Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction

Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to reduce radiation dose and benefit clinical applications. Previous voxel-based generation methods represent the CT as discrete voxels, resulting in high memory requirements and limited spatial resolution due to the use of 3D decoders. In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstruction from extremely sparse (fewer than 10) projection views at an ultrafast speed. The intensity field of a CT can be regarded as a continuous function of 3D spatial points. Therefore, the reconstruction can be reformulated as regressing the intensity value of an arbitrary 3D point from given sparse projections. Specifically, for a point, DIF-Net extracts its view-specific features from different 2D projection views. These features are subsequently aggregated by a fusion module for intensity estimation. Notably, thousands of points can be processed in parallel to improve efficiency during training and testing. In practice, we collect a knee CBCT dataset to train and evaluate DIF-Net. Extensive experiments show that our approach can reconstruct CBCT with high image quality and high spatial resolution from extremely sparse views within 1.6 seconds, significantly outperforming state-of-the-art methods. Our code will be available at https://github.com/xmed-lab/DIF-Net.Comment: MICCAI'2

A Privacy-Preserving Finite-Time Push-Sum based Gradient Method for Distributed Optimization over Digraphs

This paper addresses the problem of distributed optimization, where a network of agents represented as a directed graph (digraph) aims to collaboratively minimize the sum of their individual cost functions. Existing approaches for distributed optimization over digraphs, such as Push-Pull, require agents to exchange explicit state values with their neighbors in order to reach an optimal solution. However, this can result in the disclosure of sensitive and private information. To overcome this issue, we propose a state-decomposition-based privacy-preserving finite-time push-sum (PrFTPS) algorithm without any global information such as network size or graph diameter. Then, based on PrFTPS, we design a gradient descent algorithm (PrFTPS-GD) to solve the distributed optimization problem. It is proved that under PrFTPS-GD, the privacy of each agent is preserved and the linear convergence rate related to the optimization iteration number is achieved. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed approach

HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving

Autonomous driving systems generally employ separate models for different tasks resulting in intricate designs. For the first time, we leverage singular multimodal large language models (MLLMs) to consolidate multiple autonomous driving tasks from videos, i.e., the Risk Object Localization and Intention and Suggestion Prediction (ROLISP) task. ROLISP uses natural language to simultaneously identify and interpret risk objects, understand ego-vehicle intentions, and provide motion suggestions, eliminating the necessity for task-specific architectures. However, lacking high-resolution (HR) information, existing MLLMs often miss small objects (e.g., traffic cones) and overly focus on salient ones (e.g., large trucks) when applied to ROLISP. We propose HiLM-D (Towards High-Resolution Understanding in MLLMs for Autonomous Driving), an efficient method to incorporate HR information into MLLMs for the ROLISP task. Especially, HiLM-D integrates two branches: (i) the low-resolution reasoning branch, can be any MLLMs, processes low-resolution videos to caption risk objects and discern ego-vehicle intentions/suggestions; (ii) the high-resolution perception branch (HR-PB), prominent to HiLM-D,, ingests HR images to enhance detection by capturing vision-specific HR feature maps and prioritizing all potential risks over merely salient objects. Our HR-PB serves as a plug-and-play module, seamlessly fitting into current MLLMs. Experiments on the ROLISP benchmark reveal HiLM-D's notable advantage over leading MLLMs, with improvements of 4.8% in BLEU-4 for captioning and 17.2% in mIoU for detection

An Efficient Distributed Nash Equilibrium Seeking with Compressed and Event-triggered Communication

Distributed Nash equilibrium (NE) seeking problems for networked games have been widely investigated in recent years. Despite the increasing attention, communication expenditure is becoming a major bottleneck for scaling up distributed approaches within limited communication bandwidth between agents. To reduce communication cost, an efficient distributed NE seeking (ETC-DNES) algorithm is proposed to obtain an NE for games over directed graphs, where the communication efficiency is improved by event-triggered exchanges of compressed information among neighbors. ETC-DNES saves communication costs in both transmitted bits and rounds of communication. Furthermore, our method only requires the row-stochastic property of the adjacency matrix, unlike previous approaches that hinged on doubly-stochastic communication matrices. We provide convergence guarantees for ETC-DNES on games with restricted strongly monotone mappings and testify its efficiency with no sacrifice on the accuracy. The algorithm and analysis are extended to a compressed algorithm with stochastic event-triggered mechanism (SETC-DNES). In SETC-DNES, we introduce a random variable in the triggering condition to further enhance algorithm efficiency. We demonstrate that SETC-DNES guarantees linear convergence to the NE while achieving even greater reductions in communication costs compared to ETC-DNES. Finally, numerical simulations illustrate the effectiveness of the proposed algorithms

On the validity of the local Fourier analysis

Local Fourier analysis (LFA) is a useful tool in predicting the convergence factors of geometric multigrid methods (GMG). As is well known, on rectangular domains with periodic boundary conditions this analysis gives the exact convergence factors of such methods. In this work, using the Fourier method, we extend these results by proving that such analysis yields the exact convergence factors for a wider class of problems

Safety and efficacy of short-term dual antiplatelet therapy combined with intensive rosuvastatin in acute ischemic stroke

Objective: To investigate the safety and efficacy of short-term (7-day) Dual Antiplatelet Therapy (DAPT) with intensive rosuvastatin in Acute Ischemic Stroke (AIS). Methods: In this study, patients with AIS in the emergency department of the hospital from October 2016 to December 2019 were registered and divided into the control group (Single Antiplatelet Therapy [SAPT] + rosuvastatin) and the study group (7-day DAPT + intensive rosuvastatin) according to the therapy regimens. The generalized linear model was used to compare the National Institute of Health Stroke Scale (NIHSS) scores between the two groups during the 21-day treatment. A Cox regression model was used to compare recurrent ischemic stroke, bleeding events, Statin-Induced Liver Injury (SILI), and Statin-Associated Myopathy (SAM) between the two groups during the 90-day follow-up. Results: Comparison of NIHSS scores after 21-day treatment: NIHSS scores in the study group decreased significantly, 0.273-times as much as that in the control group (Odds Ratio [OR] 0.273; 95% Confidence Interval [95% CI] 0.208–0.359; p < 0.001). Comparison of recurrent ischemic stroke during the 90-day follow-up: The therapy of the study group reduced the risk of recurrent stroke by 65% (7.76% vs. 22.82%, Hazard Ratio [HR] 0.350; 95% CI 0.167–0.730; p = 0.005). Comparison of bleeding events: There was no statistical difference between the two groups (7.79% vs. 6.71%, HR = 1.076; 95% CI 0.424–2.732; p = 0.878). No cases of SILI and SAM were found. Conclusions: Short-term DAPT with intensive rosuvastatin effectively relieved the clinical symptoms and significantly reduced the recurrent stroke for patients with mild-to-moderate AIS within 90 days, without increasing bleeding events, SILI and SAM

Design and real-time implementation of data-driven adaptive wide-area damping controller for back-to-back VSC-HVDC

This paper proposes a data-driven adaptive wide-area damping controller (D-WADC) for back-to-back VSC-HVDC to suppress the low frequency oscillation in a large-scale interconnected power system. The proposed D-WADC adopts a dual-loop control structure to make full use of the active and reactive power control of VSC-HVDC to improve the damping of the power system. A data-driven algorithm named the goal representation heuristic dynamic programming is employed to design the proposed D-WADC, which means the design procedure only requires the input and output data rather than the mathematic model of the concerned power system. Thus, the D-WADC can adapt to the change of operating condition through online weight modification. Besides, the adaptive delay compensator (ADC) is added to effectively compensate the stochastic delay involved in the wide-area feedback signal. Case studies are conducted based on the simplified model of a practical power system and the 16-machine system with a back-to-back VSC-HVDC. Both the simulation and hardware-in-loop experiment results verify that the proposed D-WADC can effectively suppress the low-frequency oscillation under a wide range of operating conditions, disturbances, and stochastic communication delays