To reduce the radiation dose, sparse-view computed tomography (CT) reconstruction has been proposed, aiming to recover high-quality CT images from sparsely sampled sinogram. To eliminate the artifacts present in sparse-view CT images, a new dual-domain diffusion model (DDDM) is proposed, which is composed of a sinogram upgrading module (SUM) and an image refining module (IRM) connected in series. In the sinogram domain, a novel degrading and upgrading framework is defined, in which SUM is trained to upgrade sparse-view sinograms step by step to reverse the degradation process of CT images caused by successive down-sampling of scanning views. In the image domain, IRM adopts an improved denoising diffusion framework to further reduce remaining artifacts and restore image details, where a skip connection from the original sparseview sinogram is introduced to constrain the generation of details. Our DDDM shows significant improvement over deep-learning baseline models in both classical similarity metrics and perceptual loss, and has good generalization to untrained organs. We release our code at https://github.com/YC-Markus/code-for-DDDM.

In robot-assisted cardiac surgery, predicting heart motion can help improve the operation accuracy and safety of surgical robots. Different from the conventional prediction schemes which model the point of interest (POI) with only temporal correlation of past observations, this paper proposes an LSTM-based method by exploiting the spatio-temporal correlation of the 3D movements of POI and auxiliary points (APs) on the same surface of the heart. Three different LSTM models are investigated. The first two models define the POI prediction as a pure time-series forecasting problem based on past POI trajectory, and the third model combines the past observations of POI and new observations of APs to take into consideration the extra spatial correlations for prediction. Experimental comparison studies based on 3D coordinates obtained from real stereo-endoscopic videos demonstrate the superior performance of the proposed spatio-temporal LSTM model.

In dynamic minimally invasive surgery environments, 3D reconstruction of deformable soft-tissue surfaces with stereo endoscopic images is very challenging. A simple self-supervised stereo reconstruction framework is proposed to address this issue, which bridges the traditional geometric deformable models and the newly revived neural networks. The equivalence between the classical thin plate spline (TPS) model and a single-layer fully-connected or convolutional network is studied. By alternating training of two TPS equivalent networks within the self-supervised framework, disparity priors are learnt from the past stereo frames of target tissues to form an optimized disparity basis, on which disparity maps of subsequent frames can be estimated more accurately without sacrificing computational efficiency and robustness. The proposed method was verified on stereo-endoscopic videos recorded by the da Vinci®surgical robots.

GRACE was applied to observed the trends of Terrestrial Water Storage change in the Amazon Basin by estimating the monthly mean from all the months in each year, that is from 2002 to 2015, a period of 13 years. The characteristics of the Terrestrial Water Storage (TWS) change over the Amazon Basin are that, 2012-2015 is wetter than 2002-2011. Precipitation anomaly from NOAA climate Data record shows more precipitation between 2012 and 2015 as compared to 2008 to 2010. This could be accounted to the negative TWS change over the Amazon Basin from 2008 to 2010 and positive TWS change from 2012-2015. There has been a long continuous negative change in Terrestrial Water Storage over the Amazon Basin from 2002 to 2011 compared to a short positive change from 2012 to 2015. So the question here, is that, what would be the future for TWS change over the Amazon Basin for next 10 years? The most important aspect of this research paper in the territory of water resource is to provide an analysis to the hydrological institutions about the current impact of GRACE satellite mission on terrestrial water storage observation and to further exhibits the special quality of GRACE.

We present a localized neural network algorithm for water depth estimation from optical remote sensing images. Our new model is called a locally adaptive back-propagation neural network (LABPNN). In an LABPNN, the neural networks were trained at regularly distributed normative sites. For each unit of LABPNN, training data samples were identified by a specified search radius from the normative sites. Water depth was estimated by an ensemble of LABPNNs, with weights assigned inversely by their distances to the point of estimation. The water depth prediction accuracy from the LABPNN models doubled compared to the regular back-propagation network that uses all of the samples without considering nonstationarity. We also compared the LABPNN model with the regression-based inversion model with the localization feature. The prediction error of LABPNN is less by about 5% in the first case study, and 7% less in the second case study. It is because of the better performance of neural networks than that of the regression models when the sample data are relatively sparse. The experiments suggest that the LABPNN model is a viable solution to water depth retrieval from optical images.