parallel-imaging

Python implementations of GRAPPA-like algorithms.

Sigmanet: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction,

MRI research guidance for coding agents — composable skills, established tools, and evidence-linked research memory. Install: npx skills add KeWang0622/MRFoundry

Code for paper "Robust SENSE reconstruction of simultaneous multislice EPI with low-rank enhanced coil sensitivity calibration and slice-dependent 2D Nyquist ghost correction" - https://doi.org/10.1002/mrm.27120

Multi-slice MR Reconstruction with Low-Rank Tensor Completion

Calibrationless Multi-Slice Cartesian MRI via Orthogonally Alternating Phase Encoding Direction and Joint Low-Rank Tensor Completion

MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data

Motion-Informed Coil selection for real-time speech MRI: ranks receiver channels by motion-driven k-space phase variance (ISMRM 2026)

ReSiDe

An interoperable GROG package to accelerate Non-Cartesian MR reconstructions.

MRI reconstruction from scratch in Python: GRAPPA, ESPIRiT, PICS, B0 correction, EPI ghost and B0-drift correction, unrolled deep-learning recon

Python/PyTorch implementation of BASS for learning parallel MRI sampling patterns, with SENSE-CG reconstruction, parity utilities, visualization, and tests.