Super-Resolution Ultrasound Through Sparsity-Based Deconvolution and Multi-Feature Tracking

反褶积 帧速率 计算机科学 人工智能 计算机视觉 卡尔曼滤波器 特征(语言学) 图像分辨率 跟踪(教育) 气泡 时间分辨率 帧(网络) 模式识别(心理学) 算法 光学 物理 哲学 电信 并行计算 语言学 教育学 心理学
作者
Jipeng Yan,Tao Zhang,Jacob Broughton-Venner,Pintong Huang,Meng‐Xing Tang
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:41 (8): 1938-1947 被引量:30
标识
DOI:10.1109/tmi.2022.3152396
摘要

Ultrasound super-resolution imaging through localisation and tracking of microbubbles can achieve sub-wave-diffraction resolution in mapping both micro-vascular structure and flow dynamics in deep tissue in vivo. Currently, it is still challenging to achieve high accuracy in localisation and tracking particularly with limited imaging frame rates and in the presence of high bubble concentrations. This study introduces microbubble image features into a Kalman tracking framework, and makes the framework compatible with sparsity-based deconvolution to address these key challenges. The performance of the method is evaluated on both simulations using individual bubble signals segmented from in vivo data and experiments on a mouse brain and a human lymph node. The simulation results show that the deconvolution not only significantly improves the accuracy of isolating overlapping bubbles, but also preserves some image features of the bubbles. The combination of such features with Kalman motion model can achieve a significant improvement in tracking precision at a low frame rate over that using the distance measure, while the improvement is not significant at the highest frame rate. The in vivo results show that the proposed framework generates SR images that are significantly different from the current methods with visual improvement, and is more robust to high bubble concentrations and low frame rates.
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