Deep learning based motion correction in ultrasound microvessel imaging approach improves thyroid nodule classification.

To address inter-frame motion artifacts in ultrasound quantitative high-definition microvasculature imaging (qHDMI), we introduced a novel deep learning-based motion correction technique. This approach enables the derivation of more accurate quantitative biomarkers from motion-corrected HDMI images, improving the classification of thyroid nodules. Inter-frame motion, often caused by carotid artery pulsation near the thyroid, can degrade image quality and compromise biomarker reliability, potentially leading to misdiagnosis. Our proposed technique compensates for these motion-induced artifacts, preserving the fine vascular structures critical for accurate biomarker extraction. In this study, we utilized the motion-corrected images obtained through this framework to derive the quantitative biomarkers and evaluated their effectiveness in thyroid nodule classification. We segregated the dataset according to the amount of motion into low and high motion containing cases based on the inter-frame correlation values and performed the thyroid nodule classification for the high motion containing cases and the full dataset. A comprehensive analysis of the biomarker distributions obtained after using the corresponding motion-corrected images demonstrates the significant differences between benign and malignant nodule biomarker characteristics compared to the original motion-containing images. Specifically, the bifurcation angle values derived from the quantitative high-definition microvasculature imaging (qHDMI) become more consistent with the usual trend after motion correction. The classification results demonstrated that sensitivity remained unchanged for groups with less motion, while improved by 9.2% for groups with high motion. These findings highlight that motion correction helps in deriving more accurate biomarkers, which improves the overall classification performance.