QUPS: Quick Ultrasound Processing & Simulation

Description

QUPS (pronounced "CUPS") is an abstract, lightweight, readable tool for prototyping pulse-echo ultrasound systems and algorithms. It provides a flexible, high-level representation of transducers, pulse sequences, imaging regions, and scattering media as well as hardware accelerated implementations of common signal processing functions for pulse-echo ultrasound systems. QUPS can interface with multiple other Ultrasound acquisition, simulation and processing tools including Verasonics, k-Wave, MUST, FieldII and USTB.

This package can readily be used to develop new transducer array designs by specifying element positions and orientations or develop new pulse sequence designs by specifying waveforms, element delays, and element weights (apodization). Simulating the received echoes (channel data) is supported for any valid UltrasoundSystem. Define custom properties or overload the built-in classes to create new types.

Features

• Flexible:

  • 3D space implementation
  • Transducers: Arbitrary transducer positions and orientations
  • Sequences: Arbitrary transmit waveform, delays, and apodization
  • Scans (Image Domain): Arbitrary pixel locations and beamforming apodization

• Performant:

  • Beamform a 1024 x 1024 image for 256 x 256 transmits/receives in < 2 seconds (RTX 3070)
  • Hardware acceleration via CUDA (Nvidia) or OpenCL (AMD, Apple, Intel, Nvidia), or natively via the Parallel Computing Toolbox
  • Memory efficient classes and methods such as separable beamforming delay and apodization ND-arrays minimize data storage and computational load
  • Batch simulations locally via parcluster or scale to a cluster with the MATLAB Parallel Server (optional) toolbox.

• Modular:

  • Transducer, pulse sequence, pulse waveform, scan region etc. each defined separately
  • Optionally overload classes to customize behaviour
  • Easily compare sequence to sequence or transducer to transducer
  • Simulate with MUST, FieldII, or k-Wave without redefining most parameters
  • Export or import data between USTB or Verasonics data structures

• Intuitive:

  • Native MATLAB semantics with argument validation and tab auto-completion
  • Overloaded plot and imagesc functions for data visualization
  • Documentation via help and doc

Installation

MATLAB R2023b+ & git

Starting in MATLAB R2023b+, QUPS and most of it's extension packages can be installed from within MATLAB via buildtool if you have setup git for MATLAB.

1. Install qups

   gitclone("https://github.com/thorstone25/qups.git");
   cd qups;

2. (optional) Install and patch extension packages and compile mex and CUDA binaries (failures can be safely ignored)

   buildtool install patch compile -continueOnFailure

3. (optional) Run tests (~10 min)

   buildtool test

Legacy Installation

If the above procedure does not work for you, you can manually download and install each extension.

1. Download the desired extension packages into a folder adjacent to the "qups" folder e.g. if qups is located at /path/to/my/qups, kWave should be downloaded to an adjacent folder /path/to/my/kWave.

2. Create a MATLAB Project and add the root folder of the extension to the path e.g. /path/to/my/kWave.

   • Note: The "prj" file in USTB is a Toolbox file, not a Project file - you will still need to make a new Project.

3. Open the Qups.prj project and add each extension package as a reference.

4. (optional) Apply patches to enable further parallel processing.

5. (optional) Run tests via the runProjectTests() function in the build directory.

   addpath build; runProjectTests('verbosity', 'Concise'),

Extensions

All extensions to QUPS are optional, but must be installed separately from their respective sources.

Quick Start

1. Start MATLAB R2020b or later and open the Project

   openProject .

2. (optional) Setup any available acceleration

   setup parallel CUDA cache; % setup the environment with any available acceleration

3. Create an ultrasound system and point scatterer to simulate

   scat = Scatterers('pos', 1e-3*[0 0 30]'); % a single point scatterer at 20mm depth
   xdc = TransducerArray.P4_2v(); % simulate a Verasonics L11-5v transducer
   seq = Sequence('type', 'FSA', 'numPulse', xdc.numel); % full synthetic-aperture pulse sequence
   scan = ScanCartesian('x', 1e-3*[-10, 10], 'z', 1e-3*[25 35]); % set the image boundaries - we'll set the resolution later
   us = UltrasoundSystem('xdc', xdc, 'seq', seq, 'scan', scan, 'fs', 4*xdc.fc); % create a system description
   [us.scan.dx, us.scan.dz] = deal(us.lambda / 4); % set the imaging resolution based on the wavelength

4. Display the geometry

   figure; plot(us); hold on; plot(scat, 'cx'); % plot the ultrasound system and the point scatterers

5. Simulate channel data

   
   chd = greens(us, scat); % create channel data using a shifted Green's function (CUDA/OpenCL-enabled)
   % chd = calc_scat_multi(us, scat); %  ... or with FieldII
   % chd = kspaceFirstOrder(us, scat); % ... or with k-Wave (CUDA-enabled)
   % chd = simus(us, scat); %            ... or with MUST   (CUDA-enabled)

6. Display the channel data 

figure; imagesc(chd); dbr echo 60; animate(chd.data, 'loop', false, 'title', "Tx: "+(1:chd.M));

![](fig/README/channel_data.gif)

7. Beamform

b = DAS(us, hilbert(chd));

8. Display the B-mode image

figure; imagesc(us.scan, b); dbr b-mode 60; title('B-mode image');

![](fig/README/point-target.png)

## Documentation
QUPS is documented within MATLAB. To see all the available classes, use `help ./src` or `doc ./src` from within the QUPS folder. Use `help` or `doc` on any class or method with `help classname` or `help classname.methodname` e.g. `help UltrasoundSystem.DAS`.

For a walk through of going from defining a simulation to a beamformed image, see [example.mlx](example.mlx) (or [example_.m](example_.m)).

See the [examples](examples/) folder for examples of specific applications.

For syntax examples for each class, see [cheat_sheet.m](cheat_sheet.m).

For further documentation on customizing classes, see the class structure [README](src/README.md).

If you have trouble, please submit an [issue](https://github.com/thorstone25/qups/issues).

## Citation
If you use this software, please cite this repository using the [citation file](CITATION.cff) or via the menu option in the "About" section of the [github page](github.com/thorstone25/qups).

If you use any of the extensions, please see their citation policies:
* [FieldII](https://www.field-ii.dk/?background.html)
* [MUST](https://www.biomecardio.com/MUST/documentation.html)
* [MatCL](https://github.com/IANW-Projects/MatCL?tab=readme-ov-file#reference) (via Matlab-OpenCL)
* [k-Wave](https://github.com/ucl-bug/k-wave?tab=readme-ov-file#license)
* [USTB](https://www.ustb.no/citation/)

## Parallel Processing with External Packages
Some QUPS methods, including most simulation and beamforming methods, can be parallelized natively by specifying a `parcluster` or launching a `parallel.ProcessPool` or  ideally a `parallel.ThreadPool`. However, restrictions apply. 

Workers in a `parallel.ThreadPool` cannot call mex functions, use GUIs or user inputs, or perform any file operations (reading or writing) [before R2024a](https://www.mathworks.com/help/parallel-computing/release-notes.html#mw_c7230d70-f9e0-4600-8c6b-3e47ed5396c2). Workers in a `parallel.ProcessPool` or `parcluster` do not have these restrictions, but tend to be somewhat slower and require much more memory. All workers are subject to [race conditions](https://en.wikipedia.org/wiki/Race_condition). 

Removing race conditions and inaccesible functions in the extension packages will enable native parallelization. The patches described below are applied automatically with the "[patch](https://github.com/thorstone25/qups/edit/main/README.md#matlab-r2023b--git)" task via buildtool. Otherwise, you will need to apply the patches manually to enable parallelization.

### [FieldII](https://www.field-ii.dk/) 
FieldII uses [mex](https://www.mathworks.com/help/matlab/call-mex-file-functions.html) functions for all calls, which requires file I/O. This **cannot** be used with a `parallel.ThreadPool`, but can easily be used with a `parallel.ProcessPool` or `parcluster`.

###  [k-Wave](http://www.k-wave.org/index.php) (with binaries)
To enable simulating multiple transmits simultaneously using k-Wave binaries, the temporary filename race condition in `kspaceFirstOrder3DC.m` must be remedied. 
Edit `kspaceFirstOrder3DC.m` and look for an expression setting the temporary folder `data_path = tempdir`. Replace this with `data_path = tempname; mkdir(data_path);` to create a new temporary directory for each worker. 
You may also want to delete this folder after the temporary files are deleted. Record a variable `new_path = true;` if a new directory was created, and place `if new_path, rmdir(data_path); end` at the end of the function. Otherwise, the temporary drive is cleared when the system reboots.

On Linux, the filesystem does not deallocate deleted temporary files until MATLAB is closed. This can lead to write erros if many large simulations are run in the same MATLAB session. To avoid this issue, within `kspaceFirstOrder3DC.m`, set the file size of the temporary input/output files to 0 bytes prior to deleting them, e.g.

if isunix % tolerate deferred deletion for parpools on linux system("truncate -s 0 " + input_filename ); system("truncate -s 0 " + output_filename); end delete(input_filename ); delete(output_filename);

### [MUST](https://www.biomecardio.com/MUST/documentation.html)
To enable the usage of a `parallel.ThreadPool` with the `simus()` method, the GUI and file I/O calls used in the `AdMessage` and `MUSTStat` functions must not be called from `pfield.m` and/or `pfield3.m` (see [#2](https://github.com/thorstone25/qups/issues/2)). It is safe to comment out the advertising and statistics functions.

### [Matlab-OpenCL](github.com/thorstone25/Matlab-OpenCL)
OpenCL support is provided via [Matlab-OpenCL](github.com/thorstone25/Matlab-OpenCL), but is only tested on Linux. This package relies on [MatCL](https://github.com/IANW-Projects/MatCL), but the underlying OpenCL installation is platform and OS specific. The following packages and references may be helpful, but are not tested for compatability.

##### Ubuntu 22.04: 
| Command  | Description |
| ------- | ------------- |
| `sudo apt install opencl-headers`                    | Compilation header files (req'd for all devices)|
| `sudo apt install pocl-opencl-icd`                   | Most CPU devices |
| `sudo apt install intel-opencl-icd`                  | Intel Graphics devices |
| `sudo apt install nvidia-driver-xxx`                 | Nvidia Graphics devices (included with the driver) |
| `sudo apt install ./amdgpu-install_x.x.x-x_all.deb`  | AMD Discrete Graphics devices (see [here](https://docs.amd.com/projects/install-on-linux/en/latest/how-to/amdgpu-install.html) or [here](https://docs.amd.com/projects/install-on-linux/en/latest/how-to/native-install/ubuntu.html))|

### CUDA Support
Starting in R2023a, CUDA support is provided by default within MATLAB via [`mexcuda`](https://www.mathworks.com/help/parallel-computing/mexcuda.html).

Otherwise, for CUDA to work, `nvcc` must succesfully run from the MATLAB environment. If a Nvidia GPU is available and `setup CUDA cache` completes with no warnings, you're all set! If you have difficulty getting nvcc to work in MATLAB, you may need to figure out which environment paths are required for _your_ CUDA installation. Running `setup CUDA` will attempt to do this for you, but may fail if you have a custom installation.

#### Linux
First, be sure you can run `nvcc` from a terminal or command-line interface per [CUDA installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html). Then set the `MW_NVCC_PATH` environmental variable within MATLAB by running `setenv('MW_NVCC_PATH', YOUR_NVCC_BIN_PATH);` prior to running `setup CUDA`. You can run `which nvcc` within a terminal to locate the installation directory. For example, if `which nvcc` returns `/opt/cuda/bin/nvcc`, then run `setenv('MW_NVCC_PATH', '/opt/cuda/bin');`.

#### Windows
First, setup your system for CUDA per [CUDA installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html). On Windows you must set the path for both CUDA and the _correct_ MSVC compiler for C/C++. Start a PowerShell terminal within Visual Studio. Run `echo %CUDA_PATH%` to find the base CUDA_PATH and run `echo %VCToolsInstallDir%` to find the MSVC path. Then, in MATLAB, set these paths with `setenv('MW_NVCC_PATH', YOUR_CUDA_BIN_PATH); setenv('VCToolsInstallDir', YOUR_MSVC_PATH);`, where `YOUR_CUDA_BIN_PATH` is the path to the `bin` folder in the `CUDA_PATH` folder. Finally, run `setup CUDA`. From here the proper paths should be added.