Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning.

ABSTRACT: Contrast-free ultrasound quantitative microvasculature imaging shows promise in several applications, including the assessment of benign and malignant lesions. However, motion represents one of the major challenges in imaging tumor microvessels in organs that are prone to physiological motions. This study aims at addressing potential microvessel image degradation in in vivo human thyroid due to its proximity to carotid artery. The pulsation of the carotid artery induces inter-frame motion that significantly degrades microvasculature images, resulting in diagnostic errors. The main objective of this study is to reduce inter-frame motion artifacts in high-frame-rate ultrasound imaging to achieve a more accurate visualization of tumor microvessel features. We propose a low-complex deep learni

SUBMITTER: Saini M 

PROVIDER: S-EPMC11525680 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature