{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T10:30:24Z","timestamp":1770892224240,"version":"3.50.1"},"reference-count":64,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:p> Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal organs due to the advantages of being real-time and radiation-free. However, due to inter-operator variations, resulting images highly depend on the experience of sonographers. This work proposes an intelligent robotic sonographer to autonomously \u201cexplore\u201d target anatomies and navigate a US probe to standard planes by learning from the expert. The underlying high-level physiological knowledge from experts is inferred by a neural reward function, using a ranked pairwise image comparison approach in a self-supervised fashion. This process can be referred to as understanding the \u201clanguage of sonography.\u201d Considering the generalization capability to overcome inter-patient variations, mutual information is estimated by a network to explicitly disentangle the task-related and domain features in latent space. The robotic localization is carried out in coarse-to-fine mode based on the predicted reward associated with B-mode images. To validate the effectiveness of the proposed reward inference network, representative experiments were performed on vascular phantoms (\u201cline\u201d target), two types of ex vivo animal organ phantoms (chicken heart and lamb kidney representing \u201cpoint\u201d target), and in vivo human carotids. To further validate the performance of the autonomous acquisition framework, physical robotic acquisitions were performed on three phantoms (vascular, chicken heart, and lamb kidney). The results demonstrated that the proposed advanced framework can robustly work on a variety of seen and unseen phantoms as well as in vivo human carotid data. Code: https:\/\/github.com\/yuan-12138\/MI-GPSR . Video: https:\/\/youtu.be\/u4ThAA9onE0 . <\/jats:p>","DOI":"10.1177\/02783649231223547","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T12:11:24Z","timestamp":1705407084000},"page":"981-1002","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":30,"title":["Intelligent robotic sonographer: Mutual information-based disentangled reward learning from few demonstrations"],"prefix":"10.1177","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7461-2200","authenticated-orcid":false,"given":"Zhongliang","family":"Jiang","sequence":"first","affiliation":[{"name":"The Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany"}]},{"given":"Yuan","family":"Bi","sequence":"additional","affiliation":[{"name":"The Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany"}]},{"given":"Mingchuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"The College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China"}]},{"given":"Ying","family":"Hu","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]},{"given":"Michael","family":"Burke","sequence":"additional","affiliation":[{"name":"The Department of Electrical and Computer Systems Engineering, Monash University, Clayton, AU-VIC, Australia"}]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[{"name":"The Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany"},{"name":"The Laboratory for Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, 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