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Purpose The study compares the feasibility of the quantitative volumetric and semi-quantitative approach for quantification of chronic aortic regurgitation (AR) using different imaging modalities. Methods Left ventricular (LV) volumes, regurgitant volumes (RVol) and regurgitant fractions (RF) were assessed retrospectively by 2D, 3D echocardiography and cMRI in 55 chronic AR patients. Semi-quantitative parameters were assessed by 2D echocardiography. Results 22 (40%) patients had mild, 25 (46%) moderate and 8 (14%) severe AR. The quantitative volumetric approach was feasible using 2D, 3D echocardiography and cMRI, whereas the feasibility of semi-quantitative parameters varied considerably. LV volume (LVEDV, LVESV, SVtot) analyses showed good correlations between the different imaging modalities, although significantly increased LV volumes were assessed by cMRI. RVol was significantly different between 2D/3D echocardiography and 2D echocardiography/cMRI but was not significantly different between 3D echocardiography/cMRI. RF was not statistically different between 2D echocardiography/cMRI and 3D echocardiography/cMRI showing poor correlations (r < 0.5) between the different imaging modalities. For AR grading by RF, moderate agreement was observed between 2D/3D echocardiography and 2D echocardiography/cMRI and good agreement was observed between 3D echocardiography/cMRI. Conclusion Semi-quantitative parameters are difficult to determine by 2D echocardiography in clinical routine. The quantitative volumetric RF assessment seems to be feasible and can be discussed as an alternative approach in chronic AR. However, RVol and RF did not correlate well between the different imaging modalities. The best agreement for grading of AR severity by RF was observed between 3D echocardiography and cMRI. LV volumes can be verified by different approaches and different imaging modalities.
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http://dx.doi.org/10.1530/ERP-17-0083 | DOI Listing |
Neural Netw
September 2025
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:
Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFNeural Netw
September 2025
Shanghai Maritime University, Shanghai, 201306, China. Electronic address:
Cross-modal hashing aims to leverage hashing functions to map multimodal data into a unified low-dimensional space, realizing efficient cross-modal retrieval. In particular, unsupervised cross-modal hashing methods attract significant attention for not needing external label information. However, in the field of unsupervised cross-modal hashing, there are several pressing issues to address: (1) how to facilitate semantic alignment between modalities, and (2) how to effectively capture the intrinsic relationships between data, thereby constructing a more reliable affinity matrix to assist in the learning of hash codes.
View Article and Find Full Text PDFJ Occup Environ Hyg
September 2025
Division of Biology, Chemistry, and Materials Science, Office of Science and Engineering Laboratories, US Food and Drug Administration (FDA), Oak Ridge, Tennessee.
This work assesses the current characterization framework of single-use personal protective equipment (PPE) per recognized consensus standards and presents a novel quantitative approach to refining characterization of barrier materials and predicting PPE performance. Scanning electron microscopy (SEM) and image analysis software (Diameter J) were used to examine the microscopic fiber and pore structure of filter layers of surgical N95 filtering facepiece respirators, before and after exposure to chemicals used in decontamination modalities (vaporized hydrogen peroxide or ozone). The effect of porosity on penetration was assessed by bacterial filtration efficiency (BFE) testing.
View Article and Find Full Text PDFJ Vis Exp
August 2025
Department of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa; Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa; Geminii, Inc.
Non-small cell lung cancer (NSCLC) continues to be the number one cause of cancer-related death for both women and men worldwide. More information needs to be gathered to understand the interactions between cancer cells, the immune system, the microenvironment within each tumor, and the host tissue to develop more effective treatment modalities. Reported here is a simple, repeatable method for inducing cancer within the mouse lung, allowing for the monitoring of tumor growth from early to late-stage disease.
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