Multi-Frequency 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) System for the Prostate

成像体模 欠采样 弹性成像 传感器 声学 磁共振弹性成像 横波 计算机科学 生物医学工程 物理 材料科学 超声波 人工智能 光学 剪切(地质) 工程类 复合材料
作者
Tajwar Abrar Aleef,Julio Lobo,Ali Baghani,Shahed K. Mohammed,Hani Eskandari,Hamid Moradi,Robert Rohling,S. Larry Goldenberg,William Morris,S. Sara Mahdavi,Septimiu E. Salcudean
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:42 (11): 3436-3450 被引量:10
标识
DOI:10.1109/tmi.2023.3288468
摘要

This article describes a novel system for quantitative and volumetric measurement of tissue elasticity in the prostate using simultaneous multi-frequency tissue excitation. Elasticity is computed by using a local frequency estimator to measure the three-dimensional local wavelengths of steady-state shear waves within the prostate gland. The shear wave is created using a mechanical voice coil shaker which transmits simultaneous multi-frequency vibrations transperineally. Radio frequency data is streamed directly from a BK Medical 8848 transrectal ultrasound transducer to an external computer where tissue displacement due to the excitation is measured using a speckle tracking algorithm. Bandpass sampling is used that eliminates the need for an ultra-fast frame rate to track the tissue motion and allows for accurate reconstruction at a sampling frequency that is below the Nyquist rate. A roll motor with computer control is used to rotate the transducer and obtain 3D data. Two commercially available phantoms were used to validate both the accuracy of the elasticity measurements as well as the functional feasibility of using the system for in vivo prostate imaging. The phantom measurements were compared with 3D Magnetic Resonance Elastography (MRE), where a high correlation of 96% was achieved. In addition, the system has been used in two separate clinical studies as a method for cancer identification. Qualitative and quantitative results of 11 patients from these clinical studies are presented here. Furthermore, an AUC of 0.87±0.12 was achieved for malignant vs. benign classification using a binary support vector machine classifier trained with data from the latest clinical study with leave one patient out cross-validation.
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