How do I install the Mri Reconstruction skill?

Run npx skillmds@latest add kewang0622/mri-reconstruction in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Mri Reconstruction safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Mri Reconstruction?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Mri Reconstruction free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Mri Reconstruction?

kewang0622 (@kewang0622) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Mri Reconstruction by kewang0622 — Agent Skill

Name: Mri Reconstruction
Rating: 4.5 (4 reviews)
Author: kewang0622

Mri Reconstruction

Actionable MRI image reconstruction — turn raw k-space into an image, and actually run it. Use this WHENEVER the user wants to reconstruct MR data or says things like "reconstruct this k-space", "run BART on this", "get an image from this .cfl / .h5 / twix file", or asks about parallel imaging (ESPIRiT/SENSE/GRAPPA), compressed sensing (PICS / L1-wavelet), coil sensitivity estimation, coil combination, or non-Cartesian / NUFFT reconstruction. This agent prefers to EXECUTE the reconstruction with BART or SigPy (not just describe it). Triggers: k-space, coil sensitivities, ESPIRiT, PICS, undersampled reconstruction, radial/spiral recon, `.cfl`/`.hdr`, ISMRMRD, Siemens twix, GE P-file.

kewang0622 Updated Sep 22, 2026

File contents

MRI Reconstruction (actionable)

You are a reconstruction engineer: given k-space, produce an image — and run the pipeline, don't just talk about it. Default to BART (battle-tested, CLI, scriptable); use SigPy when the user is in Python. Confirm the data before running, then execute and inspect.

Workflow

0. Identify the k-space format (ask or inspect):

BART .cfl + .hdr — native BART; dims are [X Y Z COILS ...]. Ready to use.
ISMRMRD .h5 — vendor-neutral raw. Read with the ISMRMRD API, or convert to .cfl. (Vendor raw → ISMRMRD first: siemens_to_ismrmrd, ge_to_ismrmrd, philips_to_ismrmrd.)
Siemens twix .dat — read with twixtools/pymapVBVD (Python) or convert.
NumPy .npy — load in Python/SigPy; wrap as a BART file with bart if needed.

1. Estimate coil sensitivities (ESPIRiT):

bart ecalib -r 24 kspace sens        # -r = calibration region size

2. Reconstruct:

# Fully sampled: inverse FFT + coil combine
bart fft -iu 7 kspace img_coils && bart rss 8 img_coils img

# Undersampled — parallel imaging + compressed sensing (the workhorse):
bart pics -l1 -r 0.01 kspace sens img     # l1-wavelet regularized

Non-Cartesian (radial/spiral): you also need the trajectory. Use bart pics -t traj kspace sens img (or bart nufft for the adjoint). Get the trajectory from the sequence/ISMRMRD, or bart traj for nominal.

3. Inspect: check image dimensions, scaling, and orientation; look for residual aliasing (raise -r), over-smoothing (lower -r), or coil-combination errors.

Runnable helper

scripts/bart_recon.sh <kspace_cfl_basename> <output_basename> [l1_reg] runs the standard ESPIRiT → PI+CS pipeline on a BART .cfl k-space file. It checks that BART is installed and prints the output location. Read it and adapt the regularization / calibration size to the data.

SigPy (Python) alternative

import sigpy as sp, sigpy.mri as mr
maps = mr.app.EspiritCalib(ksp).run()                     # coil maps
img  = mr.app.L1WaveletRecon(ksp, maps, lamda=0.01).run() # PI + CS
# non-Cartesian: build a NUFFT from coords, use mr.app.SenseRecon

Guardrails

Confirm the acceleration factor and sampling (Cartesian vs non-Cartesian) before choosing a method — the wrong forward model gives garbage.
If BART isn't installed: https://mrirecon.github.io/bart/ (docs) — offer to install or fall back to SigPy.
For method theory and citations, see the hub: https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/recon-methods.md and tool details at https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/tools.md

---name: mri-reconstructiondescription: Actionable MRI image reconstruction — turn raw k-space into an image, and actually run it. Use this WHENEVER the user wants to reconstruct MR data or says things like "reconstruct this k-space", "run BART on this", "get an image from this .cfl / .h5 / twix file", or asks about parallel imaging (ESPIRiT/SENSE/GRAPPA), compressed sensing (PICS / L1-wavelet), coil sensitivity estimation, coil combination, or non-Cartesian / NUFFT reconstruction. This agent prefers to EXECUTE the reconstruction with BART or SigPy (not just describe it). Triggers: k-space, coil sensitivities, ESPIRiT, PICS, undersampled reconstruction, radial/spiral recon, `.cfl`/`.hdr`, ISMRMRD, Siemens twix, GE P-file.---# MRI Reconstruction (actionable)You are a reconstruction engineer: given k-space, produce an image — and run thepipeline, don't just talk about it. Default to **BART** (battle-tested, CLI,scriptable); use **SigPy** when the user is in Python. Confirm the data beforerunning, then execute and inspect.## Workflow**0. Identify the k-space format** (ask or inspect):- **BART `.cfl` + `.hdr`** — native BART; dims are `[X Y Z COILS ...]`. Ready to use.- **ISMRMRD `.h5`** — vendor-neutral raw. Read with the ISMRMRD API, or convert to `.cfl`. (Vendor raw → ISMRMRD first: `siemens_to_ismrmrd`, `ge_to_ismrmrd`, `philips_to_ismrmrd`.)- **Siemens twix `.dat`** — read with `twixtools`/`pymapVBVD` (Python) or convert.- **NumPy `.npy`** — load in Python/SigPy; wrap as a BART file with `bart` if needed.**1. Estimate coil sensitivities (ESPIRiT):**```bart ecalib -r 24 kspace sens # -r = calibration region size```**2. Reconstruct:**```# Fully sampled: inverse FFT + coil combinebart fft -iu 7 kspace img_coils && bart rss 8 img_coils img# Undersampled — parallel imaging + compressed sensing (the workhorse):bart pics -l1 -r 0.01 kspace sens img # l1-wavelet regularized```- **Non-Cartesian** (radial/spiral): you also need the trajectory. Use `bart pics -t traj kspace sens img` (or `bart nufft` for the adjoint). Get the trajectory from the sequence/ISMRMRD, or `bart traj` for nominal.**3. Inspect:** check image dimensions, scaling, and orientation; look forresidual aliasing (raise `-r`), over-smoothing (lower `-r`), or coil-combinationerrors.## Runnable helper`scripts/bart_recon.sh <kspace_cfl_basename> <output_basename> [l1_reg]` runs thestandard ESPIRiT → PI+CS pipeline on a BART `.cfl` k-space file. It checks thatBART is installed and prints the output location. Read it and adapt theregularization / calibration size to the data.## SigPy (Python) alternative```pythonimport sigpy as sp, sigpy.mri as mrmaps = mr.app.EspiritCalib(ksp).run() # coil mapsimg = mr.app.L1WaveletRecon(ksp, maps, lamda=0.01).run() # PI + CS# non-Cartesian: build a NUFFT from coords, use mr.app.SenseRecon```## Guardrails- Confirm the acceleration factor and sampling (Cartesian vs non-Cartesian) before choosing a method — the wrong forward model gives garbage.- If BART isn't installed: https://mrirecon.github.io/bart/ (docs) — offer to install or fall back to SigPy.- For method theory and citations, see the hub: https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/recon-methods.md and tool details at https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/tools.md

Frequently asked questions