class KbNufftAdjoint(im_size, grid_size=None, numpoints=6, n_shift=None, table_oversamp=1024, kbwidth=2.34, order=0.0, dtype=None, device=None)[source]

This object interpolates off-grid Fourier data to on-grid locations using a Kaiser-Bessel kernel prior to inverse DFT. Mathematically, in one dimension it estimates $$x_n, n \in [0, ..., N-1]$$ from a off-grid signal $$Y_m, m \in [0, ..., M-1]$$ where the off-grid frequency locations are $$\omega_m$$. To perform the estimate, this layer applies

$X_k = \sum_{j=1}^J \sum_{m=0}^{M-1} Y_m u_j(\omega_m) \mathbb{1}_{\{\{k_m+j\}_K=k\}},$
$x_n = s_n^* \sum_{k=0}^{K-1} X_k e^{i \gamma k n}$

In the first step, $$u$$, the Kaiser-Bessel kernel, is used to estimate $$Y$$ at on-grid frequency locations from locations at $$\omega$$. $$k_m$$ is the index of the root offset of nearest samples of $$X$$ to frequency location $$\omega_m$$, $$\mathbb{1}$$ is an indicator function, and $$J$$ is the number of nearest neighbors to use from $$X_k, k \in [0, ..., K-1]$$.

In the second step, an image-domain signal $$x_n$$ is estimated from a gridded, oversampled frequency-domain signal, $$X_k$$ by applying the inverse FFT, after which the complex conjugate scaling coefficients $$s_n$$ are multiplied. The oversampling coefficient is $$\gamma = 2\pi / K, K >= N$$. Multiple dimensions are handled separably. For a detailed description see Nonuniform fast Fourier transforms using min-max interpolation (JA Fessler and BP Sutton).

Note

This function is not the inverse of KbNufft; it is the adjoint.

When called, the parameters of this class define properties of the kernel and how the interpolation is applied.

• im_size is the size of the base image, analagous to $$N$$.

• grid_size is the size of the grid after adjoint interpolation, analogous to $$K$$. To reduce errors, NUFFT operations are done on an oversampled grid to reduce interpolation distances. This will typically be 1.25 to 2 times im_size.

• numpoints is the number of nearest neighbors to use for interpolation, i.e., $$J$$.

• n_shift is the FFT shift distance, typically im_size // 2.

Parameters

Examples

>>> data = torch.randn(1, 1, 12) + 1j * torch.randn(1, 1, 12)
>>> omega = torch.rand(2, 12) * 2 * np.pi - np.pi

forward(data, omega, interp_mats=None, smaps=None, norm=None)[source]

Interpolate from scattered data to gridded data and then iFFT.

Input tensors should be of shape (N, C) + klength, where N is the batch size and C is the number of sensitivity coils. omega, the k-space trajectory, should be of size (len(grid_size), klength) or (N, len(grid_size), klength), where klength is the length of the k-space trajectory.

Note

If the batch dimension is included in omega, the interpolator will parallelize over the batch dimension. This is efficient for many small trajectories that might occur in dynamic imaging settings.

If your tensors are real, ensure that 2 is the size of the last dimension.

Parameters
Return type

Tensor

Returns

data transformed to the image domain.