基于改進(jìn)型多模態(tài)信息融合深度強(qiáng)化學(xué)習(xí)的自主超聲掃描方法

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Autonomous ultrasound scanning method based on improved multimodal information fusion and deep reinforcement learning

Xu Jiakaia，Lu Q1υ? ，Li Xiangyunb，Li Kanga，c （a.ColegefclcnUeibelCte pital，Sichuan University，Chengdu 610o65，China）

Abstract：Toaddressthe isses of low training accuracy，prolonged trainingtime，and lowsuccess rateof scanning tasks in ultrasound scanning basedondeep reinforcementlearning（DRL），this paper proposed an autonomous ultrasound scaning method basedonimproved multimodalinformation fusionandDRL.Firstly，the methodintegratedultrasound images，dualview probe manipulation images，and 6D tactile feedback to provide comprehensive multimodal perception.To accurately capturespatiotemporal informationinmultimodaldataandachieveeficientfeaturefusion，thispaperdesignedamultimodal featureextractionandfusionmodulebasedontheself-atentionmechanism（SA）.Secondly，itformulatedthe6Dposedecisionmaking task fortherobotasaDRLproblem.Andthis paperdesignedahybridrewardfunction toemulatetoprofesionalultrasonographers.Lastly，to addresslocaloptima andslowconvergence inDRL training，this paper introduced the DSAC-PERDP algorithm.Tests inreal environmentsdemonstrate thattheproposed method improves scanning accuracy，task successrate， and training speed by 49.8% ， 13.4% and 260.0% ，respectively，compared to baseline models.Moreover，the method maintainsrobust performanceunder interferenceconditions.Thesefindingsvalidatethattheproposedapproach notonlysignificantlyimprovesscanningaccuracy，task succssrate，and trainingefciencybutalsoexhibitsnotableanti-interferencecapabilities.

Key words：autonomous ultrasound scanning；deepreinforcement learning（DRL）；multimodal；self-atention mechanism; DSAC-PERDP algorithm

0 引言

醫(yī)學(xué)超聲因其非侵入性、安全性和成本效益而成為廣泛應(yīng)用的診斷工具[1，2]，常用于腎臟、肝臟、心臟病學(xué)、產(chǎn)科等多個(gè)醫(yī)學(xué)領(lǐng)域[3，4]。（剩余19202字）