ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2020
Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Harnessing chemically crosslinked microbubble clusters using deep learning for ultrasound contrast imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Advancements in Three-Dimensional Super-Resolution Ultrasound Imaging: A Narrative Review.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2025Review
- Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning.Scientific reports · 2024Article
- Current trends in the characterization and monitoring of vascular response to cancer therapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Super-Resolution Ultrasound Imaging for Monitoring the Therapeutic Efficacy of a Vascular Disrupting Agent in an Animal Model of Breast Cancer.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2024Article
- Comparison of micro-CT image enhancement after use of different vascular casting agents.Quantitative imaging in medicine and surgery · 2024Article
- A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging.Sensors (Basel, Switzerland) · 2023Article
- Super-resolution of 2D ultrasound images and videos.Medical & biological engineering & computing · 2023Article
- Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals.Life (Basel, Switzerland) · 2023Review
- Assessment of Transarterial Chemoembolization Using Super-resolution Ultrasound Imaging and a Rat Model of Hepatocellular Carcinoma.Ultrasound in medicine & biology · 2023Article
- Generalized mathematical framework for contrast-enhanced ultrasound imaging with pulse inversion spectral deconvolution.Ultrasonics · 2023Article
- Three-Dimensional Ultrasound Localization Microscopy with Bipartite Graph-Based Microbubble Pairing and Kalman-Filtering-Based Tracking on a 256-Channel Verasonics Ultrasound System with a 32 × 32 Matrix Array.Journal of medical and biological engineering · 2022Article
- Deep Learning-Based Microbubble Localization for Ultrasound Localization Microscopy.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022Article
- Evaluation of Nonlinear Contrast Pulse Sequencing for Use in Super-Resolution Ultrasound Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
- Faster super-resolution ultrasound imaging with a deep learning model for tissue decluttering and contrast agent localization.Biomedical physics & engineering express · 2021Article
- Multiscale computational modeling of cancer growth using features derived from microCT images.Scientific reports · 2021Article
- 3-D Super-Resolution Ultrasound Imaging for Monitoring Early Changes in Breast Cancer after Treatment with a Vascular-Disrupting Agent.IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium · 2021Article
- Three-dimensional visualization and improved quantification with super-resolution ultrasound imaging - validation framework for analysis of microvascular morphology using a chicken embryo model.Physics in medicine and biology · 2021Article
- Current Development and Applications of Super-Resolution Ultrasound Imaging.Sensors (Basel, Switzerland) · 2021Review
- Faster motion correction of clinical contrast-enhanced ultrasound imaging using deep learning.IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium · 2020Article
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3 authors.
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Abstract
Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures down to tens of micrometers. The image processing methods for the spatiotemporal filtering needed in SR-US, such as singular value decomposition (SVD), are computationally burdensome and performed offline. Deep learning has been applied to many biomedical imaging problems, and trained neural networks have been shown to process an image in milliseconds. The goal of this study was to evaluate the effectiveness of deep learning to realize a spatiotemporal filter in the context of SR-US processing. A 3-D convolutional neural network (3DCNN) was trained on in vitro and in vivo data sets using SVD as ground truth in tissue clutter reduction. In vitro data were obtained from a tissue-mimicking flow phantom, and in vivo data were collected from murine tumors of breast cancer. Three training techniques were studied: training with in vitro data sets, training with in vivo data sets, and transfer learning with initial training on in vitro data sets followed by fine-tuning with in vivo data sets. The neural network trained with in vitro data sets followed by fine-tuning with in vivo data sets had the highest accuracy at 88.0%. The SR-US images produced with deep learning allowed visualization of vessels as small as [Formula: see text] in diameter, which is below the diffraction limit (wavelength of [Formula: see text] at 14 MHz). The performance of the 3DCNN was encouraging for real-time SR-US imaging with an average processing frame rate for in vivo data of 51 Hz with GPU acceleration.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.