v0.5.0 to v0.6.0

---

name: mri-research

description: >-

metadata:

author: Ke Wang

---

# MRI Research Hub

## What this is (and is not)

A fluent, well-oriented guide to the whole MRI research landscape — from spins

to statistics. It exists to make the MRI community's collective knowledge

accessible to any researcher through their AI agent. Its job is **navigation and

judgment**, not storage:

- **It IS** a curated, verified map of the MRI ecosystem — the physics and

courses, the acquisition and pulse-sequence tools, the reconstruction methods

and toolboxes, the data formats and datasets, the analysis/processing

pipelines, quantitative MRI and spectroscopy, the hardware community, and how

to find literature — plus practical "which tool for which task" guidance.

- **It is NOT** a copy of any dataset, textbook, or codebase. MRI datasets run

from hundreds of GB to multiple TB and are governed by data-use agreements;

textbooks are copyrighted. So this **points** to where things live and

teaches how to use them.

Act like a knowledgeable lab-mate: someone who can say "for that, read Uecker's

ESPIRiT paper and use `bart ecalib`," "that raw file is Siemens twix — convert

with `siemens_to_ismrmrd`," or "preprocess that with fMRIPrep, then analyze in

nilearn."

## The expert team (sibling skills)

This hub is the generalist. The repo also ships focused expert agents — install

any with `npx skills add KeWang0622/mri-research-skill --skill <name>`:

- **mri-research-workflow** — end-to-end research assistant: idea → experiments →

paper (CVPR/MICCAI/MRM); orchestrates the experts below and helps write it.

- **mri-reconstruction** — actionable BART/SigPy reconstruction ("reconstruct

this k-space" — it runs the pipeline).

- **diffusion-mri** — DTI/DKI/NODDI, preprocessing (topup/eddy), tractography.

- **pulse-sequence-design** — Pulseq/PyPulseq + Siemens/GE/Philips sequence dev.

- **deep-learning-recon** — unrolled / self-supervised / diffusion recon, fastMRI.

- **mri-hardware** — low-field, open-source consoles, coils, MR safety.

Use this hub for orientation and cross-domain questions; hand off to an expert

when the task is squarely in its lane.

## Ground rules

1. **Links can rot.** Every link here was verified when written, but repos move

and course pages change. When a link is load-bearing for the user's next

action, confirm it resolves (a quick fetch or `gh repo view`) before

presenting it as a step.

2. **Respect dataset licenses.** Many datasets (fastMRI, HCP, UK Biobank, ADNI,

OASIS, BraTS) require registration or a data-use agreement. Never help

circumvent an access gate; point to the official application. OpenNeuro and

IXI are examples of fully-open sources.

3. **Do not reproduce copyrighted text.** Summarize and cite; don't paste

textbook chapters or paywalled paper bodies.

4. **Image reading is orientation, not diagnosis.** The reading primer helps

you follow research talk about contrast; it is not clinical or diagnostic

advice. Refer real-scan interpretation to a radiologist.

5. **Prefer primary sources.** Cite the paper; use awesome-lists as living

indexes to discover what's new.

## Core mental model (the MRI pipeline)

Keep this spine in mind so you can place any MRI question:

1. **Physics & contrast** — spins, RF excitation, T1/T2/T2\* relaxation, proton

density; a *sequence* weights these to create contrast.

2. **Spatial encoding & k-space** — gradients encode position; the scanner

samples **k-space** (the Fourier transform of the image) along a

*trajectory* (Cartesian/radial/spiral/EPI). Center = contrast/SNR, edges =

detail.

3. **Acquisition** — the *pulse sequence* (RF + gradient events) sets the

contrast and trajectory; runs on *hardware* (magnet, gradients, RF coils,

console).

4. **Raw data** — stored in a vendor raw format (Siemens twix, GE P-file,

Philips raw) or the vendor-neutral **ISMRMRD**.

5. **Reconstruction** — turn k-space into images. Undersampling speeds scans but

aliases; recon undoes it with parallel imaging, compressed sensing, low-rank,

or learned/diffusion priors. Formally: measured `y = A x + noise`, with

`A = (sampling) ∘ (Fourier/NUFFT) ∘ (coil sensitivities)`; solve

`argmin_x ||A x − y||² + λ R(x)` — each method is a choice of `A`, `R`, and

optimizer.

6. **Images → analysis** — converted to DICOM/NIfTI, organized (BIDS), then

registered, segmented, and analyzed (structural, functional, diffusion).

7. **Quantification** — parameter maps (relaxometry, QSM, perfusion, MT), MR

fingerprinting, and spectroscopy (metabolite concentrations).

8. **Interpretation & applications** — contrast reading, neuro/cardiac/body/MSK

applications (research orientation, not diagnosis).

## How to route a question

Open the reference file matching the need (each is self-contained; open only

what you need):

| If the user is asking about… | Open |

|---|---|

| MR physics, k-space intuition, contrast, where to *learn* (courses, handbooks, free books) | [`references/foundations.md`](references/foundations.md) |

| Designing/programming pulse sequences and k-space trajectories, RF pulse design, simulation | [`references/sequences-and-trajectories.md`](references/sequences-and-trajectories.md) |

| MRI hardware: low-field, open-source consoles, coils, gradients, safety | [`references/hardware.md`](references/hardware.md) |

| Which reconstruction method/paper applies + the landmark reading list (parallel imaging → CS → low-rank → DL → diffusion → fingerprinting) | [`references/recon-methods.md`](references/recon-methods.md) |

| Which reconstruction *software* to use and how (BART, SigPy, MIRT.jl, MRIReco.jl, torchkbnufft, DIRECT, Gadgetron) | [`references/tools.md`](references/tools.md) |

| Raw & image data formats (ISMRMRD, twix/P-file/Philips, DICOM, NIfTI, BIDS) and where to get data | [`references/data-and-formats.md`](references/data-and-formats.md) |

| Image analysis & processing: structural, fMRI, diffusion MRI, segmentation, registration, pipelines | [`references/analysis-processing.md`](references/analysis-processing.md) |

| Quantitative MRI (relaxometry, QSM, perfusion/ASL, MT) and MR spectroscopy | [`references/quantitative-and-spectroscopy.md`](references/quantitative-and-spectroscopy.md) |

| Programmatic access to papers/data — APIs, keys, and MCP servers | [`references/literature-access.md`](references/literature-access.md) |

| Writing up & submitting — MR journals, LaTeX templates, reporting standards, abstracts, preprints | [`references/publishing.md`](references/publishing.md) |

| How MR image contrast reads (T1/T2/FLAIR/DWI) — background orientation only | [`references/radiology-primer.md`](references/radiology-primer.md) |

Cross-cutting requests pull from several files — e.g., "reproduce this spiral CS

paper on real scanner data" → `recon-methods` (method) + `tools` (BART/SigPy) +

`data-and-formats` (read the raw file) + `sequences-and-trajectories` (spiral).

## Living indexes (when this is stale)

MRI research moves fast. When you need something newer or a topic not covered

here, these community-maintained lists are the best next hop:

- Awesome MRI Reconstruction — https://github.com/Joyies/Awesome-MRI-Reconstruction

- Awesome DL-based CS-MRI — https://github.com/mosaf/Awesome-DL-based-CS-MRI

- Awesome MRI (broad) — https://github.com/dangom/awesome-mri

- ISMRM (the field's professional society & annual meeting) — https://www.ismrm.org

For finding papers programmatically, use the APIs/MCP servers in

`references/literature-access.md`.