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# Computational Ultrasound Imaging Toolbox for [MATLAB](mathworks-url)
# Computational Ultrasound Imaging Toolbox for [MATLAB][mathworks-url]

[mathworks-url]: https://mathworks.com/products/matlab.html

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Develop and
evaluate computational image formation methods for
Expand All @@ -24,20 +31,22 @@ Advances in
electronic miniaturization and
processing power have recently led to
freely programmable ultrasound imaging (UI) systems and
software-based "ultrafast" imaging modes, e.g.
software-based "ultrafast" imaging modes, such as

- coherent plane-wave compounding,
- synthetic aperture imaging, or
- limited-diffraction beam imaging, that
- limited-diffraction beam imaging.

capture large fields of view (FOVs) at
These imaging modes capture
large fields of view (FOVs) at
rates in the kilohertz range.

Standard image formation algorithms, e.g.
Standard image formation algorithms, such as
delay-and-sum (DAS) or
Fourier methods, however, gradually trade
the image quality off against
the frame rate.
Fourier methods, however, increase
the frame rate at
the expense of
the image quality.
They rely on
relatively simple physical models that
do not support
Expand All @@ -49,7 +58,7 @@ these systems.

Computational UI methods leverage
the available processing power for
realistic physical models that include
realistic physical models that reflect
the abilities of
freely programmable UI systems.
They recover
Expand All @@ -65,7 +74,7 @@ an arbitrary incident wave,
2. the subsequent recording of
the resulting echoes via
a fully-sampled transducer array, and
3. their optional mixing into
3. the optional mixing of the recorded echoes into
compound signals.

The toolbox, considering
Expand All @@ -76,11 +85,10 @@ these inverse problems based on
discretized scattering operators and
their adjoints.
These operators map
the relative spatial fluctuations in
compressibility and/or mass density to
the material parameter fluctuations to
the mixed radio frequency voltage signals.

It excels in
The toolbox excels in
the *repetitive* application of
identical scattering operators in
iterative image formation methods and, thus, complements
Expand All @@ -90,7 +98,7 @@ popular simulation tools, e.g.
It compensates
the relatively costly initialization of
a scattering operator by
an extremely fast evaluation.
a fast evaluation.

Typical applications include

Expand All @@ -102,7 +110,7 @@ Typical applications include

Usability and
simplicity were
crucial design paradigms.
design paradigms.
The toolbox enables
the solution of
complex inverse scattering problems with
Expand All @@ -112,12 +120,12 @@ code.
## Main Features

- d-dimensional Euclidean space (d = 2, 3)
- two types of heterogeneous acoustic material parameters: compressibility and mass density
- one type of heterogeneous acoustic material parameter: compressibility
- modular object-oriented design
- arbitrary dispersion relations describing
the combination of
frequency-dependent absorption and
dispersion, e.g.
dispersion, such as
the time-causal model
- arbitrary types of incident waves, including
- steered quasi-plane waves,
Expand All @@ -129,15 +137,15 @@ code.
lq-minimization (convex and nonconvex)
- efficient implementations using
hierarchical matrix factorizations
- multi GPU support via mex / CUDA API
- GPU support via mex / CUDA API

## Current Limitations

- Born approximation (future releases might support Rytov, WKB, Padé, and full wave solutions)
- Born approximation
- linear systems (wave propagation, scattering, transducer behavior)
- pulse-echo mode, i.e. no transmission measurements
- pulse-echo mode (i.e., no transmission measurements)
- half-space with rigid (Neumann) boundary
- symmetric grids (future releases might support the fast multipole method and adaptive cross approximation)
- symmetric grids
- developed and tested in MATLAB R2018b, R2019a, R2020a / CUDA Toolkit v10.1.168 on Ubuntu 12.04/16.04/18.04

## References :notebook:
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