MIRTorch

GitHub release (latest by date including pre-releases) Read the Docs

A PyTorch-based differentiable Image Reconstruction Toolbox, developed at the University of Michigan.

The work is inspired by MIRT, a well-acclaimed toolbox for medical imaging reconstruction.

The overarching goal is to provide fast iterative and data-driven image reconstruction across CPUs and GPUs. Researchers can rapidly develop new model-based and learning-based methods (i.e., unrolled neural networks) with convenient abstraction layers. With the full support of auto-differentiation, one may optimize imaging protocols and image reconstruction parameters with gradient methods.

Documentation: https://mirtorch.readthedocs.io/en/latest/

Installation

We recommend to pre-install PyTorch first. To install the MIRTorch package, after cloning the repo, please try python setup.py install.

requirements.txt details the package dependencies. We recommend installing pytorch_wavelets directly from the source code instead of pip.

Features

Linear maps

The LinearMap class overloads common matrix operations, such as +, - , *.

Instances include basic linear operations (like convolution), classical imaging processing, and MRI system matrix (Cartesian and Non-Cartesian, sensitivity- and B0-informed system models). More is on the way…

Since the Jacobian matrix of a linear operator is itself, the toolbox can actively calculate such Jacobians during backpropagation, avoiding the large cache cost required by auto-differentiation.

Proximal operators

The toolbox contains common proximal operators such as soft thresholding. These operators also support the regularizers that involve multiplication with diagonal or unitary matrices, such as orthogonal wavelets.

Iterative reconstruction (MBIR) algorithms

Currently, the package includes the conjugate gradient (CG), fast iterative thresholding (FISTA), optimized gradient method (POGM), forward-backward primal-dual (FBPD) algorithms for image reconstruction.

Dictionary learning

For dictionary learning-based reconstruction, we implemented an efficient dictionary learning algorithm (SOUP-DIL) and orthogonal matching pursuit (OMP). Due to PyTorch’s limited support of sparse matrices, we use SciPy as the backend.

Usage and examples

/example includes several examples.

/example/demo_mnist.ipynb shows the LASSO on MNIST with FISTA and POGM.

/example/demo_mri.ipynb contains the SENSE (CG-SENSE) and B0-informed reconstruction with penalized weighted least squares (PWLS).

/example/demo_cs.ipynb shows the compressed sensing reconstruction of under-determined MRI signals.

/example/demo_dl.ipynb exhibits the dictionary learning results.

Bjork repo contains MRI sampling pattern optimization examples. One may use the reconstruction loss as the objective function to jointly optimize reconstruction algorithms and the sampling pattern.

Acknowledgments

This work is inspired by (but not limited to):

SigPy: https://github.com/mikgroup/sigpy

MIRT/MIRT.jl: https://web.eecs.umich.edu/~fessler/code/index.html

PyLops: https://github.com/PyLops/pylops

If the code is useful to your research, please cite:

@article{wang:22:bjork,
  author={Wang, Guanhua and Luo, Tianrui and Nielsen, Jon-Fredrik and Noll, Douglas C. and Fessler, Jeffrey A.},
  journal={IEEE Transactions on Medical Imaging}, 
  title={B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI}, 
  year={2022},
  pages={1-1},
  doi={10.1109/TMI.2022.3161875}}

License

This package uses the BSD3 license.