Deep Learning Recon

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.

Project research memory

For project experiments, read .mri-research/INDEX.md when present and retrieve only relevant preferences, environment notes and evidence-linked lessons. After meaningful runs or corrections, record outcomes, failures, limitations and next steps; revise scoped lessons without erasing history. Keep user preferences separate from scientific findings. Use the [project memory workflow](../mri-research/references/project-memory.md) to initialize the folder or connect project CLAUDE.md / AGENTS.md. If the hub is absent, retrieve the reference from the official skill repository.

Tool setup before execution

For any application this skill uses, check for a compatible installation and follow the official upstream's setup instructions. Within the authorized task, install missing dependencies yourself in an isolated environment, run a small upstream example, then execute the user's workflow. Do not leave routine setup to the user or replace a missing tool with a homemade numerical implementation. Use established simulators/solvers; write only necessary configuration and glue. If blocked, report the actual obstacle and an established alternative. Read the [tool setup guide](../mri-research/references/tool-setup.md) when installing, repairing, or choosing an execution environment. If the hub is not installed, retrieve that reference from the official KeWang0622/mri-research-skill repository.

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. 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: KeWang0622
• Source: KeWang0622/mri-research-skill
• License: MIT

Install and usage instructions live in the source repository linked above.