deepmr.radial_proj#
- deepmr.radial_proj(shape, nviews=None, order='ga', **kwargs)[source]#
Design a 3D radial projectiontrajectory.
The trajectory consists of a 2D radial trajectory, whose plane is rotated to cover the 3D k-space. In-plane rotations are sequential. Plane rotation types are specified via the
order
argument.- Parameters:
shape (Iterable[int]) – Matrix shape
(in-plane, contrasts=1, echoes=1)
.nviews (int, optional) – Number of spokes (in-plane, radial). The default is
$\pi$ * (shape[0], shape[1])
ifshape[2] == 1
, otherwise it is($\pi$ * shape[0], 1)
.order (str, optional) –
Radial plane rotation type. These can be:
ga
: Pseudo golden angle variation of periodicity377
.ga::multiaxis
: Pseudo golden angle, i.e., same asga
but views are repeated 3 times on orthogonal axes.ga-sh
: Shuffled pseudo golden angle.ga-sh::multiaxis
: Multiaxis shuffled pseudo golden angle, i.e., same asga-sh
but views are repeated 3 times on orthogonal axes.
The default is
ga
.
- Keyword Arguments:
variant (str) –
Type of radial trajectory. Allowed values are:
fullspoke
: starts at the edge of k-space and ends on the opposite side (default).center-out
: starts at the center of k-space and ends at the edge.
- Returns:
head – Acquisition header corresponding to the generated sampling pattern.
- Return type:
Header
Example
>>> import deepmr
We can create a Nyquist-sampled 3D radial trajectory for a matrix of
(128, 128, 128)
voxels by:>>> head = deepmr.radial_proj(128)
An undersampled trajectory can be generated by specifying the
nviews
argument:>>> head = deepmr.radial_proj(128, nviews=64)
Multiple contrasts with different sampling (e.g., for MR Fingerprinting) can be achieved by providing a tuple of ints as the
shape
argument:>>> head = deepmr.radial_proj((128, 420)) >>> head.traj.shape torch.Size([420, 402, 128, 2])
corresponding to 420 different contrasts, each sampled with a different fully sampled plane. Similarly, multiple echoes (with fixed sampling) can be specified as:
>>> head = deepmr.radial_proj((128, 1, 8)) >>> head.traj.shape torch.Size([8, 161604, 128, 2])
corresponding to a 8-echoes fully sampled k-spaces, e.g., for QSM and T2* mapping.
Notes
The returned
head
(deepmr.Header()
) is a structure with the following fields:- shape (torch.Tensor):
This is the expected image size of shape
(nz, ny, nx)
.
- t (torch.Tensor):
This is the readout sampling time
(0, t_read)
inms
. with shape(nsamples,)
.
- traj (torch.Tensor):
This is the k-space trajectory normalized as
(-0.5 * shape, 0.5 * shape)
with shape(ncontrasts, nviews, nsamples, 2)
.
- dcf (torch.Tensor):
This is the k-space sampling density compensation factor with shape
(ncontrasts, nviews, nsamples)
.
- TE (torch.Tensor):
This is the Echo Times array. Assumes a k-space raster time of
1 us
and minimal echo spacing.