Arterial Distension Monitoring Scheme Using FPGA-Based Inference Machine in Ultrasound Scanner Circuit System

This paper presents an arterial distension monitoring scheme using a field-programmable gate array (FPGA)-based inference machine in an ultrasound scanner circuit system. An arterial distension monitoring requires a precise positioning of an ultrasound probe on an artery as a prerequisite. The propo...

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Veröffentlicht in:IEEE transactions on biomedical circuits and systems 2024-06, Vol.18 (3), p.702-713
Hauptverfasser: Lee, Young-Chan, Ko, Doo-Hyeon, Son, Min-Hyeong, Yang, Se-Hwan, Um, Ji-Yong
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creator Lee, Young-Chan
Ko, Doo-Hyeon
Son, Min-Hyeong
Yang, Se-Hwan
Um, Ji-Yong
description This paper presents an arterial distension monitoring scheme using a field-programmable gate array (FPGA)-based inference machine in an ultrasound scanner circuit system. An arterial distension monitoring requires a precise positioning of an ultrasound probe on an artery as a prerequisite. The proposed arterial distension monitoring scheme is based on a finite state machine that incorporates sequential support vector machines (SVMs) to assist in both coarse and fine adjustments of probe position. The SVMs sequentially perform recognitions of ultrasonic A-mode echo pattern for a human carotid artery. By employing sequential SVMs in combination with convolution and average pooling, the number of features for the inference machine is significantly reduced, resulting in less utilization of hardware resources in FPGA. The proposed arterial distension monitoring scheme was implemented in an FPGA (Artix7) with a resource utilization percentage less than 9.3%. To demonstrate the proposed scheme, we implemented a customized ultrasound scanner consisting of a single-element transducer, an FPGA, and analog interface circuits with discrete chips. In measurements, we set virtual coordinates on a human neck for 9 human subjects. The achieved accuracy of probe positioning inference is 88%, and the Pearson coefficient (r) of arterial distension estimation is 0.838.
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An arterial distension monitoring requires a precise positioning of an ultrasound probe on an artery as a prerequisite. The proposed arterial distension monitoring scheme is based on a finite state machine that incorporates sequential support vector machines (SVMs) to assist in both coarse and fine adjustments of probe position. The SVMs sequentially perform recognitions of ultrasonic A-mode echo pattern for a human carotid artery. By employing sequential SVMs in combination with convolution and average pooling, the number of features for the inference machine is significantly reduced, resulting in less utilization of hardware resources in FPGA. The proposed arterial distension monitoring scheme was implemented in an FPGA (Artix7) with a resource utilization percentage less than 9.3%. To demonstrate the proposed scheme, we implemented a customized ultrasound scanner consisting of a single-element transducer, an FPGA, and analog interface circuits with discrete chips. 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An arterial distension monitoring requires a precise positioning of an ultrasound probe on an artery as a prerequisite. The proposed arterial distension monitoring scheme is based on a finite state machine that incorporates sequential support vector machines (SVMs) to assist in both coarse and fine adjustments of probe position. The SVMs sequentially perform recognitions of ultrasonic A-mode echo pattern for a human carotid artery. By employing sequential SVMs in combination with convolution and average pooling, the number of features for the inference machine is significantly reduced, resulting in less utilization of hardware resources in FPGA. The proposed arterial distension monitoring scheme was implemented in an FPGA (Artix7) with a resource utilization percentage less than 9.3%. To demonstrate the proposed scheme, we implemented a customized ultrasound scanner consisting of a single-element transducer, an FPGA, and analog interface circuits with discrete chips. In measurements, we set virtual coordinates on a human neck for 9 human subjects. The achieved accuracy of probe positioning inference is 88%, and the Pearson coefficient (r) of arterial distension estimation is 0.838.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>38324435</pmid><doi>10.1109/TBCAS.2024.3363134</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-2315-9503</orcidid><orcidid>https://orcid.org/0009-0008-8442-2345</orcidid><orcidid>https://orcid.org/0000-0002-0180-0400</orcidid></addata></record>
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subjects Analog circuits
Arterial distension monitoring
Biomedical monitoring
Blood pressure
Carotid arteries
Carotid Arteries - diagnostic imaging
Carotid Arteries - physiology
Carotid artery
Distension
field programmable gate array (FPGA)
Field programmable gate arrays
Finite state machines
Humans
Image Processing, Computer-Assisted - methods
Inference
Monitoring
Probes
Resource utilization
Scanners
sequential support vector machine (SVM)
Signal Processing, Computer-Assisted - instrumentation
Support Vector Machine
Support vector machines
Ultrasonic imaging
Ultrasonic scanners
Ultrasonography - instrumentation
Ultrasonography - methods
Ultrasound
ultrasound probe positioning
ultrasound scanner
title Arterial Distension Monitoring Scheme Using FPGA-Based Inference Machine in Ultrasound Scanner Circuit System
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