Dynamic total body positron emission tomography (TB-PET) makes it feasible to measure the kinetics of the tracer in all organs of 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. The CM was evaluated using physiologically realistic simulations and an application to a subject’s dynamic [18F]FDG TB-PET dataset analyzed with a standard single-input two-tissue compartment model. When trained on 500,000 physiologically realistic two-tissue compartment model simulations, the CM produces similar accuracy to MCMC (median absolute percent errorKiimages 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.

With the rapid deployment of large-scale Internet constellations, the frequency co-sharing between non-geostationary satellite orbit (NGSO) constellation systems has led to increasingly prominent potential interference issues. Reliable and effective quantitative spectrum compatibility analysis results are essential for coexistence on the same frequency. Due to the randomness of the ground station distribution of NGSO constellation systems, which influences interference analysis, this paper investigates the probability distribution of the satellite sub-point of low Earth orbit (LEO) satellites. By replacing the traditional time-domain extrapolation with spatial sampling, a mapping mechanism is established between constellation interference configurations and aggregated interference probability distributions. Based on this, an optimized segmentation model for the ground station distribution of large-scale LEO constellations is discussed. A grid-based adaptive segmentation method, driven by antenna characteristics, is proposed. Simulation results demonstrate that the proposed method offers higher accuracy in characterizing the satellite spatial distribution in constellations when compared with the spherical uniform triangular segmentation and equidistant latitude-longitude spherical segmentation methods. Furthermore, the proposed method shows both accuracy and computational efficiency when compared to traditional timedomain extrapolation results, proving its effectiveness.

Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs adapt to input graphs of varying size and structure. We propose a new graph pooling operation based on compressive Haar transforms — \emph{HaarPooling}. HaarPooling implements a cascade of pooling operations; it is computed by following a sequence of clusterings of the input graph. A HaarPooling layer transforms a given input graph to an output graph with a smaller node number and the same feature dimension; the compressive Haar transform filters out fine detail information in the Haar wavelet domain. In this way, all the HaarPooling layers together synthesize the features of any given input graph into a feature vector of uniform size. Such transforms provide a sparse characterization of the data and preserve the structure information of the input graph. GNNs implemented with standard graph convolution layers and HaarPooling layers achieve state of the art performance on diverse graph classification and regression problems.

Cosmic microwave background radiation (CMB) is critical to the understanding of the early universe and precise estimation of cosmological constants. Due to the contamination of thermal dust noise in the galaxy, the CMB map that is an image on the two-dimensional sphere has missing observations, mainly concentrated on the equatorial region. The noise of the CMB map has a significant impact on the estimation precision for cosmological parameters. Inpainting the CMB map can effectively reduce the uncertainty of parametric estimation. In this paper, we propose a deep learning-based variational autoencoder - CosmoVAE, to restoring the missing observations of the CMB map. The input and output of CosmoVAE are square images. To generate training, validation, and test data sets, we segment the full-sky CMB map into many small images by Cartesian projection. CosmoVAE assigns physical quantities to the parameters of the VAE network by using Fourier coefficients, which are sampled by the angular power spectrum of the Gaussian random field as latent variables. CosmoVAE adopts a new loss function to improve the learning performance of the model, which consists of ℓ1reconstruction loss, Kullback-Leibler divergence between the posterior distribution of encoder network and the prior distribution of latent variables, perceptual loss, and total-variation regularizer. The proposed model achieves state of the art performance for Planck Commander 2018 CMB map inpainting.