A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (5/6)

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 5.

Structure

In Vivo Data

Number of Acquisitions: 20,000

Volunteers: Nine volunteers

File Structure: Each volunteer's data is compressed in a separate zip file.

Note: For volunteer 1, due to a higher number of acquisitions, data for this volunteer is distributed across multiple zip files, each containing acquisitions from different body regions.

Regions :

Abdomen: 6599 acquisitions

Neck: 3294 acquisitions

Breast: 3291 acquisitions

Lower limbs: 2616 acquisitions

Upper limbs: 2110 acquisitions

Back: 2090 acquisitions

File Naming Convention: Incremental IDs from acquisition_00000 to acquisition_19999. 

In Vitro Data

Number of Acquisitions: 32 from CIRS model 054G phantom

File Structure: The in vitro data is compressed in the cirs-phantom.zip file.

File Naming Convention: Incremental IDs from invitro_00000 to invitro_00031.

CSV Files

Two CSV files are provided:

invivo_dataset.csv :

Contains a list of all in vivo acquisitions.

Columns: id, path, volunteer id, body region.

invitro_dataset.csv :

Contains a list of all in vitro acquisitions.

Columns: id, path

Zenodo dataset splits and files

The dataset has been divided into six subdatasets, each one published in a separate entry on Zenodo. The following table indicates, for each file or compressed folder, the Zenodo dataset split where it has been uploaded along with its size. Each dataset split is named "A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (ii/6)", where ii represents the split number. This repository contains the 5th split.

File name
Size
Zenodo subdataset number

invivo_dataset.csv
995.9 kB
1

invitro_dataset.csv
1.1 kB
1

cirs-phantom.zip
418.2 MB
1

volunteer-1-lowerLimbs.zip
29.7 GB
1

volunteer-1-carotids.zip
8.8 GB
1

volunteer-1-back.zip
7.1 GB
1

volunteer-1-abdomen.zip
34.0 GB
2

volunteer-1-breast.zip
15.7 GB
2

volunteer-1-upperLimbs.zip
25.0 GB
3

volunteer-2.zip
26.5 GB
4

volunteer-3.zip
20.3 GB
3

volunteer-4.zip
24.1 GB
5

volunteer-5.zip
6.5 GB
5

volunteer-6.zip
11.5 GB
5

volunteer-7.zip
11.1 GB
6

volunteer-8.zip
21.2 GB
6

volunteer-9.zip
23.2 GB
4

Normalized RF Images

Beamforming:

Depth from 1 mm to 55 mm

Width spanning the probe aperture

Grid: 𝜆/8 × 𝜆/8

Resulting images shape: 1483 × 1189

Two beamformed RF images from each acquisition:

Input image: single unfocused acquisition obtained from a single plane wave (PW) steered at 0° (acquisition-xxxx-1PW)

Target image: coherently compounded image from 87 PWs acquisitions steered at different angles (acquisition-xxxx-87PWs)

Normalization:

The two RF images have been normalized

To display the images:

Perform the envelop detection (to obtain the IQ images)

Log-compress (to obtain the B-mode images)

File Format: Saved in npy format, loadable using Python and numpy.load(file).

Training and Validation Split in the paper

For the volunteer-based split used in the paper:

Training set: volunteers 1, 2, 3, 6, 7, 9

Validation set: volunteer 4

Test set: volunteers 5, 8

Images analyzed in the paper

Carotid acquisition (from volunteer 5): acquisition_12397

Back acquisition (from volunteer 8): acquisition_19764

In vitro acquisition: invitro-00030

License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Please cite the original paper when using this dataset :

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. DOI: 10.3390/jimaging9120256

Contact

For inquiries or issues related to this dataset, please contact:

Name: Roser Viñals

Email: [e-mail]