11 citations · 24 across the 5 of their papers we have counts for
8 papers
Needle tip force estimation by deep learning from raw spectral OCT data
M. Gromniak, N. Gessert, T. Saathoff +1
Purpose. Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside th…
Deep Learning for High Speed Optical Coherence Elastography
Maximilian Neidhardt, Marcel Bengs, Sarah Latus +3
Mechanical properties of tissue provide valuable information for identifying lesions. One approach to obtain quantitative estimates of elastic properties is shear wave elastography…
High-Speed Markerless Tissue Motion Tracking Using Volumetric Optical Coherence Tomography Images
Matthias Schlüter, Lukas Glandorf, Johanna Sprenger +4
Modern optical coherence tomography (OCT) devices provide volumetric images with micrometer-scale spatial resolution and a temporal resolution beyond video rate. In this work, we a…
Bimodal intravascular volumetric imaging combining OCT and MPI
Sarah Latus, Florian Griese, Matthias Schlüter +6
Intravascular optical coherence tomography (IVOCT) is a catheter based image modality allowing for high resolution imaging of vessels. It is based on a fast sequential acquisition…
Spatio-Temporal Deep Learning Models for Tip Force Estimation During Needle Insertion
Nils Gessert, Torben Priegnitz, Thore Saathoff +6
Purpose. Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and…
Feasibility of a markerless tracking system based on optical coherence tomography
Matthias Schlüter, Christoph Otte, Thore Saathoff +2
Clinical tracking systems are popular but typically require specific tracking markers. During the last years, scanning speed of optical coherence tomography (OCT) has increased to…