Note
Go to the end to download the full example code.
Creating motion artifacts on an anatomical EPI with SNAKE-fMRI#
This examples walks through the elementary components of SNAKE and demonstrates how to add motion artifacts to the simulation.
# Imports
import numpy as np
import matplotlib.pyplot as plt
from snake.core.phantom import Phantom
from snake.core.sampling import EPI3dAcquisitionSampler
from snake.core.simulation import GreConfig, SimConfig, default_hardware
Setting up the base simulation Config. This configuration holds all key parameters for the simulation, describing the scanner parameters.
sim_conf = SimConfig(
max_sim_time=6,
seq=GreConfig(TR=50, TE=30, FA=3),
hardware=default_hardware,
)
sim_conf.hardware.n_coils = 8
sim_conf.fov.res_mm = (3, 3, 3)
sim_conf
Creating the base Phantom#
The simulation acquires the data describe in a phantom. A phantom consists of fuzzy segmentation of head tissue, and their MR intrinsic parameters (density, T1, T2, T2*, magnetic susceptibilities)
Here we use Brainweb reference mask and values for convenience.
phantom = Phantom.from_brainweb(
sub_id=4, sim_conf=sim_conf, output_res=1, tissue_file="tissue_7T"
)
# Here are the tissue availables and their parameters
phantom.affine
No matrix size found in the header. The header is probably missing.
array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]], dtype=float32)
Adding motion to the Phantom#
The motion is added to the phantom by applying a transformation to the simulation’s FOV configuration (as if the phantom was moving in the scanner).
from snake.core.handlers import RandomMotionImageHandler
motion = RandomMotionImageHandler(
ts_std_mms=[1, 1, 1],
rs_std_degs=[0.1, 0.1, 0.1],
)
motion2 = RandomMotionImageHandler(
ts_std_mms=[3, 3, 3],
rs_std_degs=[0.5, 0.5, 0.5],
)
Setting up Acquisition Pattern and Initializing Result file.#
# The next piece of simulation is the acquisition trajectory.
# Here nothing fancy, we are using a EPI (fully sampled), that samples a 3D
# k-space (this akin to the 3D EPI sequence of XXXX)
sampler = EPI3dAcquisitionSampler(accelz=1, acsz=0.1, orderz="top-down")
Acquisition with Cartesian Engine#
We acquire the three setup separately.
from snake.core.engine import EPIAcquisitionEngine
engine = EPIAcquisitionEngine(model="simple")
engine(
"example_nomotion.mrd",
sampler=sampler,
phantom=phantom,
handlers=[],
sim_conf=sim_conf,
worker_chunk_size=16,
n_workers=4,
)
engine(
"example_motion.mrd",
sampler=sampler,
phantom=phantom,
handlers=[motion],
sim_conf=sim_conf,
worker_chunk_size=16,
n_workers=4,
)
engine(
"example_motion2.mrd",
sampler=sampler,
phantom=phantom,
handlers=[motion2],
sim_conf=sim_conf,
worker_chunk_size=16,
n_workers=4,
)
Existing example_nomotion.mrd it will be overwritten
[Errno 2] No such file or directory: 'example_nomotion.mrd'
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/volatile/github-ci-mind-inria/.local/lib/python3.10/site-packages/xsdata/formats/dataclass/parsers/utils.py:126: ConverterWarning: Failed to convert value for `waveformInformationType.waveformType`
`41407` is not a valid `waveformInformationTypeWaveformType`
warnings.warn(message, ConverterWarning)
No coil_cov found in the dataset.
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Existing example_motion2.mrd it will be overwritten
[Errno 2] No such file or directory: 'example_motion2.mrd'
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Simple reconstruction#
from snake.mrd_utils import CartesianFrameDataLoader
from snake.toolkit.reconstructors import ZeroFilledReconstructor
with CartesianFrameDataLoader("example_nomotion.mrd") as data_loader:
rec = ZeroFilledReconstructor(n_jobs=1)
rec_nomotion = rec.reconstruct(data_loader).squeeze()
with CartesianFrameDataLoader("example_motion.mrd") as data_loader:
rec = ZeroFilledReconstructor(n_jobs=1)
rec_motion = rec.reconstruct(data_loader).squeeze()
motion = data_loader.get_dynamic(0)
with CartesianFrameDataLoader("example_motion2.mrd") as data_loader:
rec = ZeroFilledReconstructor(n_jobs=1)
rec_motion2 = rec.reconstruct(data_loader).squeeze()
motion2 = data_loader.get_dynamic(0)
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with CartesianFrameDataLoader("example_motion2.mrd") as data_loader:
sim_conf.max_sim_time
rec_motion.shape
(2, 60, 72, 60)
Show the motion#
fig, axs = plt.subplots(2, 1, figsize=(10, 5), sharex=True)
time = np.arange(len(motion.data[0])) * sim_conf.seq.TR / 1000
TR_vol = 3 # We generated 2 frames in 6 seconds
axs[0].axvline(TR_vol, c="gray")
axs[1].axvline(TR_vol, c="gray")
axs[0].plot(time, motion.data[:3, :].T)
axs[1].plot(time, motion.data[3:, :].T)
axs[0].set_prop_cycle(None) # reset color cycling
axs[1].set_prop_cycle(None)
axs[0].plot(time, motion2.data[:3, :].T, linestyle="dashed")
axs[1].plot(time, motion2.data[3:, :].T, linestyle="dashed")
axs[1].set_xlabel("time (s)")
axs[0].set_ylabel("Translation (mm)")
axs[1].set_ylabel("Rotation (deg)")
Text(76.22222222222221, 0.5, 'Rotation (deg)')
Visualizing the reconstructed data#
import matplotlib.pyplot as plt
from snake.toolkit.plotting import axis3dcut
fig, axs = plt.subplots(1, 3, figsize=(26, 5))
axis3dcut(
rec_motion[0].T,
None,
None,
cbar=False,
cuts=(0.5, 0.5, 0.5),
ax=axs[0],
)
axis3dcut(
rec_motion2[0].T,
None,
None,
cbar=False,
cuts=(0.5, 0.5, 0.5),
ax=axs[1],
)
axis3dcut(rec_nomotion[0].T, None, None, cbar=False, cuts=(0.5, 0.5, 0.5), ax=axs[2])
axs[0].set_title("Small motion")
axs[1].set_title("Big motion")
axs[2].set_title("No motion")
plt.show()
/volatile/github-ci-mind-inria/gpu_mind_runner/_work/_tool/Python/3.10.16/x64/lib/python3.10/site-packages/numpy/lib/_function_base_impl.py:4620: RuntimeWarning: invalid value encountered in subtract
diff_b_a = subtract(b, a)
sim_conf.seq.TR_eff
50
Total running time of the script: (1 minutes 15.219 seconds)