| [139386] |
| Title: 4D Deep learning for real-time volumetric optical coherence elastography. <em>International Journal of Computer Assisted Radiology and Surgery 2020</em> |
| Written by: M. Neidhardt and M. Bengs and S. Latus and M. Schlüter and T. Saathoff and A. Schlaefer |
| in: <em>International Journal of Computer Assisted Radiology and Surgery</em>. (2020). |
| Volume: Number: |
| on pages: 1861-6429 |
| Chapter: |
| Editor: |
| Publisher: |
| Series: |
| Address: |
| Edition: |
| ISBN: |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: 10.1007/s11548-020-02261-5 |
| URL: https://doi.org/10.1007/s11548-020-02261-5 |
| ARXIVID: |
| PMID: |
Note:
Abstract: Elasticity of soft tissue provides valuable information to physicians during treatment and diagnosis of diseases. A number of approaches have been proposed to estimate tissue stiffness from the shear wave velocity. Optical coherence elastography offers a particularly high spatial and temporal resolution. However, current approaches typically acquire data at different positions sequentially, making it slow and less practical for clinical application