UNICORN: Ultrasound Nakagami Imaging via Score Matching and Adaptation

Author:

Kwanyoung Kim, Jaa-Yeon Lee, Jong Chul Ye

Keyword:

Computer Science, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Medical Physics (physics.med-ph)

journal:

date:

2024-03-10 00:00:00

Abstract

Nakagami imaging holds promise for visualizing and quantifying tissue scattering in ultrasound waves, with potential applications in tumor diagnosis and fat fraction estimation which are challenging to discern by conventional ultrasound B-mode images. Existing methods struggle with optimal window size selection and suffer from estimator instability, leading to degraded resolution images. To address this, here we propose a novel method called UNICORN (Ultrasound Nakagami Imaging via Score Matching and Adaptation), that offers an accurate, closed-form estimator for Nakagami parameter estimation in terms of the score function of ultrasonic envelope. Extensive experiments using simulation and real ultrasound RF data demonstrate UNICORN's superiority over conventional approaches in accuracy and resolution quality.

PDF: UNICORN: Ultrasound Nakagami Imaging via Score Matching and Adaptation.pdf