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The global monitoring of volume transports of large rivers is a challenging technological problem with a number of important applications. It is highly desirable to use satellite-based remote sensing techniques for consistent worldwide measurements on a regular basis. While water level measurements in rivers with spaceborne altimeters have already been demonstrated a few years ago, instruments for routine current measurements from space have not been available until now. This will change with the advent of along-track interferometric synthetic aperture radars (along-track InSARs) on upcoming satellites such as the German TerraSAR-X, which is scheduled for launch in 2006. In this paper we evaluate the potential of such instruments for current measurements in rivers. We consider fundamental theoretical issues as well as existing InSAR data from the Shuttle Radar Topography Mission (SRTM) and numerical simulation results. We find that currents in the Elbe river estuary (Germany) can be derived from SRTM data quite well. A similar data quality is predicted for TerraSAR-X. We discuss the potential of TerraSAR-X for routine current measurements in rivers and propose the development of a comprehensive data synthesis system which combines water level and current data from satellites and other sources in such a way that a maximum amount of information on rivers is obtained.

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 elasticity value, which neglects information such as the shape of the propagating shear wave or push sequence characteristics. We present 3D spatio-temporal CNNs for fast local elasticity estimation from ultrasound data. This approach is based on retrieving elastic properties from shear wave propagation within small local regions. A large training data set is acquired with a robot from homogeneous gelatin phantoms ranging from 17.42 kPa to 126.05 kPa with various push locations. The results show that our approach can estimate elastic properties on a pixelwise basis with a mean absolute error of 5.01 ± 4.37 kPa. Furthermore, we estimate local elasticity independent of the push location and can even perform accurate estimates inside the push region. For phantoms with embedded inclusions, we report a 53.93% lower MAE (7.50 kPa) and on the background of 85.24% (1.64 kPa) compared to a conventional shear wave method. Overall, our method offers fast local estimations of elastic properties with small spatio-temporal window sizes.

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Soft tissue deformation and ruptures complicate needle placement. However, ruptures at tissue interfaces also contain information which helps physicians to navigate through different layers. This navigation task can be challenging, whenever ultrasound (US) image guidance is hard to align and externally sensed forces are superimposed by friction. Methods: We propose an experimental setup for reproducible needle insertions, applying optical coherence tomography (OCT) directly at the needle tip as well as external US and force measurements. Processing the complex OCT data is challenging as the penetration depth is limited and the data can be difficult to interpret. Using a machine learning approach, we show that ruptures can be detected in the complex OCT data without additional external guidance or measurements after training with multi-modal ground-truth from US and force. Results: We can detect ruptures with accuracies of 0.94 and 0.91 on homogeneous and inhomogeneous phantoms, respectively, and 0.71 for ex-situ tissues. Conclusion: We propose an experimental setup and deep learning based rupture detection for the complex OCT data in front of the needle tip, even in deeper tissue structures without the need for US or force sensor guiding. Significance: This study promises a suitable approach to complement a robust robotic needle placement.

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