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学者姓名：吴水才
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摘要 ：
Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.
关键词 ：
advanced brain aging advanced brain aging structural MRI structural MRI accelerated brain aging accelerated brain aging heterogeneity heterogeneity subtypes subtypes
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| GB/T 7714 | Liu, Lingyu , Lin, Lan , Sun, Shen et al. Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset [J]. | BIOENGINEERING-BASEL , 2024 , 11 (2) . |
| MLA | Liu, Lingyu et al. "Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset" . | BIOENGINEERING-BASEL 11 . 2 (2024) . |
| APA | Liu, Lingyu , Lin, Lan , Sun, Shen , Wu, Shuicai . Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset . | BIOENGINEERING-BASEL , 2024 , 11 (2) . |
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摘要 ：
Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-tonormal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.
关键词 ：
Entropy imaging Entropy imaging Quantitative ultrasound Quantitative ultrasound Probability distribution histogram Probability distribution histogram Cumulative relative entropy Cumulative relative entropy k-nearest neighbor k-nearest neighbor
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| GB/T 7714 | Li, Sinan , Tsui, Po-Hsiang , Wu, Weiwei et al. Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications [J]. | ULTRASONICS , 2024 , 138 . |
| MLA | Li, Sinan et al. "Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications" . | ULTRASONICS 138 (2024) . |
| APA | Li, Sinan , Tsui, Po-Hsiang , Wu, Weiwei , Wu, Shuicai , Zhou, Zhuhuang . Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications . | ULTRASONICS , 2024 , 138 . |
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摘要 ：
The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model's generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model's discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely "Abdominal and Direct Fetal ECG Database" and "Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations", resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper's model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols.
关键词 ：
PatchGAN PatchGAN convolutional neural networks convolutional neural networks bidirectional long short-term memory bidirectional long short-term memory CycleGAN CycleGAN fetal electrocardiogram signal extraction fetal electrocardiogram signal extraction
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| GB/T 7714 | Yang, Yuyao , Chen, Lin , Wu, Shuicai . Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture [J]. | SENSORS , 2024 , 24 (9) . |
| MLA | Yang, Yuyao et al. "Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture" . | SENSORS 24 . 9 (2024) . |
| APA | Yang, Yuyao , Chen, Lin , Wu, Shuicai . Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture . | SENSORS , 2024 , 24 (9) . |
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摘要 ：
Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns, such as scatterer concentrations and arrangements. In this study, we proposed a machine learning approach to integrate the strength of multimodality quantitative ultrasound envelope statistics imaging techniques and applied it to detecting microwave ablation induced thermal lesions in porcine liver ex vivo . The quantitative ultrasound parameters included were homodyned-K alpha which is a scatterer clustering parameter related to the effective scatterer number per resolution cell, Nakagami m which is a shape parameter of the envelope probability density function, and Shannon entropy which is a measure of signal uncertainty or complexity. Specifically, the homodyned-K log(10) ( alpha ), Nakagami-m , and horizontally normalized Shannon entropy parameters were combined as input features to train a support vector machine (SVM) model to classify thermal lesions with higher scatterer concentrations from normal tissues with lower scatterer concentrations. Through heterogeneous phantom simulations based on Field II, the proposed SVM model showed a classification accuracy above 0.90; the area accuracy and Dice score of higher-scatterer-concentration zone identification exceeded 83% and 0.86, respectively, with the Hausdorff distance <26. Microwave ablation experiments of porcine liver ex vivo at 60 - 80 W, 1-3 min showed that the SVM model achieved a classification accuracy of 0.85; compared with single log(10) ( alpha ) , m, or hNSE parametric imaging, the SVM model achieved the highest area accuracy (89.1%) and Dice score (0.77) as well as the smallest Hausdorff distance (46.38) of coagulation zone identification. We concluded that the proposed multimodality quantitative ultrasound envelope statistics imaging based SVM approach can enhance the capability to characterize tissue scatterer distribution patterns and has the potential to detect the thermal lesions induced by microwave ablation.
关键词 ：
Multimodality ultrasound envelope statistics imaging Multimodality ultrasound envelope statistics imaging Ultrasound tissue characterization Ultrasound tissue characterization Tissue scatterer distribution Tissue scatterer distribution Quantitative ultrasound Quantitative ultrasound Machine learning Machine learning
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| GB/T 7714 | Li, Sinan , Tsui, Po-Hsiang , Wu, Weiwei et al. Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions [J]. | ULTRASONICS SONOCHEMISTRY , 2024 , 107 . |
| MLA | Li, Sinan et al. "Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions" . | ULTRASONICS SONOCHEMISTRY 107 (2024) . |
| APA | Li, Sinan , Tsui, Po-Hsiang , Wu, Weiwei , Zhou, Zhuhuang , Wu, Shuicai . Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions . | ULTRASONICS SONOCHEMISTRY , 2024 , 107 . |
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摘要 ：
本发明涉及图像处理领域，具体涉及一种基于CT影像的热消融手术路径规划装置及方法，该方法包括：获取目标患者的胸腔CT序列图像并进行三视图显示；根据分割模型对CT序列图像进行胸腔目标结构分割；对胸腔目标结构进行三维场景重建；根据重建场景图像中的肺肿瘤结构和皮肤结构分别提取肿瘤靶点和进针区域并组成穿刺路径；根据重建场景图像分别提取强约束条件和弱约束条件；根据强约束条件将进针区域划分为可行进针区域和不可行进针区域；根据弱约束条件对可行进针区域的穿刺路径进行评分，并根据评分结果筛选目标路径。本发明实现消融手术穿刺路径自动规划，通过自动规划的路径提高了消融手术的准确性和质量。
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| GB/T 7714 | 吴水才 , 王正帅 , 高宏建 et al. 一种基于CT影像的热消融手术路径规划装置及方法 : CN202310666475.3[P]. | 2023-06-06 . |
| MLA | 吴水才 et al. "一种基于CT影像的热消融手术路径规划装置及方法" : CN202310666475.3. | 2023-06-06 . |
| APA | 吴水才 , 王正帅 , 高宏建 , 周著黄 , 杨春兰 . 一种基于CT影像的热消融手术路径规划装置及方法 : CN202310666475.3. | 2023-06-06 . |
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摘要 ：
一种基于K近邻估计的超声信息熵成像方法属于超声影像技术领域。此发明利用现有超声扫描设备，实现了一种可以定量显示生物组织目标区域的可视化成像新技术。此发明从超声背向散射回波原始数据中直接计算信息熵，并利用滑动窗口法生成2维的超声信息熵图像。此发明采用了基于K近邻的估计器来计算熵值，提高了熵估计的稳定性和可靠性。
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| GB/T 7714 | 吴水才 , 李思楠 , 周著黄 . 一种基于K近邻估计的超声信息熵成像方法 : CN202310608734.7[P]. | 2023-05-28 . |
| MLA | 吴水才 et al. "一种基于K近邻估计的超声信息熵成像方法" : CN202310608734.7. | 2023-05-28 . |
| APA | 吴水才 , 李思楠 , 周著黄 . 一种基于K近邻估计的超声信息熵成像方法 : CN202310608734.7. | 2023-05-28 . |
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摘要 ：
Visually, the state of peripheral circulation in the body can be seen in the prolonged capillary refill time (CRT) when there are peripheral circulation disorders. Various forms of shock, organ failure, frostbite, and vasculitis are associated with prolonged capillary refill time. Given that this index is currently measured mainly by clinicians with the naked eye, it is lack of consensus on the measurement method, resulting extremely large measurement errors. This limits the application in clinical practice controversial. An examination of CRT's clinical utilization, its emotional components, and novel approaches to gauging CRT that could potentially facilitate more precise clinical tests for monitoring the condition of human peripheral circulation is presented in the review.In addition, our laboratory provides a new method for measuring CRT - a fully automatic capillary refill time measurement instrument based on PID pressure control, which introduces optical sensors instead of human eyes to record the change of light intensity in the subject area and can quantify and control the pressure by PID system, which reduces the influence of human factors on the measurement to a certain extent. This technology introduces an optical sensor instead of the human eye to record the light intensity changes in the subject area and can quantify and control the pressure through a PID system, which reduces the influence of human factors on the measurement results and improves the accuracy of the measurement.
关键词 ：
CRT CRT peripheral perfusion peripheral perfusion capillary refill time capillary refill time infectious shock infectious shock guiding surgery for autoimmune pancreatitis guiding surgery for autoimmune pancreatitis
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| GB/T 7714 | Ma, Ziyu , Lin, Lan , Wu, Shuicai et al. Can Capillary Refill Time (CRT) measurement be a reliable clinical test? [J]. | PROCEEDINGS OF 2023 4TH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE FOR MEDICINE SCIENCE, ISAIMS 2023 , 2023 : 1089-1095 . |
| MLA | Ma, Ziyu et al. "Can Capillary Refill Time (CRT) measurement be a reliable clinical test?" . | PROCEEDINGS OF 2023 4TH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE FOR MEDICINE SCIENCE, ISAIMS 2023 (2023) : 1089-1095 . |
| APA | Ma, Ziyu , Lin, Lan , Wu, Shuicai , Chen, Yixiong , Sun, Shen . Can Capillary Refill Time (CRT) measurement be a reliable clinical test? . | PROCEEDINGS OF 2023 4TH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE FOR MEDICINE SCIENCE, ISAIMS 2023 , 2023 , 1089-1095 . |
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摘要 ：
Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.
关键词 ：
multi-modality MRI multi-modality MRI brain age prediction brain age prediction UK Biobank UK Biobank machine learning machine learning
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| GB/T 7714 | Xiong, Min , Lin, Lan , Jin, Yue et al. Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults [J]. | SENSORS , 2023 , 23 (7) . |
| MLA | Xiong, Min et al. "Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults" . | SENSORS 23 . 7 (2023) . |
| APA | Xiong, Min , Lin, Lan , Jin, Yue , Kang, Wenjie , Wu, Shuicai , Sun, Shen . Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults . | SENSORS , 2023 , 23 (7) . |
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摘要 ：
一种基于超声信息熵图像与零差K分布α参数图像融合的定征生物组织的方法属于超声影像技术领域。此发明将超声信息熵图像和零差K分布α参数图像进行信息融合，并生成可以清晰显示组织变性边界的超声伪彩色图像。该融合方法识别生物组织变性区域的面积准确率达到87.68％。
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| GB/T 7714 | 吴水才 , 李思楠 , 夏涛 et al. 一种基于超声信息熵图像与零差K分布α参数图像融合的定征生物组织的方法 : CN202210631746.7[P]. | 2022-06-06 . |
| MLA | 吴水才 et al. "一种基于超声信息熵图像与零差K分布α参数图像融合的定征生物组织的方法" : CN202210631746.7. | 2022-06-06 . |
| APA | 吴水才 , 李思楠 , 夏涛 , 周著黄 . 一种基于超声信息熵图像与零差K分布α参数图像融合的定征生物组织的方法 : CN202210631746.7. | 2022-06-06 . |
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摘要 ：
Percutaneous thermal therapy is an important clinical treatment method for some solid tumors. It is critical to use effective image visualization techniques to monitor the therapy process in real time because precise control of the therapeutic zone directly affects the prognosis of tumor treatment. Ultrasound is used in thermal therapy monitoring because of its real-time, non-invasive, non-ionizing radiation, and low-cost characteristics. This paper presents a review of nine quantitative ultrasound-based methods for thermal therapy monitoring and their advances over the last decade since 2011. These methods were analyzed and compared with respect to two applications: ultrasonic thermometry and ablation zone identification. The advantages and limitations of these methods were compared and discussed, and future developments were suggested.
关键词 ：
thermometry thermometry quantitative ultrasound quantitative ultrasound thermal therapy thermal therapy ablation zone ablation zone
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| GB/T 7714 | Li, Sinan , Zhou, Zhuhuang , Wu, Shuicai et al. A Review of Quantitative Ultrasound-Based Approaches to Thermometry and Ablation Zone Identification Over the Past Decade [J]. | ULTRASONIC IMAGING , 2022 , 44 (5-6) : 213-228 . |
| MLA | Li, Sinan et al. "A Review of Quantitative Ultrasound-Based Approaches to Thermometry and Ablation Zone Identification Over the Past Decade" . | ULTRASONIC IMAGING 44 . 5-6 (2022) : 213-228 . |
| APA | Li, Sinan , Zhou, Zhuhuang , Wu, Shuicai , Wu, Weiwei . A Review of Quantitative Ultrasound-Based Approaches to Thermometry and Ablation Zone Identification Over the Past Decade . | ULTRASONIC IMAGING , 2022 , 44 (5-6) , 213-228 . |
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