JOURNAL ARTICLE

Ultrasound Localization Microscopy Precision of Clinical 3-D Ultrasound Systems

Stefanie DencksThomas LissonNico ObliszFabian KießlingGeorg Schmitz

Year: 2024 Journal:   IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control Vol: 71 (12: Breaking the Resolution)Pages: 1677-1689   Publisher: Institute of Electrical and Electronics Engineers

Abstract

Ultrasound localization microscopy (ULM) is becoming well established in preclinical applications. For its translation into clinical practice, the localization precision achievable with commercial ultrasound (US) scanners is crucial-especially with volume imaging, which is essential for dealing with out-of-plane motion. Here, we propose an easy-to-perform method to estimate the localization precision of 3-D US scanners. With this method, we evaluated imaging sequences of the Philips Epiq 7 US device using the X5-1 and the XL14-3 matrix transducers and also tested different localization methods. For the X5-1 transducer, the best lateral, elevational, and axial precision was 109, 95, and m for one contrast mode, and 29, 22, and m for the other. The higher frequency XL14-3 transducer yielded precisions of 17, 38, and m using the harmonic imaging mode. Although the center of mass was the most robust localization method also often providing the best precision, the localization method has only a minor influence on the localization precision compared to the impact by the imaging sequence and transducer. The results show that with one of the imaging modes of the X5-1 transducer, precisions comparable to the XL14-3 transducer can be achieved. However, due to localization precisions worse than m, reconstruction of the microvasculature at the capillary level will not be possible. These results show the importance of evaluating the localization precision of imaging sequences from different US transducers or scanners in all directions before using them for in vivo measurements.

Keywords:
Ultrasound Microscopy Ultrasonic imaging Acoustic microscopy Materials science Biomedical engineering Acoustics Computer science Optics Computer vision Physics Engineering

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56
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0.83
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Citation History

Topics

Radiomics and Machine Learning in Medical Imaging
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging
Ultrasound Imaging and Elastography
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging

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