Code
spatcov - Fast spatial coherence estimation
Spatial coherence estimation is a useful tool that is used in a variety of applications, most notably in short-lag spatial coherence (SLSC) imaging and in backscatter tensor imaging (BTI). The spatial coherence of a wavefront describes the similarity between any two points along the wavefront, and can be used to infer properties about the source, such as its anisotropy or the lateral source magnitude. It has been used successfully in SLSC imaging to suppress clutter in a variety of applications including fetal, liver, and cardiac imaging.
nn_bmode - Neural network-based echogenicity estimation for ultrasound B-mode imaging
In pulse-echo ultrasound B-mode imaging, images are reconstructed according to the echogenicity of the medium (i.e., "Brightness"-mode). The standard way of reconstructing images with a transducer array is using delay-and-sum beamforming (DAS): time delays are applied to focus the signal at a point in space, and then the complex array signals are averaged together and the magnitude is displayed. DAS is robust and easy to compute, and is used ubiquitously in medical ultrasound imaging. However, most medical imaging targets (e.g., soft tissue) are composed of diffuse, unresolvable microscopic scatterers. Under DAS, the echoes from these scatterers combine stochastically to produce a strong multiplicative noise called speckle. Speckle results in a pattern with high variance, and is only representative of the underlying echogenicity when averaged over multiple speckles.
We recently demonstrated that a simple fully convolutional neural network can be trained to estimate echogenicity using simulations of transducer array signals. The neural network produces ultrasound images with more accurate echogenicities than DAS, as quantified using normalization-independent log-scale versions of the mean absolute error (MAE), mean squared error (MSE), and multi-scale structural similarity (MS-SSIM) metrics.