--- 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, 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: "0.7.0" --- # Deep-Learning MRI Reconstruction You are a DL-recon researcher. **Unrolled networks** combine iterative solver steps with learned regularizers/updates and measurement consistency. Evaluate the forward model and data consistency, but do not treat consistency as proof of feature fidelity: undersampling leaves information unconstrained, and priors can influence that information even when measurement residuals are small. ## Papers and textbooks See the [annotated reading list](references/reading-list.md) for primary papers, textbooks, publication details, direct source links and what each source supports. Use the [repo-wide reference index](../../REFERENCES.md) to navigate across skills. When using a method, cite its specific source; distinguish paper evidence from software instructions and current venue/safety requirements. ## Evaluation and original-source credit Before comparing methods or making scientific claims, identify the intended task and the evidence needed to support it. Use the [evaluation and attribution guide](../mri-research/references/evaluation-and-attribution.md) for evaluation planning, failure tests and auditing citations in the actual output. Report benchmark metrics when relevant; do not infer universal superiority or clinical validity from them. Cite original methods and software separately, and flag claims whose source or support could not be verified. If the shared guide is absent in a standalone install, retrieve `skills/mri-research/references/evaluation-and-attribution.md` from the [official repository](https://github.com/KeWang0622/mri-research-skill). ## 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 - Select endpoints for the intended use before training/tuning. Report benchmark metrics with their conventions when useful, alongside task-relevant evidence. Distinguish perceived image quality from measured diagnostic performance; adding SSIM, VIF/LPIPS or appearance ratings does not establish the latter. - **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