fast-das 1.0.0

Parallel beamforming wrapper for native code

fast-DAS

Parallel delay-and-sum implementation wrapping native CUDA from python

About

• Supports windows and unix
• Uses CPU-multithreading when use_gpu is False
• Uses NVIDIA CUDA library when use_gpu is True
• Cross-compiled on windows using WSL and CMake

Usage

fast-DAS is a python wrapper and native code implementation for the delay-and-sum algorithm pre-compiled in C++ (CPU version) and CUDA (GPU version).
The goal is to boost the digital beamforming process for ultrasound acquisitions.

Installation

pip install fast-das

Input format

To beamform with fast-DAS, initialize a DAS object based on your compute preference (CPU/GPU):

    from fastDAS import delay_and_sum as fd
    das = fd.DAS(use_gpu=True)

For out-of-the-box usage with Verasonics Vantage systems, save the MATLAB workspace after collecting an RF acquisition.
The workspace file should contain at least the following variables as a .mat file:

    {
        'NA': {'NA_tot':_, 'NA':_},
        'PData': {
            'Size': _,
            'PDelta': _,
            'Origin': _,
        },
        'Trans': {
            'lensCorrection': _,
            'ElementPos': _,
            'WavelenToMm': _,
        },
        'TX': {
            'Steer': _,
            'Delay': _,
        },
        'TW': {
            'Peak': _
        },
        'Receive': {
            'SamplesPerWave': _,
            'StartDepth': _,
            'StartSample': _,
            'EndSample': _
        }
    }

In a plane wave compounding acqusition, the init_delays() function can be used to calculate the Tx and Rx delay map based on a workspace like the one above.
Otherwise, you can implement the del_Tx and del_Rx variables yourself.
Note that the units of these delay maps are not [usec] but rather [samples].

Customized Usage

If you want to use the native code wrapper in an acquisition other than plane wave steering, calculate your own del_Tx and del_Rx delays.
You'll need to provide the DAS class with some basic information on the acquisition protocol (see DAS.beamform() docstring for specific details)

    das = fd.DAS(use_gpu=True)

    del_Tx = np.zeros((NA, FOVz, FOVx), dtype=np.float32)
    del_Rx = np.zeros((n_el, FOVz, FOVx), dtype=np.float32)

    # Calc the delays
    # ...
    
    das.del_Tx = del_Tx
    das.del_Rx = del_Rx

    RF = get_your_RF_data()
    das.beamform(RF, N, start_sample, end_sample)