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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 6

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

Xu Jiakaia
Lu Qib
Li Xiangyunb,c
Li Kanga,c
a. College of Electrical Engineering, b. Sichuan University-Pittsburgh Institute, c. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610065, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0476

Abstract

To address the issues of low training accuracy, prolonged training time, and low success rate of scanning tasks in ultrasound scanning based on deep reinforcement learning (DRL) , this paper proposes an autonomous ultrasound scanning method based on improved multimodal information fusion and DRL. First, the method integrates ultrasound images, dual-view probe manipulation images, and 6D tactile feedback to provide comprehensive multimodal perception. To accurately capture spatiotemporal information in multimodal data and achieve efficient feature fusion, this paper designs a multimodal feature extraction and fusion module based on the self-attention mechanism (SA) . Second, the 6D pose decision-making task for the robot is formulated as a DRL problem, and this paper designs a hybrid reward function to emulate to professional Ultrasonographers. Lastly, to address local optima and slow convergence in DRL training, this paper introduces the DSAC-PERDP (Discrete Soft Actor-Critic with Prioritized Experience Replay based on Dynamic Priority) algorithm. Tests in real environments demonstrate that the proposed method improves scanning accuracy, task success rate, and training speed by 49.8%, 13.4%, and 260.0%, respectively, compared to baseline models. Moreover, the method maintains robust performance under interference conditions. These findings validate that the proposed approach not only significantly improves scanning accuracy, task success rate, and training efficiency but also exhibits notable anti-interference capabilities.

Foundation Support

国家自然科学基金资助项目(51805449,62103291)
四川省科技计划项目(2024YFFK0033,2023YFH0037,2023ZHCG0075,2023YFG0057,2022YFS0021,2022YFH0073)
四川大学华西医院医工交叉融合人才培养基金资助项目
四川大学华西医院1·3·5卓越学科项目(ZYYC21004,ZYJC21081)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0476
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-10] Accepted Paper

Cite This Article

徐加开, 陆奇, 李祥云, 等. 基于改进型多模态信息融合深度强化学习的自主超声扫描方法 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0476. (Xu Jiakai, Lu Qi, Li Xiangyun, et al. Autonomous ultrasound scanning method based on improved multimodal information fusion and deep reinforcement learning [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0476. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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