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  4. A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)
 
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A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)

Vinals Terres, Roser  
•
Thiran, Jean-Philippe  
2024
Zenodo

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning. J. Imaging 2023, 9, 256. https://doi.org/10.3390/jimaging9120256. Please cite the original paper when using this dataset. Due to data size restriction, the dataset has been divided into six subdatasets, each one published into a separate entry in Zenodo. This repository contains subdataset 3.

  • Details
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Type
dataset
DOI
10.5281/zenodo.10608773
ACOUA ID

197f1e40-f75a-4a53-a61b-f3f62e23a1ce

Author(s)
Vinals Terres, Roser  
•
Thiran, Jean-Philippe  
Date Issued

2024

Version

2

Publisher

Zenodo

Subjects

deep learning

•

image reconstruction

•

quality enhancement

•

ultrafast ultrasound imaging

EPFL units
LTS5  
RelationURL/DOI

IsSupplementTo

https://infoscience.epfl.ch/record/307565

IsPartOf

https://infoscience.epfl.ch/record/307555

IsPartOf

https://infoscience.epfl.ch/record/307556
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Available on Infoscience
February 6, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/203491
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