sensmap

sensmap#

mrtwin.sensmap(shape, coil_width=2.0, shift=None, dphi=0.0, nrings=None, cache=None, cache_dir=None)[source]#

Simulate birdcage coils.

Adapted from SigPy [1].

Parameters:

  • shape (Iterable[int]) – Size of the matrix (ncoils, ny, nx) (2D) or (ncoils, nz, ny, nx) (3D) for the sensitivity coils.

  • coil_width (float, optional) – Width of the coil, with respect to image dimension. The default is 2.0.

  • shift (Sequence[int] | None, optional) – Displacement of the coil center with respect to matrix center. The default is (0, 0) / (0, 0, 0).

  • dphi (float, optional) – Bulk coil angle in [deg]. The default is 0.0°.

  • nrings (int | None, optional) – Number of rings for a cylindrical hardware set-up. The default is ncoils // 4.

  • cache (bool | None, optional) – If True, cache the phantom. The default is True for 3D phantoms and False for single-slice 2D.

  • cache_dir (CacheDirType, optional) – cache_directory for phantom caching. The default is None (~/.cache/mrtwin).

Returns:

smap – Complex spatially varying sensitivity maps of shape (nmodes, ny, nx) (2D) or (nmodes, nz, ny, nx) (3D). If nmodes = 1, the first dimension is squeezed.

Return type:

torch.Tensor

Example

>>> from mrtwin import sensmap

We can generate a set of nchannels=8 2D sensitivity maps of shape (ny=128, nx=128) by:

>>> smap = sensmap((8, 128, 128))

Coil center and rotation can be modified by shift and dphi arguments:

>>> smap = sensmap((8, 128, 128), shift=(-3, 5), dphi=30.0) # center shifted by (dy, dx) = (-3, 5) pixels and rotated by 30.0 degrees.

Similarly, nchannels=8 3D sensitivity maps can be generated as:

>>> smap = sensmap((8, 128, 128, 128))

Beware that this will require more memory.

References

[1] mikgroup/sigpy

Examples using mrtwin.sensmap#

Coil Sensitivity Simulation

Coil Sensitivity Simulation
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