PyGROG
GPU-accelerated GROG (GRAPPA Operator Gridding) for non-Cartesian MRI reconstruction.
PyGROG implements the GROG algorithm — a data-driven, parallel-imaging-aware gridding method that maps non-Cartesian k-space samples onto a Cartesian grid using GRAPPA kernels trained from an auto-calibration region. It provides:
- GROG interpolator (
pygrog.calib.GrogInterpolator) — non-Cartesian to Cartesian gridding via fractional GRAPPA operators. - SparseFFT operator (
pygrog.operator.SparseFFT) — fast sparse forward and adjoint Cartesian FFT for undersampled MRI, with optional sensitivity maps and GPU pipelining. - MaskedFFT operator (
pygrog.operator.MaskedFFT) — dense-grid masked FFT companion for GROG gridded paths. - Reconstruction gadgets (
pygrog.gadgets) — off-resonance correction and low-rank subspace projection, stackable on any base operator. Public aliases includeSubspaceGadget,OffResonanceGadget,with_subspace, andwith_offresonance. - Calibration utilities (
pygrog.utils) — NLINV coil sensitivity estimation and PCA coil compression. - Iterative solvers (
op.solve(...),pygrog.solve) — CG/LSMR interfaces that accept torch, NumPy, and CuPy arrays (CuPy via DLPack, no CPU copy). - Interoperability (
pygrog.interop) — drop-in adapters for mri-nufft, sigpy, mrpro, and deepinverse.
Quick Start
pip install pygrog
CUDA builds are published on GitHub Releases (not on PyPI). If your PyTorch is
CUDA-enabled, install the matching PyGROG CUDA wheel with -f:
# CUDA must match your installed torch wheel: cu126 | cu128 | cu130
CUDA=cu128
PYGROG_VERSION=1.0.0
pip install "pygrog==${PYGROG_VERSION}+${CUDA}" \
-f "https://github.com/FiRMLAB-Pisa/pygrog/releases/expanded_assets/v${PYGROG_VERSION}"
Find your torch CUDA version with:
python -c "import torch; print(torch.version.cuda)"
import numpy as np
from pygrog.calib import GrogInterpolator
from pygrog.operator import SparseFFT
# 1. Build the GROG plan from the non-Cartesian trajectory
grog = GrogInterpolator(shape=(256, 256), coords=coords)
# 2. Fit GRAPPA kernels from the auto-calibration region (ACR)
grog.calc_interp_table(acr_data)
# 3. Grid and reconstruct in one call
image = grog.interpolate(kspace_nc, ret_image=True)
See the documentation for full examples, API reference, and theory.
Documentation
Full documentation (installation, examples, API, theory) lives at https://pygrog.readthedocs.io/en/latest/.
Installation
# CPU (from PyPI)
pip install pygrog
# Development install with all optional dependencies
pip install --no-build-isolation -e ".[dev]"
CUDA wheels are attached to each GitHub Release.
Install them with a torch-scatter-style -f command:
# CUDA must match your installed torch wheel: cu126 | cu128 | cu130
CUDA=cu128
PYGROG_VERSION=1.0.0
pip install "pygrog==${PYGROG_VERSION}+${CUDA}" \
-f "https://github.com/FiRMLAB-Pisa/pygrog/releases/expanded_assets/v${PYGROG_VERSION}"
Development Style
For contributors, formatting and linting are Ruff-only:
ruff format .
ruff check .
ruff check is configured to apply safe auto-fixes by default.
Related Projects
- mri-nufft — Non-uniform FFT for MRI
- mrpro — MRI reconstruction in PyTorch
- sigpy — Signal processing for inverse problems
- deepinverse — Deep learning for inverse problems
License
MIT — see LICENSE for details.
1 maintainer
Metadata
1 maintainer
Release files for pygrog 0.1.2
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Built distributions (wheels)
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| pygrog-0.1.2-cp313-cp313-win_amd64.whl | CPython 3.13 | CPython 3.13 | Windows x86-64 | Details |
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| pygrog-0.1.2-cp313-cp313-macosx_12_0_arm64.whl | CPython 3.13 | CPython 3.13 | macOS 12.0+ ARM64 | Details |
| pygrog-0.1.2-cp312-cp312-win_amd64.whl | CPython 3.12 | CPython 3.12 | Windows x86-64 | Details |
| pygrog-0.1.2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64 | Details |
| pygrog-0.1.2-cp312-cp312-macosx_12_0_arm64.whl | CPython 3.12 | CPython 3.12 | macOS 12.0+ ARM64 | Details |
| pygrog-0.1.2-cp311-cp311-win_amd64.whl | CPython 3.11 | CPython 3.11 | Windows x86-64 | Details |
| pygrog-0.1.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.24+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| pygrog-0.1.2-cp311-cp311-macosx_12_0_arm64.whl | CPython 3.11 | CPython 3.11 | macOS 12.0+ ARM64 | Details |
| pygrog-0.1.2-cp310-cp310-win_amd64.whl | CPython 3.10 | CPython 3.10 | Windows x86-64 | Details |
| pygrog-0.1.2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl | CPython 3.10 | CPython 3.10 | Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64 | Details |
| pygrog-0.1.2-cp310-cp310-macosx_12_0_arm64.whl | CPython 3.10 | CPython 3.10 | macOS 12.0+ ARM64 | Details |
Total release size: 224.7 MB
Release files / pygrog-0.1.2-cp313-cp313-win_amd64.whl
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