QUPS

A MATLAB toolbox for ultrasound imaging and simulation

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 minimize data storage and computional 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 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 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.

Extension	Description	Installation Paths	Citation
FieldII	point scatterer simulator	addpath path/to/fieldII	website
k-Wave	distributed medium simulator	addpath path/to/kWave	website
kWaveArray	k-Wave transducer extension	addpath path/to/kWaveArray	forum, paper
MUST	point scatterer simulator	addpath path/to/MUST	website
USTB	signal processing library and toolbox	addpath path/to/USTB	website
Matlab-OpenCL	hardware acceleration	(see README)	website (via MatCL)
CUDA	hardware acceleration	(see CUDA Support)

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.L11_5v(); % 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 (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);
colormap jet; colorbar;
animate(chd.data);

7. Beamform

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

8. Display the B-mode image

figure; imagesc(us.scan, b); % plot the image (in dB when b is complex)
dbr b-mode 60; % ... with 60dB dynamic range
title('B-mode image');

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 (or example_.m).

See the examples folder for examples of specific applications.

For syntax examples for each class, see cheat_sheet.m.

For further documentation on customizing classes, see the class structure README.

If you have trouble, please submit an issue.

Citation

If you use this software, please cite this repository using the citation file or via the menu option in the "About" section of the github page.

If you use any of the extensions, please see their citation policies:

• FieldII
• MUST
• MatCL (via Matlab-OpenCL)
• k-Wave
• USTB

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 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). 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.

Removing race conditions and inaccesible functions in the extension packages will enable native parallelization. The patches described below are applied automatically with the "patch" task via buildtool. Otherwise, you will need to apply the patches manually to enable parallelization.

FieldII

FieldII uses mex 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 (with binaries)

To enable simulating multiple transmits simultaneously using k-Wave binaries, the temporary filename race condition in kspaceFirstOrder3DC.m must be removed. 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

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). It is safe to comment out the advertising and statistics functions.

Matlab-OpenCL

OpenCL support is provided via Matlab-OpenCL, but is only tested on Linux. This package relies on 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 or here)

CUDA Support

Starting in R2023a, CUDA support is provided by default within MATLAB via mexcuda.

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. 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. 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.