Bryan Hamrick (ugandanumber4)

Various weather conditions, such as rain, haze, or snow, can degrade visual quality in images/videos, which may significantly degrade the performance of related applications. In this paper, a novel framework based on sequential dual attention deep network is proposed for removing rain streaks (deraining) in a single image, called by SSDRNet (Sequential dual attentionbased Single image DeRaining deep Network). buy Polyinosinic acid-polycytidylic acid Since the inherent correlation among rain steaks within an image should be stronger than that between the rain streaks and the background (non-rain) pixels, a two-stage learning strategy is implemented to better capture the distribution of rain streaks within a rainy image. The two-stage deep neural network primarily involves three blocks residual dense blocks (RDBs), sequential dual attention blocks (SDABs), and multi-scale feature aggregation modules (MAMs), which are all delicately and specifically designed for rain removal. The two-stage strategy successfully learns very fine details of the rain steaks of the image and then clearly removes them. Extensive experimental results have shown that the proposed deep framework achieves the best performance on qualitative and quantitative metrics compared with state-of-the-art methods. The corresponding code and the trained model of the proposed SSDRNet have been available online at https//github.com/fityanul/SDAN-for-Rain-Removal.Focused ultrasound (FUS) exposure of microbubble (MB) contrast agents can transiently increase microvascular permeability allowing anticancer drugs to extravasate into a targeted tumor tissue. Either fixed or mechanically steered in space, most studies to date have used a single element focused transducer to deliver the ultrasound (US) energy. The goal of this study was to investigate various multi-FUS strategies implemented on a programmable US scanner (Vantage 256, Verasonics Inc) equipped with a linear array for image guidance and a 128-element therapy transducer (HIFUPlex-06, Sonic Concepts). The multi-FUS strategies include multi-FUS with sequential excitation (multi-FUS-SE) and multi-FUS with temporal sequential excitation (multi-FUS-TSE) and were compared to single-FUS and sham treatment. This study was performed using athymic mice implanted with breast cancer cells (N = 20). FUS therapy experiments were performed for 10 min after a solution containing MBs (Definity, Lantheus Medical Imaging Inc) and n therapy.Passive acoustic mapping (PAM) is an algorithm that reconstructs the location of acoustic sources using an array of receivers. This technique can monitor therapeutic ultrasound procedures to confirm the spatial distribution and amount of microbubble activity induced. Current PAM algorithms have an excellent lateral resolution but have a poor axial resolution, making it difficult to distinguish acoustic sources within the ultrasound beams. With recent studies demonstrating that short-length and low-pressure pulses-acoustic wavelets-have the therapeutic function, we hypothesized that the axial resolution could be improved with a quasi-pulse-echo approach and that the resolution improvement would depend on the wavelet's pulse length. This article describes an algorithm that resolves acoustic sources axially using time of flight and laterally using delay-and-sum beamforming, which we named axial temporal position PAM (ATP-PAM). The algorithm accommodates a rapid short pulse (RaSP) sequence that can safely deliver drugs across the blood-brain barrier. We developed our algorithm with simulations (k-wave) and in vitro experiments for one-, two-, and five-cycle pulses, comparing our resolution against that of two current PAM algorithms. We then tested ATP-PAM in vivo and evaluated whether the reconstructed acoustic sources mapped to drug delivery within the brain. In simulations and in vitro, ATP-PAM had an improved resolution for all pulse lengths tested. In vivo, experiments in mice indicated that A