Torch KB-NUFFT

API | GitHub | Notebook Examples

Simple installation from PyPI:

pip install torchkbnufft

About

Torch KB-NUFFT implements a non-uniform Fast Fourier Transform with Kaiser-Bessel gridding in PyTorch. The implementation is completely in Python, facilitating robustness and flexible deployment in human-readable code. NUFFT functions are each wrapped as a torch.autograd.Function, allowing backpropagation through NUFFT operators for training neural networks.

Documentation

Most files are accompanied with docstrings that can be read with help while running IPython. Example:

from torchkbnufft import KbNufft

help(KbNufft)

Behavior can also be inferred by inspecting the source code here. An html-based API reference is here.

Examples

Simple Forward NUFFT

Jupyter notebook examples are in the notebooks/ folder. The following minimalist code loads a Shepp-Logan phantom and computes a single radial spoke of k-space data:

import torch
import numpy as np
from torchkbnufft import KbNufft
from skimage.data import shepp_logan_phantom

x = shepp_logan_phantom().astype(np.complex)
im_size = x.shape
x = np.stack((np.real(x), np.imag(x)))
# convert to tensor, unsqueeze batch and coil dimension
# output size: (1, 1, 2, ny, nx)
x = torch.tensor(x).unsqueeze(0).unsqueeze(0)

klength = 64
ktraj = np.stack(
    (np.zeros(64), np.linspace(-np.pi, np.pi, klength))
)
# convert to tensor, unsqueeze batch dimension
# output size: (1, 2, klength)
ktraj = torch.tensor(ktraj).unsqueeze(0)

nufft_ob = KbNufft(im_size=im_size)
# outputs a (1, 1, 2, klength) vector of k-space data
y = nufft_ob(x, ktraj)

A detailed example of basic NUFFT usage is included in notebooks/Basic Example.ipynb.

SENSE-NUFFT

The package also includes utilities for working with SENSE-NUFFT operators. The above code can be modified to replace the nufft_ob with the following sensenufft_ob:

from torchkbnufft import MriSenseNufft

sensenufft_ob = MriSenseNufft(im_size=im_size, smap=smap)

Application of the object in place of nufft_ob above would first multiply by the sensitivity coils in smap, then compute a 64-length radial spoke for each coil. All operations are broadcast across coils, which minimizes interaction with the Python interpreter and maximizes computation speed.

A detailed example of SENSE-NUFFT usage is included in notebooks/SENSE Example.ipynb.

Sparse Matrix Precomputation

Previously, sparse matrix-based interpolation was the fastest operation mode. As of v0.2.0, this is no longer the case on the GPU. Nonetheless, sparse matrices can be faster than normal operation mode in certain situations (e.g., when using many coils and slices). The following code calculates sparse interpolation matrices and uses them to compute a single radial spoke of k-space data:

from torchkbnufft import AdjKbNufft
from torchkbnufft.nufft.sparse_interp_mat import precomp_sparse_mats

adjnufft_ob = AdjKbNufft(im_size=im_size)

# precompute the sparse interpolation matrices
real_mat, imag_mat = precomp_sparse_mats(ktraj, adjnufft_ob)
interp_mats = {
    'real_interp_mats': real_mat,
    'imag_interp_mats': imag_mat
}

# use sparse matrices in adjoint
image = adjnufft_ob(kdata, ktraj, interp_mats)

A detailed example of sparse matrix precomputation usage is included in notebooks/Sparse Matrix Example.ipynb.

Running on the GPU

All of the examples included in this repository can be run on the GPU by sending the NUFFT object and data to the GPU prior to the function call, e.g.:

adjnufft_ob = adjnufft_ob.to(torch.device('cuda'))
kdata = kdata.to(torch.device('cuda'))
ktraj = ktraj.to(torch.device('cuda'))

image = adjnufft_ob(kdata, ktraj)

Similar to programming low-level code, PyTorch will throw errors if the underlying dtype and device of all objects are not matching. Be sure to make sure your data and NUFFT objects are on the right device and in the right format to avoid these errors.

Computation Speed

TorchKbNufft is first and foremost designed to be lightweight with minimal dependencies outside of PyTorch. The following computation times seconds were observed on a workstation with a Xeon E5-1620 CPU and an Nvidia GTX 1080 GPU for a 15-coil, 405-spoke 2D radial problem.

Profiling for your machine can be done by running

python profile_torchkbnufft.py

References

Fessler, J. A., & Sutton, B. P. (2003). Nonuniform fast Fourier transforms using min-max interpolation. IEEE transactions on signal processing, 51(2), 560-574.

Beatty, P. J., Nishimura, D. G., & Pauly, J. M. (2005). Rapid gridding reconstruction with a minimal oversampling ratio. IEEE transactions on medical imaging, 24(6), 799-808.

Citation

If you want to cite the package, you can use the following:

@misc{Muckley2019,
  author = {Muckley, M.J. et al.},
  title = {Torch KB-NUFFT},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/mmuckley/torchkbnufft}}
}