MIRTorch

A differentiable PyTorch toolbox for medical imaging reconstruction, developed at the University of Michigan. MIRTorch provides composable linear maps, proximal operators, iterative solvers, and MRI and SPECT system models.

Documentation · Examples · API

FINUFFT/cuFINUFFT now accelerate non-Cartesian and B0-informed MRI; warm NVIDIA A10 benchmarks measured up to 5.2× faster NUFFT, 9.4× faster Toeplitz, and 12.3× faster iterative solvers (workload-dependent). Both NUFFT backends also support efficient first-order trajectory gradients, enabling SNOPY-style sampling-pattern optimization directly in MIRTorch (Wang and Fessler, 2023).

Install PyTorch for your platform, then:

pip install MIRTorch

CUDA users can install cuFINUFFT with:

pip install "MIRTorch[cufinufft]"

For local development:

pip install -e ".[dev]"

NuSense, NuSenseGram, Gmri, and GmriGram use an installed FINUFFT or cuFINUFFT library when the device supports it, then fall back to torchkbnufft. Base macOS, Apple Metal, and Linux ARM installs therefore work without a native library. Set backend="torchkbnufft" or backend="finufft" to override the automatic choice.

Real-valued CUDA runs of Diff2dgram, FISTA, and POGM use torch.compile automatically when PyTorch provides it. Other inputs stay eager; pass compile=False to disable compilation explicitly.

The notebooks choose CUDA, Apple Metal, or CPU at runtime:

• demo_mri.ipynb: CG-SENSE and B0-informed PWLS
• demo_3d.ipynb: 3D non-Cartesian MRI and Toeplitz embedding
• demo_trajectory_optimization.ipynb: SNOPY-style radial trajectory optimization
• demo_cs.ipynb: compressed-sensing MRI
• demo_mlem.ipynb: SPECT reconstruction
• demo_mnist.ipynb: CG, FISTA, and POGM
• demo_dl.ipynb: dictionary learning

MIRTorch is inspired by MIRT, MIRT.jl, SigPy, and PyLops.

If MIRTorch is useful in your work, please cite:

@inproceedings{wang:22:mirtorch,
  title={{MIRTorch}: A {PyTorch}-powered Differentiable Toolbox for Fast Image
         Reconstruction and Scan Protocol Optimization},
  author={Wang, Guanhua and Shah, Neel and Zhu, Keyue and Noll, Douglas C. and
          Fessler, Jeffrey A.},
  booktitle={Proceedings of the International Society for Magnetic Resonance
             in Medicine (ISMRM)},
  pages={4982},
  year={2022}
}

See the documentation for the BJORK and SPECT citations.

MIRTorch is distributed under the BSD 3-Clause License.