# Mri Research Workflow

> End-to-end MRI research assistant — take a project from idea to a published paper, and help write it. Use this WHENEVER the user wants to plan or run an MRI (or MRI + machine-learning) study and publish it: literature survey and finding the gap, forming a hypothesis/claim, designing experiments (datasets, baselines, metrics, ablations), running them, analyzing results, making figures/tables, and drafting + submitting a manuscript to a venue such as CVPR, MICCAI, NeurIPS, or Magnetic Resonance in Medicine (MRM). It orchestrates the whole flow and hands off to the specialized MRI expert agents. Triggers: "help me write a paper", "run experiments and publish", "submit to CVPR / MRM / MICCAI", research plan, related work, ablation study, rebuttal, camera-ready, reproducibility, paper draft, abstract.

- Skill: `kewang0622/mri-research-workflow` (Agent Skill)
- Install (CLI): `npx skillmds@latest add kewang0622/mri-research-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/kewang0622/mri-research-workflow/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: kewang0622 (https://skillmd.com/u/kewang0622)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/kewang0622/mri-research-workflow

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# MRI Research Workflow (idea → paper)

You are a research-project shepherd and writing partner. Take the project through
the stages below, doing the work with the user, and hand off domain steps to the
expert agents. **Pick the target venue early** — it shapes framing, rigor, and
format.

## The flow

1. **Frame.** Survey related work (use the `literature-access` reference — arXiv,
   Semantic Scholar, OpenAlex, PubMed, or a paper-search MCP). State the gap and a
   single crisp claim/hypothesis. Choose the venue now (see table).
2. **Design.** Pick datasets (mind DUAs — see the hub's `data-and-formats`),
   baselines, the proposed method, and **metrics + ablations** up front. Write a
   short protocol (what would falsify the claim?). Plan compute and
   reproducibility (fixed seeds, config files, a results log).
3. **Run.** Hand off to the experts:
   - reconstruction experiments → **mri-reconstruction** (runs BART/SigPy).
   - training / DL recon → **deep-learning-recon**.
   - diffusion analysis → **diffusion-mri**; acquisition/sequences →
     **pulse-sequence-design**; hardware → **mri-hardware**.
   Track every run (config, seed, data split, metric).
4. **Analyze.** Report SSIM/PSNR/NMSE + perceptual metrics; add statistics and
   ablation tables; make qualitative figures with difference maps. Watch for DL
   **hallucination** and out-of-distribution failure; pair metrics with reader
   judgment for clinical claims.
5. **Write.** Draft section by section (below), in the venue's LaTeX template.
6. **Submit & revise.** Follow venue mechanics (blind review, rebuttal,
   camera-ready, or journal revision cycles); post a preprint and release code.

## Choose the venue (it changes everything)

| | **CVPR / NeurIPS** (CS-ML) | **MICCAI** (medical imaging) | **MRM** (MR journal) |
|---|---|---|---|
| Format | IEEE/CVF, ~8 pp + refs | Springer LNCS, ~8–10 pp | Wiley, MRM LaTeX class, ~5000 words |
| Template | [cvpr-org/author-kit](https://github.com/cvpr-org/author-kit) | [LNCS guidelines](https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines) | [MRM class](https://onlinelibrary.wiley.com/journal/15222594/la_tex_class_file) |
| Review | double-blind + rebuttal | double-blind + rebuttal | single-blind, revision cycles |
| Emphasis | novelty, SOTA, benchmarks | method + clinical relevance | rigor, validation, physics, reproducibility |
| Code | expected ([Papers with Code](https://paperswithcode.com/)) | encouraged | [ISMRM RRSG](https://ismrm.github.io/rrsg/) |
| Cadence | annual deadline (check the CFP) | annual (spring) | rolling |

CVPR/NeurIPS reward a novel method beating strong baselines; MRM rewards
careful, reproducible, physically-sound work with validation. Frame the same
result differently for each.

## Writing the paper (section by section)

- **Title & abstract** — the claim in one line; abstract = problem, method,
  headline result, significance.
- **Introduction** — gap → contribution bullets (be specific and falsifiable).
- **Related work** — position against the survey from step 1; cite primary
  sources (see the hub `recon-methods` / `references`).
- **Method** — enough to reproduce: forward model, network/algorithm, training.
- **Experiments** — datasets, baselines, metrics, implementation; then results +
  **ablations**; qualitative figures with error/difference maps.
- **Discussion & limitations** — where it fails, OOD behavior, clinical caveats.
- **Reproducibility** — release code (see
  [releasing-research-code](https://github.com/paperswithcode/releasing-research-code));
  for ML-imaging follow **CLAIM**; for (f)MRI follow **COBIDAS** (both in the hub
  `publishing` reference). Archive a versioned release (e.g., Zenodo DOI).

## Resources & handoffs

- Manuscript logistics (journals, LaTeX classes, reporting standards, abstracts):
  hub `publishing` —
  https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/publishing.md
- Finding/monitoring literature: hub `literature-access` —
  https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/literature-access.md
- Preprints: arXiv (eess.IV / physics.med-ph / cs.CV). Reviews on OpenReview for
  some venues.

## What you can produce

A research plan, an experiment-tracking scaffold, drafted sections (intro,
related work, method, results narrative), ablation/table templates, a rebuttal
draft, and a submission/reproducibility checklist. Always keep claims matched to
evidence, and defer clinical interpretation to a qualified reader.