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学者姓名：吴水才
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Abstract ：
Objective: Aiming at the shortcomings of artificial surgical path planning for the thermal ablation of liver tumors, such as the time-consuming and labor-consuming process, and relying heavily on doctors' puncture experience, an automatic path-planning system for thermal ablation of liver tumors based on CT images is designed and implemented. Methods: The system mainly includes three modules: image segmentation and three-dimensional reconstruction, automatic surgical path planning, and image information management. Through organ segmentation and three- dimensional reconstruction based on CT images, the personalized abdominal spatial anatomical structure of patients is obtained, which is convenient for surgical path planning. The weighted summation method based on clinical constraints and the concept of Pareto optimality are used to solve the multi-objective optimization problem, screen the optimal needle entry path, and realize the automatic planning of the thermal ablation path. The image information database was established to store the information related to the surgical path. Results: In the discussion with clinicians, more than 78% of the paths generated by the planning system were considered to be effective, and the efficiency of system path planning is higher than doctors' planning efficiency. Conclusion: After improvement, the system can be used for the planning of the thermal ablation path of a liver tumor and has certain clinical application value.
Keyword ：
surgical planning surgical planning weighted summation weighted summation Pareto optimality Pareto optimality system design system design thermal ablation of tumors thermal ablation of tumors
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| GB/T 7714 | Song, Ziwei , Ding, Feifei , Wu, Weiwei et al. Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images [J]. | SENSORS , 2024 , 24 (11) . |
| MLA | Song, Ziwei et al. "Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images" . | SENSORS 24 . 11 (2024) . |
| APA | Song, Ziwei , Ding, Feifei , Wu, Weiwei , Zhou, Zhuhuang , Wu, Shuicai . Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images . | SENSORS , 2024 , 24 (11) . |
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Abstract ：
The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.
Keyword ：
brain age prediction brain age prediction UK biobank UK biobank multimodal neuroimaging multimodal neuroimaging resilient brain aging resilient brain aging accelerated brain aging accelerated brain aging discriminative event-based analysis discriminative event-based analysis
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| GB/T 7714 | Lin, Lan , Wu, Yutong , Liu, Lingyu et al. Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data [J]. | BIOENGINEERING-BASEL , 2024 , 11 (7) . |
| MLA | Lin, Lan et al. "Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data" . | BIOENGINEERING-BASEL 11 . 7 (2024) . |
| APA | Lin, Lan , Wu, Yutong , Liu, Lingyu , Sun, Shen , Wu, Shuicai . Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data . | BIOENGINEERING-BASEL , 2024 , 11 (7) . |
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Abstract ：
This study investigates the relationship between modifiable risk factors and dementia subtypes using data from 460,799 participants in the UK Biobank. Utilizing univariate Cox proportional hazards regression models, we examined the associations between 83 modifiable risk factors and the risks of all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VD). Composite scores for different domains were generated by aggregating risk factors associated with ACD, AD, and VD, respectively, and their joint associations were assessed in multivariable Cox models. Additionally, population attributable fractions (PAF) were utilized to estimate the potential impact of eliminating adverse characteristics of the risk domains. Our findings revealed that an unfavorable medical history significantly increased the risk of ACD, AD, and VD (hazard ratios (HR) = 1.88, 95% confidence interval (95% CI): 1.74-2.03, p < 0.001; HR = 1.80, 95% CI: 1.54-2.10, p < 0.001; HR = 2.39, 95% CI: 2.10-2.71, p < 0.001, respectively). Blood markers (PAF = 12.1%; 17.4%) emerged as the most important risk domain for preventing ACD and VD, while psychiatric factors (PAF = 18.3%) were the most important for preventing AD. This study underscores the potential for preventing dementia and its subtypes through targeted interventions for modifiable risk factors. The distinct insights provided by HR and PAF emphasize the importance of considering both the strength of the associations and the population-level impact of dementia prevention strategies. Our research provides valuable guidance for developing effective public health interventions aimed at reducing the burden of dementia, representing a significant advancement in the field.
Keyword ：
risk domains risk domains Cox analysis Cox analysis population attributable fractions population attributable fractions risk factors risk factors dementia dementia
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| GB/T 7714 | Ma, Xiangge , Gao, Hongjian , Wu, Yutong et al. Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank [J]. | BIOMEDICINES , 2024 , 12 (9) . |
| MLA | Ma, Xiangge et al. "Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank" . | BIOMEDICINES 12 . 9 (2024) . |
| APA | Ma, Xiangge , Gao, Hongjian , Wu, Yutong , Zhu, Xinyu , Wu, Shuicai , Lin, Lan . Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank . | BIOMEDICINES , 2024 , 12 (9) . |
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The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.
Keyword ：
deep learning deep learning lifespan brain age lifespan brain age machine learning machine learning neuroimaging neuroimaging brain age prediction brain age prediction
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| GB/T 7714 | Wu, Yutong , Gao, Hongjian , Zhang, Chen et al. Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review [J]. | TOMOGRAPHY , 2024 , 10 (8) : 1238-1262 . |
| MLA | Wu, Yutong et al. "Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review" . | TOMOGRAPHY 10 . 8 (2024) : 1238-1262 . |
| APA | Wu, Yutong , Gao, Hongjian , Zhang, Chen , Ma, Xiangge , Zhu, Xinyu , Wu, Shuicai et al. Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review . | TOMOGRAPHY , 2024 , 10 (8) , 1238-1262 . |
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Abstract ：
In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.
Keyword ：
multiparametric imaging multiparametric imaging cognitive resilience cognitive resilience MRI MRI cognitive aging cognitive aging neuroimaging neuroimaging
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| GB/T 7714 | Xu, Xinze , Lin, Lan , Wu, Shuicai et al. Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics [J]. | BRAIN SCIENCES , 2023 , 13 (12) . |
| MLA | Xu, Xinze et al. "Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics" . | BRAIN SCIENCES 13 . 12 (2023) . |
| APA | Xu, Xinze , Lin, Lan , Wu, Shuicai , Sun, Shen . Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics . | BRAIN SCIENCES , 2023 , 13 (12) . |
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Abstract ：
Single-antenna microwave ablation (MWA) is mainly used to treat small tumors less than 3 cm in diameter. To obtain a larger coagulation zone in a single ablation, a dual-antenna ablation approach was proposed. A three-dimensional finite element method (FEM) simulation model of parallel dual-antennas was developed. Ex vivo experiments at 50 W for 8 min were performed to verify the model. Both the temperature changes in tissue and the size of the coagulation zone were recorded. The effects of dual-antenna spacing, heating power, and blood perfusion on the coagulation zone were analyzed. Fifteen experiments were carried out. The errors between the mean measurements and simulated results at the set temperature points were 1.08 degrees C, 0.95 degrees C, and 2.1 degrees C, respectively. For the same conditions, the blood perfusion of 1.0, 1.5, and 3.0 kg/(m(3)center dot s) can result in a reduction in the coagulation volume by 18.4%, 25.4%, and 42.5%. As the spacing increased, the coagulation zone of each antenna started to fuse together later and the resulting integral coagulation zone became larger. Dual-antenna MWA is expected to be used for the treatment of tumors larger than 5 cm in diameter.
Keyword ：
temperature distribution temperature distribution tumor ablation tumor ablation finite element method simulation finite element method simulation blood perfusion blood perfusion dual-antenna array dual-antenna array
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| GB/T 7714 | Wang, Jinying , Huang, Shengyang , Gao, Hongjian et al. Computer Simulations of Dual-Antenna Microwave Ablation and Comparison to Experimental Measurements [J]. | APPLIED SCIENCES-BASEL , 2023 , 13 (1) . |
| MLA | Wang, Jinying et al. "Computer Simulations of Dual-Antenna Microwave Ablation and Comparison to Experimental Measurements" . | APPLIED SCIENCES-BASEL 13 . 1 (2023) . |
| APA | Wang, Jinying , Huang, Shengyang , Gao, Hongjian , Liu, Ju , Zhang, Yubo , Wu, Shuicai . Computer Simulations of Dual-Antenna Microwave Ablation and Comparison to Experimental Measurements . | APPLIED SCIENCES-BASEL , 2023 , 13 (1) . |
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Abstract ：
Age-related cognitive decline is a global phenomenon that affects individuals worldwide. The course and extent of this decline are influenced by numerous factors, such as genetics, lifestyle, education, and cognitive engagement. The theory of brain and cognitive reserve/maintenance posits that these factors have a significant impact on the degree of cognitive decline and overall brain health. However, the absence of standardized definitions and measurements for these terms creates ambiguity in research. To address this issue, we utilized a robust and systematic experimental paradigm, employing a considerably large subject pool comprising 17,030 participants from the UK Biobank. Utilizing advanced machine learning methodologies, we were able to accurately quantify both brain maintenance (BM) and cognitive maintenance (CM), making use of six distinct MRI modalities and nine distinct cognitive capabilities. Our study successfully identified several significant features that were meaningfully associated with both BM and CM outcomes. The results of our study demonstrate that lifestyle factors play a significant role in influencing both BM and CM through unique and independent mechanisms. Specifically, our study found that health status is a critical determinant of BM, while diabetes was found to be moderately associated with CM. Furthermore, our study revealed a positive correlation between BM/CM and cognitive reserve. By carefully considering the unique and independent mechanisms that govern both BM and CM, as well as their correlation with cognitive reserve, our study has provided valuable insight into the various strategies that may be leveraged to promote sustainable interventions to enhance cognitive and brain health across the lifespan.
Keyword ：
cognitive aging cognitive aging brain maintenance brain maintenance cognitive maintenance cognitive maintenance cognitive reserve cognitive reserve MRI MRI
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| GB/T 7714 | Lin, Lan , Xiong, Min , Jin, Yue et al. Quantifying Brain and Cognitive Maintenance as Key Indicators for Sustainable Cognitive Aging: Insights from the UK Biobank [J]. | SUSTAINABILITY , 2023 , 15 (12) . |
| MLA | Lin, Lan et al. "Quantifying Brain and Cognitive Maintenance as Key Indicators for Sustainable Cognitive Aging: Insights from the UK Biobank" . | SUSTAINABILITY 15 . 12 (2023) . |
| APA | Lin, Lan , Xiong, Min , Jin, Yue , Kang, Wenjie , Wu, Shuicai , Sun, Shen et al. Quantifying Brain and Cognitive Maintenance as Key Indicators for Sustainable Cognitive Aging: Insights from the UK Biobank . | SUSTAINABILITY , 2023 , 15 (12) . |
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Abstract ：
It is rare to use the one-stage model without segmentation for the automatic detection of coronary lesions. This study sequentially enrolled 200 patients with significant stenoses and occlusions of the right coronary and categorized their angiography images into two angle views: The CRA (cranial) view of 98 patients with 2453 images and the LAO (left anterior oblique) view of 176 patients with 3338 images. Randomization was performed at the patient level to the training set and test set using a 7:3 ratio. YOLOv5 was adopted as the key model for direct detection. Four types of lesions were studied: Local Stenosis (LS), Diffuse Stenosis (DS), Bifurcation Stenosis (BS), and Chronic Total Occlusion (CTO). At the image level, the precision, recall, mAP@0.1, and mAP@0.5 predicted by the model were 0.64, 0.68, 0.66, and 0.49 in the CRA view and 0.68, 0.73, 0.70, and 0.56 in the LAO view, respectively. At the patient level, the precision, recall, and F1 scores predicted by the model were 0.52, 0.91, and 0.65 in the CRA view and 0.50, 0.94, and 0.64 in the LAO view, respectively. YOLOv5 performed the best for lesions of CTO and LS at both the image level and the patient level. In conclusion, the one-stage model without segmentation as YOLOv5 is feasible to be used in automatic coronary lesion detection, with the most suitable types of lesions as LS and CTO.
Keyword ：
convolutional neural network convolutional neural network coronary artery stenosis detection coronary artery stenosis detection deep learning deep learning coronary angiography coronary angiography without segmentation without segmentation one-stage detection one-stage detection
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| GB/T 7714 | Wu, Hui , Zhao, Jing , Li, Jiehui et al. One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning [J]. | DIAGNOSTICS , 2023 , 13 (18) . |
| MLA | Wu, Hui et al. "One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning" . | DIAGNOSTICS 13 . 18 (2023) . |
| APA | Wu, Hui , Zhao, Jing , Li, Jiehui , Zeng, Yan , Wu, Weiwei , Zhou, Zhuhuang et al. One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning . | DIAGNOSTICS , 2023 , 13 (18) . |
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Abstract ：
The segmentation of cardiac chambers and the quantification of clinical functional metrics in dynamic echo-cardiography are the keys to the clinical diagnosis of heart disease. Identifying the end-diastolic frames (EDFs) and end-systolic frames (ESFs) and manually segmenting the left ventricle in the echocardiographic cardiac cycle before obtaining the left ventricular ejection fraction (LVEF) is a time-consuming and tedious task for clinicians. In this work, we proposed a deep learning-based fully automated echocardiographic analysis method. We pro-posed a multi-attention efficient feature fusion network (MAEF-Net) to automatically segment the left ventricle. Then, EDFs and ESFs in all cardiac cycles were automatically detected to compute LVEF. The MAEF-Net method used a multi-attention mechanism to guide the network to capture heartbeat features effectively, while sup-pressing noise, and incorporated deep supervision mechanism and spatial pyramid feature fusion to enhance feature extraction capabilities. The proposed method was validated on the public EchoNet-Dynamic dataset (n = 1226). The Dice similarity coefficient (DSC) of the left ventricular segmentation reached (93.10 +/- 2.22)%, and the mean absolute error (MAE) of cardiac phase detection was (2.36 +/- 2.23) frames. The MAE for predicting LVEF was 6.29 %. The proposed method was also validated on a private clinical dataset (n = 22). The DSC of the left ventricular segmentation reached (92.81 +/- 2.85)%, and the MAE of cardiac phase detection was (2.25 +/- 2.27) frames. The MAE for predicting LVEF was 5.91 %, and the Pearson correlation coefficient r reached 0.96. The proposed method may be used as a new method for automatic left ventricular segmentation and quantitative analysis in two-dimensional echocardiography. Our code and trained models will be made available publicly at https://github.com/xiaojinmao-code/MAEF-Net.
Keyword ：
Ejection fraction Ejection fraction Left ventricular segmentation Left ventricular segmentation Cardiac phase detection Cardiac phase detection Deep learning Deep learning Echocardiography Echocardiography
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| GB/T 7714 | Zeng, Yan , Tsui, Po-Hsiang , Pang, Kunjing et al. MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography [J]. | ULTRASONICS , 2023 , 127 . |
| MLA | Zeng, Yan et al. "MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography" . | ULTRASONICS 127 (2023) . |
| APA | Zeng, Yan , Tsui, Po-Hsiang , Pang, Kunjing , Bin, Guangyu , Li, Jiehui , Lv, Ke et al. MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography . | ULTRASONICS , 2023 , 127 . |
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Abstract ：
The homodyned-K (HK) distribution is a generalized model of envelope statistics whose parameters alpha (the clustering parameter) and k (the coherent-to-diffuse signal ratio) can be used to monitor the thermal lesions. In this study, we proposed an ultrasound HK contrast-weighted summation (CWS) parametric imaging algorithm based on the H-scan technique and investigated the optimal window side length (WSL) of the HK parameters estimated by the XU estimator (an estimation method based on the first moment of the intensity and two log-moments, which was used in the proposed algorithm) through phantom simulations. H-scan diversified ultrasonic backscattered signals into low- and high-frequency passbands. After envelope detection and HK parameter estimation for each frequency band, the alpha and k parametric maps were obtained, respectively. According to the contrast between the target region and background, the (alpha or k) parametric maps of the dual-frequency band were weighted and summed, and then the CWS images were yielded by pseudo-color imaging. The proposed HK CWS parametric imaging algorithm was used to detect the microwave ablation coagulation zones of porcine liver ex vivo under different powers and treatment durations. The performance of the proposed algorithm was compared with that of the conventional HK parametric imaging and frequency diversity and compounding Nakagami imaging algorithms. For two-dimensional HK parametric imaging, it was found that a WSL equal to 4 pulse lengths of the transducer was sufficient for estimating the alpha and k parameters in terms of both parameter estimation stability and parametric imaging resolution. The HK CWS parametric imaging provided an improved contrast-to-noise ratio over conventional HK parametric imaging, and the HK alpha(cws) parametric imaging achieved the best accuracy and Dice score of coagulation zone detection.
Keyword ：
contrast-weighted summation contrast-weighted summation homodyned-K imaging homodyned-K imaging microwave ablation microwave ablation quantitative ultrasound quantitative ultrasound H-scan H-scan
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| GB/T 7714 | Li, Sinan , Zhou, Zhuhuang , Wu, Shuicai et al. Ultrasound Homodyned-K Contrast-Weighted Summation Parametric Imaging Based on H-scan for Detecting Microwave Ablation Zones [J]. | ULTRASONIC IMAGING , 2023 , 45 (3) : 119-135 . |
| MLA | Li, Sinan et al. "Ultrasound Homodyned-K Contrast-Weighted Summation Parametric Imaging Based on H-scan for Detecting Microwave Ablation Zones" . | ULTRASONIC IMAGING 45 . 3 (2023) : 119-135 . |
| APA | Li, Sinan , Zhou, Zhuhuang , Wu, Shuicai , Wu, Weiwei . Ultrasound Homodyned-K Contrast-Weighted Summation Parametric Imaging Based on H-scan for Detecting Microwave Ablation Zones . | ULTRASONIC IMAGING , 2023 , 45 (3) , 119-135 . |
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