Domain Transform Network for Photoacoustic Tomography from Limited-view and Sparsely Sampled Data.

TL;DR: This paper proposes a triple-path feature transform network (TFT-Net) for ring-array photoacoustic tomography, enhancing imaging quality from limited-view data by combining raw signals, linear reconstruction images, and physical models, and achieving fast and high-quality image reconstruction.

TL;DR: In this article, two optimization techniques, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), have been considered for image reconstruction and performance of developed algorithms was evaluated using structural similarity index (SSIM).

TL;DR: Utranspa is a transformer-based network for sparsely viewed photoacoustic tomography. It utilizes transformer architecture to exploit spatial and temporal correlations in the sparsely sampled data.

TL;DR: In this article, the authors developed a novel PACT system to provide real-time imaging, which is achieved by a 120-elements ultrasound array only using a single data acquisition (DAQ) channel.

TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.

TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.

TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.