8 citations · 12 across the 3 of their papers we have counts for
11 papers
Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep Learning
Maximilian Neidhardt, Marcel Bengs, Sarah Latus +4
Ultrasound shear wave elasticity imaging is a valuable tool for quantifying the elastic properties of tissue. Typically, the shear wave velocity is derived and mapped to an elastic…
A novel optical needle probe for deep learning-based tissue elasticity characterization
Robin Mieling, Johanna Sprenger, Sarah Latus +2
The distinction between malignant and benign tumors is essential to the treatment of cancer. The tissue's elasticity can be used as an indicator for the required tissue characteriz…
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…
In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation
Florian Griese, Sarah Latus, Matthias Schlüter +4
Purpose: Using 4D magnetic particle imaging (MPI), intravascular optical coherence tomography (IVOCT) catheters are tracked in real time in order to compensate for image artifacts…
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…
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods
Nils Gessert, Matthias Schlüter, Sarah Latus +3
Early detection of cancer is crucial for treatment and overall patient survival. In the upper aerodigestive tract (UADT) the gold standard for identification of malignant tissue is…