UNICORN: Ultrasound Nakagami Imaging via Score Matching and Adaptation
CoRR(2024)
摘要
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.
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