Generative Consistency Models for Estimation of Kinetic Parametric Image Posteriors in Total-Body PET

Dynamic total body positron emission tomography (TB-PET) makes it feasible to measure the kinetics of all organs in the body simultaneously which may lead to important applications in multi-organ disease and systems physiology. Since whole-body kinetics are highly heterogeneous with variable signal-to-noise ratios, parametric images should ideally comprise not only point estimates but also measures of posterior statistical uncertainty. However, standard Bayesian techniques, such as Markov chain Monte Carlo (MCMC), are computationally prohibitive at the total body scale. We introduce a generative consistency model (CM) that generates samples from the posterior distributions of the kinetic model parameters given measured time-activity curves and arterial input function. CM is able to collapse the hundreds of iterations required by standard diffusion models into just 3 denoising steps. When trained on 500,000 physiologically realistic two-tissue compartment model simulations, the CM produces similar accuracy to MCMC (median absolute percent error < 5%; median K-L divergence < 0.5) but is more than five orders of magnitude faster. CM produces more reliable Ki images than the Patlak method by avoiding the assumption of irreversibility, while also offering valuable information on statistical uncertainty of parameter estimates and the underlying model. The proposed framework removes the computational barrier to routine, fully Bayesian parametric imaging in TB-PET and is readily extensible to other tracers and compartment models.

physics.med-ph↗