Abstract
BACKGROUND: Doppler twinkling artifact is a rapid change of colors seen in CFI-mode in the presence of kidney stones and calculi. Therefore, numerous researchers use the twinkling artifact as a diagnostic sign. However, this phenomenon is under-researched, because most assumptions concerning its causes are made based on pure visual observations of the scanners screen leaving the important steps of signal transformation hidden behind the black box curtains of ultrasound machines.
 MATERIALS AND METHODS: Raw radiofrequency ultrasound signals were recorded in the phantom studies. The recorded echoes were received from objects that create the Doppler twinkling artifact and artificial blood vessels and soft tissues imitators. The data were collected between June 2016 and March 2021. Sonomed-500 with the 7.5 L38 and 3.4 C60 probes served as the research machine for the signal capture.
 Data records: We present the database containing raw radiofrequency ultrasound signals from the beam former output of the research ultrasound machine. The dataset consists of CFI and B-mode echoes recorded from twinkling objects. Therefore, this database can be useful for those who test, develop and study ultrasound signal processing algorithms. Furthermore, the database is freely available online. The 10.5 GB database consists of echoes received from five phantoms. Raw radiofrequency signals were stored in the binary files; scanning parameters were stored in text files. The database is available at: https://mosmed.ai/datasets/ultrasound_doppler_twinkling_artifact.
 Code availability: The public can visualize the database content with the specially written program TwinklingDatasetDisplay available at: https://github.com/Center-of-Diagnostics-and-Telemedicine/TwinklingDatasetDisplay.git.
 Usage notes: The database can be used to test and develop signal-processing algorithms, such as wall filtration, velocity estimation, feature extraction, speckle reduction, etc. Furthermore, the public is free to share (copy, distribute, and transmit) and remix (adapt and do derivative works) the dataset considering appropriate credit is given.
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