摘要:
Medical diagnostic ultrasound methods and systems for automated flow analysis are provided. Multiple cross-sectional areas along a vessel are determined automatically. A processor locates an abnormality as a function of the multiple cross-sectional areas, such as identifying a cross-sectional area that is a threshold amount less than an average cross-sectional area. The abnormal area is highlighted on the display to assist with medical diagnosis. For the carotid artery, the interior and exterior branches are labeled to assist medical diagnosis. The two branches are automatically identified. The branch associated with additional small branches is identified as the exterior carotid.
摘要:
Peak blood velocity measurement for automated stenosis detection is provided. Ultrasound measurements of the peak blood velocity are corrected by a calculation of the Doppler angle, which exists from misalignment of the ultrasound transducer axis and the true blood velocity. The direction of the blood velocity and the Doppler angle are found by imaging a set of planar cross-sections of a blood vessel, such as the carotid artery, to obtain velocity maps of the blood flowing in the blood vessel. Peak blood velocity can be correlated with an amount of stenosis therefore accurate peak blood velocity measurements are necessary for medical diagnosis. Automated stenosis detection allows for implementation in many medical settings. A capacitive micromachined ultrasound transducer array is also provided to measure the planar cross-sectional images.
摘要:
A method to detect and classify a structure of interest in a medical image is provided to enable high specificity without sacrificing the sensitivity of detection. The method is based on representing changes in three-dimensional image data with a vector field, characterizing the topology of this vector field and using the characterized topology of the vector field for classification of a structure of interest. The method could be used as a stand-alone method or as a post-processing method to enhance and classify outputs of a high-sensitivity low-specificity method to eliminate false positives.