Due to its non-invasive and non-radiating nature, along with its low cost, ultrasound (US) imaging is widely used in medical applications.
Traditional beamforming of medical ultrasound images relies on sampling rates significantly higher than the actual Nyquist rate of the received signals. This results in large amounts of data to store and process, imposing hardware and software challenges on the development of ultrasound machinery and algorithms and impacting the resulting performance.
In our lab, we apply advanced signal processing and AI algorithms to the ultrasound channel data for high-resolution reconstruction, reconstruction from sub-Nyquist sampled channel data, and speed of sound quantitative imaging. We demonstrate high quality recovery from sparse sets of samples acquired from smaller arrays compared to standard US imaging paving the way to WiFi portable ultrasound. Moreover, we use deep networks and speed of sound correction to recover high quality images and quantitative parameters. Using these concepts, we develop software and hardware that implements high performance, portable ultrasound, including ultrasound over WiFi that transmits channel data, a low-rate sampling probe and more.
We developed a compressed ultrasound Imaging method which enables us to reduce the overall data rate by orders of magnitude in order to be able to scan a patient in one geographical location and broadcast the compressed data over wireless. In the demo below we demonstrate a 20-fold data reduction, transmitting the streaming ultrasound raw data over WIFI to another geographical location.
Below is a high level description of the software prototype of an ultrasound imaging setup over WIFI. The prototype uses a Verasonics US machine to create an ultrasound image frame(s), then compresses the data volume in both the temporal and spatial domains, and transfers the data to a remote computer over wireless communication. The remote computer reconstructs the image to its original dimensions and presents it to the user as can be in the below example showing a liver scan.
Wireless Ultrasound Software Demo Setup
The below demo setup reduces the raw data by a factor of 1:21 which translates to a reduction from 1GB per second to 50MB per second. This paves the way to real time transmission of the ultrasound data.
Demo Movie – SAMPL Wireless Ultrasound
Compressed Ultrasound Imaging over WIFI Demo Clip
Wireless Ultrasound Hardware Demo Setup
In addition, we introduce a new US transducer, which implements a combination of both temporal and spatial subsampling at the US probe side. These methods enable forming an ultrasound image using low rate data from a sparse set of array elements.
Temporal and Spatial Subsampling Ultrasound Transducer Prototype
We are also exploring quantitative US. Typical Brightness-mode (B-mode) images do not provide sufficient contrast for certain anatomical structures and have poor physical interpretation. Inverse US techniques, can reconstruct the underlying physical properties of the material such as speed-of-sound (SoS), density, acoustic attenuation, and elasticity. These properties are known to have valuable differentiation capabilities and improve medical diagnosis. For example, speed of sound maps can discern between benign and malignant breast tumors, and tissue density maps can quantify the level of fat and steatosis in the liver.
In the lab, we developed the nonlinear waveform inversion (NWI) algorithm, which is based on representing the wave equation as an RNN, with an architecture determined by the physical acoustic model. This representation enables to apply optimization algorithms borrowed from the deep-learning toolbox and paves the way to very efficient implementation. The generality of this approach allows reconstructing multiple properties simultaneously using a nonlinear acoustics (NLA) model, which better captures the wave's propagation in the human body.
In the Illustration of the simulated medium figure below, the Water is represented in white. The left and right objects are fat and liver, respectively. The tissues and the water have different SoS, density, attenuation, and nonlinearity while the transducer array is located at the top.
In the reconstruction of simulated medium figure above, the first row shows the GT values of the simulated medium. The second row shows the initial values used as input to the inverse algorithm. In the third row presents the obtained reconstructions using NWI. We evaluate the difference using the normalized RMSE metric. Across all maps, white values correspond to the water's properties. The left and right objects are fat and liver respectively. The PML layer applies a gradual attenuation, which increases close to the boundaries of the grid, as shown in the attenuation maps.
O. Drori, A. Mamistvalov, O. Solomon, and Y. C. Eldar, "Compressed Ultrasound Imaging: from Sub-Nyquist Rates to Super-Resolution", IEEE BITS, the Information Theory Magazine, vol. 1, issue 1, pp. 27-44, August 2021.
A. Mamistvalov, A. Amar, N. Kessler and Y. C. Eldar, "Deep-Learning Based Adaptive Ultrasound Imaging from Sub-Nyquist Channel Data", Submitted to IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, August 2020.
A. Mamistvalov and Y. C. Eldar, "Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Images", IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, vol. 68, issue 12, pp. 3484-3496, December 2021.
R. Cohen and Y. C. Eldar, "Sparse Convolutional Beamforming for Ultrasound Imaging", IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 65, issue 12, pp. 2390-2406, December 2018.
T. Chernyakova and Y. C. Eldar, "Fourier Domain Beamforming: The Path to Compressed Ultrasound Imaging", IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 61, issue 8, pp. 1252-1267, July 2014. Best Paper Award.
A. Mamistvalov and Y. C. Eldar, "Compressed Fourier-Domain Convolutional Beamforming for Wireless Ultrasound Imaging", to appear in IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control.