Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA.
Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA.
Sci Rep. 2024 Oct 30;14(1):26161. doi: 10.1038/s41598-024-77610-4.
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 learning network comprising depth-wise separable convolutional layers and hybrid adaptive and squeeze-and-excite attention mechanisms to correct inter-frame motion in high-frame-rate images. Rigorous validation using phantom and in-vivo data with simulated inter-frame motion indicates average improvements of 35% in Pearson correlation coefficients (PCCs) between motion corrected and reference data with respect to that of motion corrupted data. Further, reconstruction of microvasculature images using motion-corrected frames demonstrates PCC improvement from 31 to 35%. Another thorough validation using in-vivo thyroid data with physiological inter-frame motion demonstrates average improvement of 20% in PCC and 40% in mean inter-frame correlation. Finally, comparison with the conventional image registration method indicates the suitability of proposed network for real-time inter-frame motion correction with 5000 times reduction in motion corrected frame prediction latency.
无造影超声定量微血管成像在多个应用中具有应用前景,包括评估良恶性病变。然而,运动是成像易受生理运动影响的器官中肿瘤微血管的主要挑战之一。本研究旨在解决由于人甲状腺靠近颈动脉而导致的潜在的血管内微脉管图像退化问题。颈动脉的搏动会引起帧间运动,从而显著降低微血管图像的质量,导致诊断错误。本研究的主要目的是减少高帧率超声成像中的帧间运动伪影,以更准确地可视化肿瘤微血管特征。我们提出了一种低复杂度的深度学习网络,包括深度可分离卷积层和混合自适应和挤压激发注意力机制,以校正高帧率图像中的帧间运动。使用带有模拟帧间运动的体模和体内数据进行严格验证表明,与运动损坏数据相比,运动校正和参考数据之间的皮尔逊相关系数(PCC)平均提高了 35%。此外,使用运动校正后的帧重建微血管图像可将 PCC 从 31 提高到 35%。使用带有生理帧间运动的体内甲状腺数据进行的另一次彻底验证表明,PCC 平均提高了 20%,帧间相关性提高了 40%。最后,与传统的图像配准方法相比,表明所提出的网络适用于实时帧间运动校正,运动校正帧预测延迟减少了 5000 倍。