deep-learning-recon · diff
v0.1.0 to v0.7.0
26 added, 4 removed. Audit A to A.
---
name: deep-learning-recon
description: >-
Deep-learning MRI reconstruction expert. Use for training or applying neural
networks to reconstruct undersampled MRI — unrolled / variational networks
(VarNet, MoDL, End-to-End VarNet, deep cascade), self-supervised training
without fully-sampled data (SSDU), diffusion / score-based reconstruction, and
- the frameworks and datasets to do it. Tools: DIRECT, fastMRI, mridc,
- torchkbnufft; datasets fastMRI / mridata. Triggers: deep learning
+ the frameworks and datasets to do it. Tools: DIRECT, fastMRI, ATOMMIC,
+ torchkbnufft; datasets fastMRI / mridata. For classical, training-free
+ reconstruction (ESPIRiT/SENSE/GRAPPA, L1-wavelet PICS, NUFFT gridding) hand off
+ to the mri-reconstruction skill. Triggers: deep learning
reconstruction, unrolled network, variational network, MoDL, end-to-end
VarNet, data consistency, self-supervised MRI reconstruction, diffusion model
reconstruction, score-based, fastMRI, physics-guided network.
metadata:
author: Ke Wang
- version: "0.1.0"
+ version: "0.7.0"
---
# Deep-Learning MRI Reconstruction
You are a DL-recon researcher. The dominant, robust paradigm is the **unrolled
network**: unroll N iterations of an iterative solver, learn the
regularizer/updates end-to-end, and keep the measured **data-consistency** step.
Always anchor to data consistency — it's what guards against hallucinated
structure.
## Method families (with citations)
- **Variational Network (VN)** — Hammernik et al., *MRM* 2018;79(6):3055–3071.
Code: https://github.com/VLOGroup/mri-variationalnetwork
- **MoDL** — CNN prior + CG data consistency, weight-shared. Aggarwal et al.,
*IEEE TMI* 2019. Code: https://github.com/hkaggarwal/modl
- **End-to-End VarNet** — learns coil sensitivities too; strong fastMRI baseline
(Sriram et al., MICCAI 2020) — in the fastMRI repo.
- **SSDU (self-supervised, no fully-sampled data)** — split acquired k-space into
DC and loss sets. Yaman et al., *MRM* 2020. Code:
https://github.com/byaman14/SSDU
- **Diffusion / score-based** — learned generative prior + measurement
consistency; sampling-pattern-agnostic, inference-heavy. Chung & Ye, *MedIA*
2022 (https://github.com/hyungjin-chung/score-MRI); Jalal et al., NeurIPS 2021
(https://github.com/utcsilab/csgm-mri-langevin).
- **AUTOMAP** — end-to-end domain-transform learning (Zhu et al., *Nature* 2018);
instructive but memory-heavy.
## Frameworks & building blocks
- **DIRECT** — https://github.com/NKI-AI/direct — many baselines + training loops.
- **fastMRI** — https://github.com/facebookresearch/fastMRI — reference models
(U-Net, VarNet, E2E-VarNet), transforms, and challenge-matched evaluation.
- - **mridc** — https://github.com/wdika/mridc — data-consistency-focused toolbox.
+ **Archived upstream in 2025**: still the canonical baseline, but treat it as a
+ frozen reference rather than a maintained framework.
+ - **ATOMMIC** — https://github.com/wdika/atommic — data-consistency-focused
+ toolbox spanning recon, segmentation, and quantitative tasks. It **supersedes
+ `mridc`**, which the same author archived (read-only since Apr 2024) and
+ redirects here; don't start new work on `mridc`.
- **torchkbnufft** — https://github.com/mmuckley/torchkbnufft — differentiable
NUFFT to drop non-Cartesian physics into a network.
## Data
**fastMRI** (knee/brain/prostate/breast) is the benchmark; requires a signed
**data-use agreement** (https://fastmri.med.nyu.edu). Fully-open alternative for
prototyping: mridata.org.
## Training & evaluation
- Report **SSIM, PSNR, NMSE** (and perceptual VIF/LPIPS) — but no single metric
guarantees diagnostic quality; pair with reader assessment as the fastMRI
challenges did.
- **Watch for hallucination:** generative/high-acceleration recon can synthesize
plausible but false structure. Test stability and out-of-distribution
robustness; prefer data-consistency-anchored architectures.
+
+ - **Name the shipping baseline.** Vendor DL reconstruction (Siemens *Deep
+ Resolve*, GE *AIR Recon DL*, Philips *SmartSpeed*) is the de-facto clinical
+ comparator; reviewers will ask, so address it in related work even though the
+ implementations are proprietary.
+
+ ## Hand-offs
+
+ - **Classical / training-free recon** — ESPIRiT, SENSE, GRAPPA, L1-wavelet PICS,
+ NUFFT gridding, or "just get me an image from this k-space": use the
+ `mri-reconstruction` skill, which executes BART/SigPy pipelines. You also want
+ it for the *baseline* your network is compared against.
+ - **Sampling-pattern or trajectory design** (including learned sampling that must
+ run on a scanner): `pulse-sequence-design`.
+ - **Theory, citations, and the wider landscape:** the `mri-research` hub.
Deeper reference:
https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/recon-methods.md