Grube, SarahSarahGrubeLatus, SarahSarahLatusBehrendt, FinnFinnBehrendtRiabova, OleksandraOleksandraRiabovaNeidhardt, MaximilianMaximilianNeidhardtSchlaefer, AlexanderAlexanderSchlaefer2024-10-232024-10-232024-07-13International Journal of Computer Assisted Radiology and Surgery 19 (10): 1975-1981 (2024)https://hdl.handle.net/11420/49821Purpose: Clinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes. Methods: We design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 × 16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation. Results: Our experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning. Conclusion: Our study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.en1861-642920241019751981Springerhttps://creativecommons.org/licenses/by/4.0/Deep learningNeedle tip detectionReal-timeSparse feature learningVolumetric ultrasound imagingMLE@TUHHTechnology::617: Surgery, Regional Medicine, Dentistry, Ophthalmology, Otology, AudiologyJournal Article10.15480/882.1357410.1007/s11548-024-03234-810.15480/882.13574Journal Article