deep-learning-recon · diff

v0.7.0 to v0.7.0

21 added, 8 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, 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. 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.

+ 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

- - Report **SSIM, PSNR, NMSE** (and perceptual VIF/LPIPS) — but no single metric

- guarantees diagnostic quality; pair with reader assessment as the fastMRI

- challenges did.

+ - 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