Jian-yun Luo - Nanotechnology

Author Information

Jianwen Luo is a distinguished researcher affiliated with the Department of Biomedical Engineering at the School of Medicine, Tsinghua University, Beijing, China. His research primarily focuses on advanced biomedical imaging techniques, particularly in the field of ultrasound imaging. His work has significantly contributed to the development of novel methodologies for medical image analysis and diagnostics.

Research Contributions

Jianwen Luo has made substantial contributions to the field of biomedical engineering, especially in the area of ultrasound imaging. His recent publications reflect a strong emphasis on improving diagnostic techniques through innovative computational methods. Notable works include quantitative analysis of lung ultrasound images for COVID-19 severity assessment, deep learning approaches for breast tumor segmentation, and enhanced power Doppler imaging using advanced filtering techniques. Additionally, his research spans high-frame-rate imaging, ultrasound elastography, and histological image registration, indicating a broad and impactful scope in medical imaging.

Aliases

Jianwen Luo is also known by the aliases Jian-yun Luo and Jianwe Luo in various publications and academic circles.

Publication and Citation Metrics

Metric	Value
Citation Count	5325
h-index	38
Paper Count	324

Publications:

Quantitative Analysis of Pleural Line and B-Lines in Lung Ultrasound Images for Severity Assessment of COVID-19 Pneumonia

DOI: 10.1109/TUFFC.2021.3107598

Year: 2022

Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints

DOI: 10.1016/j.media.2021.102315

Year: 2022

Improved Ultrafast Power Doppler Imaging by Using Spatiotemporal Non-Local Means Filtering

DOI: 10.1109/TUFFC.2022.3158611

Year: 2022

In Vivo assessment of hypertensive nephrosclerosis using ultrasound localization microscopy.

DOI: 10.1002/mp.15583

Year: 2022

Partial Hadamard encoded synthetic transmit aperture for high frame rate imaging with minimall2-norm least squares method.

DOI: 10.1088/1361-6560/ac6202

Year: 2022

Improved Ultrasound Imaging Performance With Complex Cumulant Analysis

DOI: 10.1109/TBME.2022.3141197

Year: 2022

Hadamard-Encoded Synthetic Transmit Aperture Imaging for Improved Lateral Motion Estimation in Ultrasound Elastography

DOI: 10.1109/TUFFC.2022.3148332

Year: 2022

Unsupervised Histological Image Registration Using Structural Feature Guided Convolutional Neural Network.

DOI: 10.1109/TMI.2022.3164088

Year: 2022

Acceleration of reconstruction for compressed sensing based synthetic transmit aperture imaging by using in-phase/quadrature data.

DOI: 10.1016/j.ultras.2021.106576

Year: 2021

ApodNet: Learning for High Frame Rate Synthetic Transmit Aperture Ultrasound Imaging

DOI: 10.1109/TMI.2021.3084821

Year: 2021

What are the Benefits of Investing in Nanotechnology Mutual Funds?

How Do Charts Enhance Data Analysis in Nanotechnology?

What are the Applications of Nanomaterial Based Biosensors?

What Makes Nanofiber Filters Unique?

Why are Hierarchical Relationships Important?

What is Environmental Sensing?

What is the Impact of Nanotechnology on Satellite Technology?

What are the Properties of Nanoscale Fibrils?

What are Trade Imbalances in Nanotechnology?

What Role Does International Cooperation Play in IP Enforcement?

Partnered Content Networks

Relevant Topics