{'fraction_non_alphanumeric': 0.028089887640449437, 'fraction_numerical': 0.0032102728731942215, 'mean_word_length': 5.235, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Alias- free color Doppler with chirps

Ultrafast Doppler imaging is an increasingly used technique to quantify and visualize blood flow dynamics at a high frame rate. However, Doppler imaging is subject to phase wrapping (aliasing) when it comes to large velocities. Blood velocities are bounded by the Nyquist velocity (maximum measurable speed), which depends on the pulse repetition frequency (PRF) and central frequency. Several solutions have been proposed to achieve alias-free Doppler images: 1) post-processing of the Doppler images, some of which are based on deep learning, 2) combination of multiple Doppler fields with different Nyquist velocities, generated using dual-PRF emissions, or dual-frequency Doppler measurements. This work aims to improve the latter by using chirps, which have the double advantage of providing wide frequency bands and giving a better spatial resolution by matched filtering. Our results showed a significant drop in the percentage of errors (up to 12 % in double aliasing) when using chirps over standard windowed sine waves (WSW). Chirps are known to improve spatial resolution in beamforming; our results support their use for Doppler imaging, as they help mitigate velocity ambiguity due to aliasing.