mri-reconstruction · diff
v0.1.0 to v0.7.0
49 added, 21 removed. Audit A to A.
---
name: mri-reconstruction
description: >-
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.
+ SigPy (not just describe it). It covers classical/analytic reconstruction —
+ for anything TRAINED (unrolled networks, VarNet, self-supervised, diffusion
+ priors, fastMRI models) hand off to the deep-learning-recon skill. Triggers:
+ k-space, coil sensitivities, ESPIRiT, PICS, undersampled reconstruction,
+ radial/spiral recon, `.cfl`/`.hdr`, ISMRMRD, Siemens twix, GE P-file.
metadata:
author: Ke Wang
- version: "0.1.0"
+ version: "0.7.0"
---
# 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 reads its own `.cfl`;
- it does **not** natively ingest NumPy or ISMRMRD, so those need an explicit
- write step:
+ **0. Identify the k-space format** (ask or inspect). BART works in its own
+ `.cfl`/`.hdr`, so other formats need a conversion step:
- **BART `.cfl` + `.hdr`** — native; dims must be `[X Y Z COILS ...]` (coils on
dim 3). Ready to use.
- - **Siemens twix `.dat`** — `bart twixread` writes a `.cfl` directly; or read in
- Python with `twixtools`/`pymapVBVD` then write a `.cfl`.
- - **NumPy array** — write a `.cfl` with BART's Python helper
- `cfl.writecfl(name, array)` (shipped in BART's `python/` dir; also `bartpy`).
+ - **Siemens twix `.dat`** — `bart twixread -A meas.dat ksp` writes a `.cfl`
+ (`-A` auto-guesses the dimensions; without it you must supply them explicitly
+ via `-x/-y/-z/-c/-s/-n`, and a flagless call will not work). Then confirm with
+ `bart show -m ksp`. Alternative: read in Python with `twixtools`/`pymapVBVD`
+ and write a `.cfl`.
+ - **NumPy array** — write a `.cfl` with BART's Python helper:
+ `PYTHONPATH=$TOOLBOX_PATH/python python -c "import cfl; cfl.writecfl(name, arr)"`
+ (`cfl.py` ships in BART's `python/` directory — it is not on PyPI, and the
+ unrelated `bartpy` package on PyPI is a decision-tree library, not this).
Make sure coils land on dim 3.
- - **ISMRMRD `.h5`** — read with the Python `ismrmrd` package (or Gadgetron),
- assemble the k-space array, then `cfl.writecfl`. Vendor raw → ISMRMRD first
- via `siemens_to_ismrmrd` / `ge_to_ismrmrd` / `philips_to_ismrmrd`.
+ - **ISMRMRD `.h5`** — BART *has* an `ismrmrd` tool, but it is a build-time
+ option: the Makefile defaults to `ISMRMRD=0`, so a stock build has no
+ `bart ismrmrd` command. Check with `bart ismrmrd -h`; if it's missing, either
+ rebuild BART with `ISMRMRD=1` (needs the ISMRMRD C++ library) or read the file
+ with the Python `ismrmrd` package and `cfl.writecfl`. Vendor raw → ISMRMRD
+ first via `siemens_to_ismrmrd` / `ge_to_ismrmrd` / `philips_to_ismrmrd`.
**1. Estimate coil sensitivities (ESPIRiT):**
```
- bart ecalib -r 24 kspace sens # -r = calibration region size
+ bart ecalib -m1 -r 24 kspace sens
```
+ `-m1` matters: `ecalib` computes **two** ESPIRiT map sets by default, and `pics`
+ then returns a soft-SENSE result with a size-2 MAPS dimension instead of a single
+ image — a silent wrong answer. `-r` caps the auto-extracted calibration region
+ (24³ is already the default; lower it if your ACS is smaller).
**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
+ - **Non-Cartesian** (radial/spiral/cones/rosette): you also need the trajectory.
+ Use `bart pics -t traj kspace sens img`. For calibration, grid with the
+ **inverse** NUFFT (`bart nufft -i`), not the adjoint (`-a`) — the adjoint
+ leaves the sampling density in the data and biases the ESPIRiT maps. Get the
trajectory from the sequence/ISMRMRD, or `bart traj` for nominal.
+ - **EPI is Cartesian.** Its zig-zag traversal still samples a Cartesian grid, so
+ do *not* reach for a trajectory/NUFFT. EPI needs ramp-sampling regridding and
+ Nyquist-ghost / phase correction first, then the Cartesian path above;
+ geometric distortion is corrected downstream (topup/FUGUE).
**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> <output_cfl> [l1_reg] [traj_cfl]` runs an
ESPIRiT → PI+CS pipeline on a BART `.cfl` k-space file. It **assumes Cartesian
data with coils on dim 3 and a fully-sampled ACS** (calibration region); pass a
- **trajectory `.cfl`** as the 4th argument for radial/spiral/EPI. It warns about
- these assumptions but can't fully verify them — check the header and adapt the
+ **trajectory `.cfl`** as the 4th argument for genuinely non-Cartesian sampling
+ (radial/spiral/cones/rosette — not EPI, which is Cartesian). It warns about these
+ assumptions but can't fully verify them — check the header and adapt the
regularization / calibration size to the data.
## SigPy (Python) alternative
```python
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.
+ - If BART isn't installed: https://codeberg.org/mrirecon/bart (source, active)
+ with docs at https://mrirecon.codeberg.page/ — offer to install or fall back to
+ SigPy. Note the `github.com/mrirecon/bart` mirror is archived as of 2026.
+ - **Report the SNR cost, not just the image.** Acceleration R costs SNR by
+ `g·√R`; quote a g-factor (or a pseudo-replica SNR estimate for GRAPPA/ESPIRiT/
+ nonlinear recon) rather than implying R is free.
+ - **Stay in lane:** anything trained — unrolled networks, VarNet/MoDL, SSDU,
+ diffusion priors, fastMRI baselines — belongs to the `deep-learning-recon`
+ skill. Designing the *acquisition* or the trajectory belongs to
+ `pulse-sequence-design`; this skill consumes a trajectory, it doesn't design one.
- 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