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Here, a post-processing framework based on a convolutional neural network (CNN) is proposed to improve the spatial resolution of ultrasonic tracking images.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods:<\/jats:title>\n                <jats:p>The custom ultrasonic tracking system comprised a needle with an integrated fibre-optic ultrasound (US) transmitter and a clinical US probe for receiving those transmissions and for acquiring B-mode US images. For post-processing of tracking images reconstructed from the received fibre-optic US transmissions, a recently-developed framework based on ResNet architecture, trained with a purely synthetic dataset, was employed. A preliminary evaluation of this framework was performed with data acquired from needle insertions in the heart of a fetal sheep <jats:italic>in vivo<\/jats:italic>. The axial and lateral spatial resolution of the tracking images were used as performance metrics of the trained network.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results:<\/jats:title>\n                <jats:p>Application of the CNN yielded improvements in the spatial resolution of the tracking images. In three needle insertions, in which the tip depth ranged from 23.9 to 38.4 mm, the lateral resolution improved from 2.11 to 1.58 mm, and the axial resolution improved from 1.29 to 0.46 mm.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion:<\/jats:title>\n                <jats:p>The results provide strong indications of the potential of CNNs to improve the spatial resolution of ultrasonic tracking images and thereby to increase the accuracy of needle tip localisation. These improvements could have broad applicability and impact across multiple clinical fields, which could lead to improvements in procedural efficiency and reductions in risk of complications.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-022-02728-7","type":"journal-article","created":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T16:02:40Z","timestamp":1662220960000},"page":"395-399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Enhancement of instrumented ultrasonic tracking images using deep learning"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5455-6854","authenticated-orcid":false,"given":"Efthymios","family":"Maneas","sequence":"first","affiliation":[]},{"given":"Andreas","family":"Hauptmann","sequence":"additional","affiliation":[]},{"given":"Erwin J.","family":"Alles","sequence":"additional","affiliation":[]},{"given":"Wenfeng","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Sacha","family":"Noimark","sequence":"additional","affiliation":[]},{"given":"Anna L.","family":"David","sequence":"additional","affiliation":[]},{"given":"Simon","family":"Arridge","sequence":"additional","affiliation":[]},{"given":"Adrien E.","family":"Desjardins","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,3]]},"reference":[{"key":"2728_CR1","doi-asserted-by":"crossref","unstructured":"Beigi P, Salcudean SE, Ng GC, Rohling R (2020) Enhancement of needle visualization and localization in ultrasound. 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All procedures performed in studies involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted. For this type of study, formal consent is not required. This article does not contain patient data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}