专业成就

科研与项目

我在研究生阶段进行过的科研以及做过的一些项目

科研经历

共 2 条

NERVE: A Near-Real-Time Three-Dimensional Reconstruction Approach for Interventional Devices and Surrounding Vessels Using a Monoplane C-arm Cone-Beam CT

第一作者

Real-time three-dimensional (3D) reconstruction of interventional devices, such as stents and embolization coils, together with surrounding vessels, using a monoplane C-arm cone-beam CT (CBCT) has emerged as a critical capability in modern neurovascular interventions. Due to the slow gantry rotation speed of current CBCT systems, real-time 3D recon struction of interventional devices and surrounding vessels has never been accomplished. In this work, we propose a framework for near-real-time 3D reconstruction of interventional devices and vasculature, termed NERVE, which integrates deep structure extraction (DSE) model and deep backprojection filtration (DBF) model together to accomplish 3D reconstruction of interventional devices and vasculature using data acquired at three projection views over a 60◦ angular range. DSE is trained to extract targets from acquired projection data by removing irrelevant anatomical background. DBF is trained to transform direction backpro jection image volumes to final 3D image volumes accordingly. DBF is designed to learn prior regularization that captures the distribution-level characteristics of ideal interventional devices and vasculature to eliminating limited view angle artifacts and high-attenuation-induced artifacts. Extensive numerical simula tions (1000 samples for testing) validate the feasibility and effec tiveness of NERVE. NERVE explicitly leverages subject-specific measured data, ensuring near-real-time 3D reconstruction with high fidelity, potentially facilitating image-guided interventional navigation for patients with neurovascular diseases.

poster9th International Conference on Image Formation in X-Ray Computed Tomography

工具：Interventional Devices; Real-Time Three Dimensional Reconstruction; Deep Learning

查看详情

GDP-Net: Global Dependency-Enhanced Dual-Domain Parallel Network for Ring Artifact Removal

第三作者

In Computed Tomography (CT) imaging, the ring artifacts caused by the inconsistent detector response can significantly degrade the reconstructed images, having negative impacts on the subsequent applications. The new generation of CT systems based on photon-counting detectors are affected by ring artifacts more severely. The flexibility and variety of detector responses make it difficult to build a well-defined model to characterize the ring artifacts. In this context, this study proposes the global dependency-enhanced dual-domain parallel neural network for Ring Artifact Removal (RAR). First, based on the fact that the features of ring artifacts are different in Cartesian and Polar coordinates, the parallel architecture is adopted to construct the deep neural network so that it can extract and exploit the latent features from different domains to improve the performance of ring artifact removal. Besides, the ring artifacts are globally relevant whether in Cartesian or Polar coordinate systems, but convolutional neural networks show inherent shortcomings in modeling long-range dependency. To tackle this problem, this study introduces the novel Mamba mechanism to achieve a global receptive field without incurring high computational complexity. It enables effective capture of the long-range dependency, thereby enhancing the model performance in image restoration and artifact reduction. The experiments on the simulated data validate the effectiveness of the dual-domain parallel neural network and the Mamba mechanism, and the results on two unseen real datasets demonstrate the promising performance of the proposed RAR algorithm in eliminating ring artifacts and recovering image details.

IEEE Transactions on Medical ImagingVolume: 44; Issue: 6; June 2025)

工具：Computed tomography, ring artifact removal, parallel neural network, global dependency, Mamba mechanism

查看详情

项目经历

共 0 条