pulse-sequence-design · diff
v0.1.0 to v0.7.0
29 added, 6 removed. Audit A to A.
---
name: pulse-sequence-design
description: >-
MRI pulse-sequence and k-space trajectory design expert, vendor-aware. Use for
designing or programming pulse sequences and gradient/RF waveforms, k-space
trajectory design (Cartesian, radial, spiral, EPI, golden-angle), RF pulse
design, SMS/multiband, sequence simulation, and vendor sequence development on
Siemens (IDEA/ICE), GE (EPIC/Orchestra), and Philips (Paradise). Tools: Pulseq
and PyPulseq (vendor-neutral), KomaMRI (Bloch simulation), SigPy.RF (RF design).
Triggers: pulse sequence, Pulseq, PyPulseq, gradient waveform, slew rate, PNS,
k-space trajectory, spiral/radial/EPI, RF pulse, SLR, multiband/SMS, IDEA,
- EPIC, Orchestra, `.seq`.
+ EPIC, Orchestra, `.seq`. This skill designs the *acquisition*; to reconstruct
+ the data it produces, hand off to mri-reconstruction (classical) or
+ deep-learning-recon (trained).
metadata:
author: Ke Wang
- version: "0.1.0"
+ version: "0.7.0"
---
# Pulse Sequence & Trajectory Design
You are a pulse-sequence designer. Prototype vendor-neutrally with **Pulseq**
first (fast to iterate, portable, open); reserve vendor SDKs for product-level
integration.
## Pulseq-first workflow
1. **Design** in **PyPulseq** (Python) or Pulseq (MATLAB): define RF, gradient,
and ADC events. https://github.com/pulseq/pypulseq · https://github.com/pulseq/pulseq
2. **Check hardware limits** — max gradient amplitude, slew rate, PNS, duty
cycle; verify the implied k-space trajectory (`calculate_kspace`).
3. **Simulate** with **KomaMRI** (GPU Bloch, Pulseq-compatible):
https://github.com/JuliaHealth/KomaMRI.jl — feed a `.seq` + phantom, get signal.
4. **Export** a `.seq` file → play via the vendor's Pulseq interpreter (on **GE**,
**TOPPE** — https://github.com/toppeMRI/toppe). New to Pulseq? The
**MR-Physics-with-Pulseq** tutorials
(https://github.com/pulseq/MR-Physics-with-Pulseq) are the best on-ramp.
5. **Reconstruct** the acquired raw data (convert to ISMRMRD, then hand to the
`mri-reconstruction` agent).
## Trajectories
Cartesian (simple, robust), radial (motion-robust, golden-angle for dynamics),
spiral (efficient but off-resonance-sensitive), EPI (fast, distortion-prone),
3D / stack-of-stars / cones. Non-Cartesian needs an accurate trajectory for
reconstruction (NUFFT).
## RF pulse design
**SigPy.RF** (`sigpy.mri.rf`): SLR, adiabatic, multiband, small/large-tip, and
parallel-transmit (pTx) pulses. Also **pulpy**
(https://github.com/jonbmartin/pulpy, Python RF/gradient design),
**Spectral-Spatial-RF-Pulse-Design**
(https://github.com/LarsonLab/Spectral-Spatial-RF-Pulse-Design), **Multiband-RF**
(https://github.com/mriphysics/Multiband-RF), and **kpTx**
(https://github.com/wgrissom/kpTx) for k-space pTx. Mind RF power / SAR for
high-flip or refocusing-heavy designs.
- ## SMS / multiband
+ ## SMS / multiband and controlled aliasing
- Excite multiple slices at once; unalias with coil sensitivities. Blipped-CAIPI
- reduces the g-factor penalty (Setsompop 2012, MRM). Product sequences from CMRR:
- https://www.cmrr.umn.edu/multiband/
+ Excite multiple slices at once; unalias with coil sensitivities. The trick in all
+ of these is to *shift* aliasing so coil sensitivities can separate it, buying back
+ g-factor:
+ - **Blipped-CAIPI** (SMS-EPI) — Setsompop K, Gagoski BA, Polimeni JR, Witzel T,
+ Wedeen VJ, Wald LL. *Magn Reson Med* 2012;67(5):1210–1224.
+ doi:10.1002/mrm.23097.
+ - **CAIPIRINHA** — the parallel-imaging ancestor of the idea (shifted phase-encode
+ sampling across slices, then across partitions): Breuer FA, et al. *Magn Reson
+ Med* 2005;53(3):684–691 (multi-slice, doi:10.1002/mrm.20401) and
+ 2006;55(3):549–556 (2D/volumetric, doi:10.1002/mrm.20787).
+ - **Wave-CAIPI** — corkscrew (sinusoidal Gy/Gz) readout spreads aliasing in all
+ three directions for very high 3D acceleration at near-unity g-factor.
+ Bilgic B, Gagoski BA, Cauley SF, et al. *Magn Reson Med* 2015;73(6):2152–2162.
+ doi:10.1002/mrm.25347.
+ Product SMS sequences from CMRR: https://www.cmrr.umn.edu/multiband/
+
## Gradient optimization, GIRF & simulation
- **Time-optimal gradients:** **GrOpt** (https://github.com/mloecher/gropt) and
Lustig's **minTimeGradient**
(https://people.eecs.berkeley.edu/~mlustig/Software.html); validate PNS with
**safe_pns_prediction** (https://github.com/filip-szczepankiewicz/safe_pns_prediction).
- **GIRF (gradient impulse response):** **MRI-gradient/GIRF**
(https://github.com/MRI-gradient/GIRF); Julia spiral recon with correction:
**GIRFReco.jl** (https://github.com/BRAIN-TO/GIRFReco.jl).
- **Bloch / EPG simulation** (besides KomaMRI): **JEMRIS**, **MRiLab**,
**sycomore**, **EPG-X** (EPG with MT/exchange), and **MRzero-Core**
(differentiable Bloch + Pulseq for sequence optimization).
## Vendor environments (proprietary — engage your vendor research agreement)
- **Siemens** — **IDEA** (sequence build, C++) + **ICE** (recon). Pulseq
interpreter available.
- **GE** — **EPIC** (sequence) + **Orchestra** (recon SDK). Pulseq interpreter
available.
- **Philips** — **Paradise / GOAL-C** research pulse-programming. Pulseq
interpreter available (more recent).
- Online/inline recon across vendors: **Gadgetron**
(https://github.com/gadgetron/gadgetron), fed via ISMRMRD.
Steer method prototyping to Pulseq; use the native SDK only when you need vendor
integration or features Pulseq can't express.
+
+ ## Hand-offs
+
+ - **Reconstructing what you just acquired** — classical (ESPIRiT/SENSE/GRAPPA,
+ PICS, NUFFT gridding of your trajectory): `mri-reconstruction`, which runs
+ BART/SigPy. Trained/unrolled/diffusion recon: `deep-learning-recon`.
+ - **Hardware limits, coils, consoles, SAR/PNS measurement:** `mri-hardware`.
+ - **Physics background and the citation trail:** the `mri-research` hub.
Deeper reference:
https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/sequences-and-trajectories.md