We present a new deep learning-based approach for dense stereo matching. Compared to previous works, our approach does not use deep learning of pixel appearance descriptors, employing very fast classical matching scores instead. At the same time, our approach uses a deep convolutional network to predict the local parameters of cost volume aggregation process, which in this paper we implement using differentiable domain transform. By treating such transform as a recurrent neural network, we are able to train our whole system that includes cost volume computation, costvolume aggregation (smoothing), and winner-takes-all disparity selection end-to-end. The resulting method is highly efficient at test time, while achieving good matching accuracy. On the KITTI 2015 benchmark, it achieves a result of 6.34% error rate while running at 29 frames per second rate on a modern GPU.

Over the past decade, substantial effort has been directed toward developing ultrasonic systems for medical imaging. With advances in computational power, previously theorized scanning methods such as ultrasound tomography can now be realized. In this paper, we present the design, error analysis, and initial backprojection images from a single element 3D ultrasound tomography system. The system enables volumetric pulse-echo or transmission imaging of distal limbs. The motivating clinical applications include: improving prosthetic fittings, monitoring bone density, and characterizing muscle health. The system is designed as a flexible mechanical platform for iterative development of algorithms targeting imaging of soft tissue and bone. The mechanical system independently controls movement of two single element ultrasound transducers in a cylindrical water tank. Each transducer can independently circle about the center of the tank as well as move vertically in depth. High resolution positioning feedback (~1μm) and control enables flexible positioning of the transmitter and the receiver around the cylindrical tank; exchangeable transducers enable algorithm testing with varying transducer frequencies and beam geometries. High speed data acquisition (DAQ) through a dedicated National Instrument PXI setup streams digitized data directly to the host PC. System positioning error has been quantified and is within limits for the imaging requirements of the motivating applications.

We propose a new method for strain field estimation in quasi-static ultrasound elastography based on matching RF data frames of compressed tissues. The method benefits from using a handheld force-controlled ultrasound probe, which provides the contact force magnitude and therefore improves repeatability of displacement field estimation. The displacement field is estimated in a two-phase manner using triplets of RF data frames consisting of a pre-compression image and two post-compression images obtained with lower and higher compression ratios. First, a reliable displacement field estimate is calculated for the first post-compression frame. Second, we use this displacement estimate to warp the second post-compression frame while using linear elasticity to obtain an initial approximation. Final displacement estimation is refined using the warped image. The two-phase displacement estimation allows for higher compression ratios, thus increasing the practical resolution of the strain estimates. The strain field is computed from a displacement field using a smoothness- regularized energy functional, which takes into consideration local displacement estimation quality. The minimization is performed using an efficient primal-dual hybrid gradient algorithm, which can leverage the architecture of a graphical processing unit. The method is quantitatively evaluated using finite element simulations. We compute strain estimates for tissue-mimicking phantoms with known elastic properties and finally perform a qualitative validation using in vivo patient data.

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