jaxus 0.1.0

Basic ultrasound simulation and reconstruction functionality implemented in JAX.

JAX UltraSound

Basic ultrasound simulation and reconstruction functionality implemented in JAX.

⚠️ Note: The project is in an early stage of development and the API is subject to change. Be sure to pin the version of the package in your requirements.txt file.

Installation

Install the package using pip:

pip install jaxus

The project also depends on JAX. To install JAX follow the official instructions. At the time of writing, the following command should work for CUDA 12:

pip install -U "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Usage

Define simulation parameters

import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np

import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np

from jaxus import (
    plot_rf,
    use_dark_style,
    simulate_rf_data,
    beamform_das,
    log_compress,
    plot_beamformed,
    CartesianPixelGrid,
)
# The number of axial samples to simulate
n_ax = 2048
# The number of elements in the probe
n_el = 128
# The center frequency in Hz
carrier_frequency = 7e6
# Sampling frequency in Hz
sampling_frequency = 4 * carrier_frequency
# The speed of sound in m/s
sound_speed = 1540
# The width of the elements in wavelengths of the center frequency
width_wl = 1.33
# The time instant of the first sample in seconds
initial_time = 0.0
# The number of scatterers to process simultaneously. If it does not fit in memory
# then lower this number.
scatterer_chunk_size = 512
# The number of axial samples to process simultaneously. If it does not fit in
# memory then lower this number.
ax_chunk_size = 1024
# Set to True to simulate a single point scatterer. If False a region filled
# with randomly placed scatterers is simulated.
single_point_scatterer = True
# The attenuation coefficient in dB/(MHz*cm)
attenuation_coefficient = 0.9
# Set to True to simulate the wavefront only. Instead of summing the wavefronts
# from each transmit element, the wavefront from the transmit element with the
# shortest time of flight is used. The reduces computation time, but is less
# accurate.
wavefront_only = False

# Generate probe geometry
probe_geometry = np.stack([jnp.linspace(-19e-3, 19e-3, n_el), jnp.zeros(n_el)], axis=1)

# Define the element angles in radians
element_angles = 0 * np.ones(n_el) * jnp.pi / 2

# Set all t0_delays to 0 to simulate a plane wave with angle 0
t0_delays = np.zeros(n_el)

# Set the tx apodization to 1
tx_apodization = np.ones(n_el)

# Define the scatterer positions and amplitudes
n_scat = 30

t = np.linspace(0, 2 * np.pi, n_scat, endpoint=False)
scatterer_x = 16e-3 * np.sin(t) ** 3
scatterer_z = -(
    13e-3 * np.cos(t)
    - 5e-3 * np.cos(2 * t)
    - 2e-3 * np.cos(3 * t)
    - 1e-3 * np.cos(4 * t)
)
scatterer_z -= np.min(scatterer_z)
scatterer_z += 15e-3

scatterer_positions = np.stack([scatterer_x, scatterer_z], axis=1)

scatterer_amplitudes = np.ones((scatterer_positions.shape[0]))

Simulate RF data

rf_data = simulate_rf_data(
    n_ax,
    scatterer_positions,
    scatterer_amplitudes,
    t0_delays,
    probe_geometry,
    element_angles,
    tx_apodization,
    initial_time,
    width_wl,
    sampling_frequency,
    carrier_frequency,
    sound_speed,
    attenuation_coefficient,
    wavefront_only,
    ax_chunk_size=ax_chunk_size,
    scatterer_chunk_size=scatterer_chunk_size,
    progress_bar=True,
)

Beamform the RF data using delay-and-sum

# Define beamforming grid
pixel_grid = CartesianPixelGrid(
    n_x=512,
    n_z=512,
    dx_wl=0.5,
    dz_wl=0.5,
    z0=1e-4,
    wavelength=sound_speed / carrier_frequency,
)

# Perform DAS beamforming
beamformed_image = beamform_das(
    rf_data[None, None, :, :, None],
    pixel_positions=pixel_grid.pixel_positions_flat,
    probe_geometry=probe_geometry,
    t0_delays=t0_delays[None],
    initial_times=np.ones(1) * initial_time,
    sampling_frequency=sampling_frequency,
    carrier_frequency=carrier_frequency,
    sound_speed=sound_speed,
    f_number=1.5,
    rx_apodization=np.ones(n_el),
    iq_beamform=True,
    t_peak=np.array([0.0]),
)

beamformed_image = log_compress(
    beamformed_image.reshape(pixel_grid.shape), normalize=True
)

Plotting

# Plot the RF data and the beamformed image
use_dark_style()
fig, axes = plt.subplots(1, 2)
ax_rf, ax_bf = axes
plot_rf(ax_rf, rf_data)
plot_beamformed(
    ax_bf,
    beamformed_image,
    extent_m=pixel_grid.extent,
    probe_geometry=probe_geometry,
)
plt.tight_layout()
plt.show()