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    Accurate 3D contrast-free myocardial infarction delineation using a 4D dual-stream spatiotemporal feature learning framework
    (Elsevier, 2023) Liu, Jinhao; Zhu, Xinglai; Xu, Chenchu; Xu, Lei; Polat, Kemal; Alenezi, Fayadh
    Accurate 3D contrast-free myocardial infarction (MI) delineation has the potential to eliminate the need for toxic injections, thereby significantly advances diagnosis and treatment of MI. In this study, we propose a 4D dual-stream spatiotemporal feature learning framework (4D-DSS) that enables learning of 4D (3D + T) representation of the heart to accurately map the 3D MI regions, thereby directly delineating of 3D MI without contrast agent. This framework creatively introduces a dual-stream 3D spatiotemporal point cloud architecture enables to learn the myocardial 4D representation in both local and global aspects, and improve the comprehension and precision of the representation. Specifically, the framework utilizes the local spatiotemporal variation of individual point clouds to characterize minute distortions in myocardial regions and the global spatiotemporal variation of point cloud sequences to represent the overall myocardial motion between frames, thereby enables comprehensive learning of 3D myocardial motion and leverages these features to classify myocardial tissue into MI regions and normal regions. 4D-DSS significantly improved performance (with a precision increase of at least 4%) compared to four advanced methods. The results support the impact of our 4D-DSS framework on the development and implementation of 3D contrast-free myocardial infarction region delineation technology.& COPY; 2023 Elsevier B.V. All rights reserved.
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    Common-Unique Decomposition Driven Diffusion Model for Contrast-Enhanced Liver MR Images Multi-Phase Interconversion
    (Institute of Electrical and Electronics Engineers Inc., 2024) Xu, Chenchu; Tian, Shijie; Wang, Boyan; Zhang, Jie; Polat, Kemal; Alhudhaif, Adi; Li, Shuo
    All three contrast-enhanced (CE) phases (e.g., Arterial, Portal Venous, and Delay) are crucial for diagnosing liver tumors. However, acquiring all three phases is constrained due to contrast agents (CAs) risks, long imaging time, and strict imaging criteria. In this paper, we propose a novel Common-Unique Decomposition Driven Diffusion Model (CUDD-DM), capable of converting any two input phases in three phases into the remaining one, thereby reducing patient wait time, conserving medical resources, and reducing the use of CAs. 1) The Common-Unique Feature Decomposition Module, by utilizing spectral decomposition to capture both common and unique features among different inputs, not only learns correlations in highly similar areas between two input phases but also learns differences in different areas, thereby laying a foundation for the synthesis of remaining phase. 2) The Multi-scale Temporal Reset Gates Module, by bidirectional comparing lesions in current and multiple historical slices, maximizes reliance on previous slices when no lesions and minimizes this reliance when lesions are present, thereby preventing interference between consecutive slices. 3) The Diffusion Model-Driven Lesion Detail Synthesis Module, by employing a continuous and progressive generation process, accurately captures detailed features between data distributions, thereby avoiding the loss of detail caused by traditional methods (e.g., GAN) that overfocus on global distributions. Extensive experiments on a generalized CE liver tumor dataset have demonstrated that our CUDD-DM achieves state-of-the-art performance (improved the SSIM by at least 2.2% (lesions area 5.3%) comparing the seven leading methods). These results demonstrate that CUDD-DM advances CE liver tumor imaging technology. IEEE
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    Prediction of Freezing of Gait in Parkinson's disease based on multi-channel time-series neural network
    (Elsevier, 2024) Wang, Boyan; Hu, Xuegang; Ge, Rongjun; Xu, Chenchu; Zhang, Jinglin; Gao, Zhifan; Zhao, Shu
    Freezing of Gait (FOG) is a noticeable symptom of Parkinson's disease, like being stuck in place and increasing the risk of falls. The wearable multi-channel sensor system is an efficient method to predict and monitor the FOG, thus warning the wearer to avoid falls and improving the quality of life. However, the existing approaches for the prediction of FOG mainly focus on a single sensor system and cannot handle the interference between multi-channel wearable sensors. Hence, we propose a novel multi-channel time-series neural network (MCT-Net) approach to merge multi-channel gait features into a comprehensive prediction framework, alerting patients to FOG symptoms in advance. Owing to the causal distributed convolution, MCT-Net is a real-time method available to give optimal prediction earlier and implemented in remote devices. Moreover, intrachannel and inter-channel transformers of MCT-Net extract and integrate different sensor position features into a unified deep learning model. Compared with four other state-of-the-art FOG prediction baselines, the proposed MCT-Net obtains 96.21% in accuracy and 80.46% in F1-score on average 2 s before FOG occurrence, demonstrating the superiority of MCT-Net.