ArticleIEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium2020
Faster motion correction of clinical contrast-enhanced ultrasound imaging using deep learning.
Article in IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning.Scientific reports · 2024Article
- Assessment of Transarterial Chemoembolization Using Super-resolution Ultrasound Imaging and a Rat Model of Hepatocellular Carcinoma.Ultrasound in medicine & biology · 2023Article
- 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
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Authors and funding
5 authors.
Funding
Abstract
Motion artifacts affect the quantification accuracy of the tumor angiogenic network measurements from clinical contrast-enhanced ultrasound (CEUS) images. Reliable motion correction methods can improve image alignments but suffer from long computation times and large memory demands. This research project aims to reduce the time and memory needed for motion correction of clinical images from patients diagnosed with hepatocellular carcinoma (HCC). First, B-mode ultrasound (US) images were acquired using a clinical scanner from 36 patients and processed using a conventional two-stage motion correction strategy. Two channel input data consisting of static and moving B-mode US images were prepared as the training data (
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Registered trials
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