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Lymph nodes are one of the most important components of the human immune system, and superficial lymph node enlargement lacks specificity.
Ultrasound examination has been widely used in the diagnosis of lymph node lesions and is of great significance in distinguishing between benign and malignant. However, the two-dimensional and Doppler ultrasound features of different types of lymph node lesions overlap and intersect, and the blood flow perfusion information of lymph nodes can provide more information for differentiation.
At present, the widely used contrast-enhanced ultrasound is easier to evaluate blood flow perfusion and can display small blood vessels smaller than 100 microns. The diagnostic accuracy of cervical lymph nodes using contrast-enhanced ultrasound is 80-90%.
However, current contrast-enhanced ultrasound is limited by physical diffraction, with a resolution ranging from sub-millimeter to millimeter. This limitation hinders the visualization of small blood vessels or microcirculation by ultrasound, and parameters such as vascular size, spatial vascular pattern, and velocity of microcirculation are crucial for disease diagnosis and prognosis evaluation. Super resolution ultrasound (SRUS) is a new blood flow imaging technique. By tracking the movement trajectory of micro-bubbles instead of imaging the micro-bubbles themselves, the ultrasound diffraction limit can be exceeded to improve the sensitivity and image resolution of blood flow.
Thus the study aim to evaluate the feasibility of SRUS technology in distinguishing between benign and malignant lymph nodes, and compare the differences in blood flow distribution and perfusion index between benign and malignant lymph nodes under SRUS imaging.
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The study aim to evaluate the feasibility of SRUS technology in distinguishing between benign and malignant lymph nodes, and compare the differences in blood flow distribution, perfusion index, CEUS parameters, blood supply mode between benign and malignant lymph nodes under SRUS imaging.
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779 participants in 2 patient groups
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li g cui
Data sourced from clinicaltrials.gov
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