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Next Webinar – Ultrasonics Symposium Challenge: Pulse-Echo Quantitative Ultrasound (QUS)

4 months 2 weeks ago
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The IEEE UFFC Education Committee cordially invites you to participate in our upcoming professional development session:

 

Next Webinar – Ultrasonics Symposium Challenge: Pulse-Echo Quantitative Ultrasound (QUS)

 

The 2026 IUS Symposium, which will be held Oct 4-8, 2026, in Raleigh, North Carolina, will host a technical challenge on Pulse-Echo Quantitative Ultrasound (QUS). This webinar will provide a short introduction to the topic of the challenge, then feature presentations by two early-career researchers in the field.

 

Sponsored by: Ultrasonics Technical Program Committee

 

Host: Jonathan Mamou

 

Speakers: Baptiste Hériard-Dubreuil, Institut Langevin

 

Hayley Marie Whitson, University of Wisconsin – Madison

 

About the Challenge:
Pulse-echo quantitative ultrasound (QUS) is a well-established research field, and studies have demonstrated the diagnostic values of QUS features in a wide range of applications.  Despite increasing interest, important challenges remain, most importantly the need to compensate for the effects of intervening tissues between the transducer and the tissue of interest. To address these challenges, novel QUS methods have been investigated to improve the compensation for attenuation and to reduce the effects of aberration through estimation of the speed of sound. Numerous algorithms and methods exist, and the goal of the Challenge is to compare and evaluate them in a rigorous matter. The proposed challenge will provide (i) a wide platform to bring together research efforts from laboratories across the globe and (ii) a framework to compare the QUS algorithms developed by these laboratories in a uniform and standardized manner. Based on ultrasound data generated from realistic simulations and acquired from phantoms with known acoustical properties, participants will compete to develop the most accurate and precise methods (in terms of bias and variance) and with the best lesion detectability.