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.

Papers and textbooks

See the annotated reading list for primary papers, textbooks, publication details, direct source links and what each source supports. Use the repo-wide reference index to navigate across skills. When using a method, cite its specific source; distinguish paper evidence from software instructions and current venue/safety requirements.

Project research memory

For project experiments, read .mri-research/INDEX.md when present and retrieve only relevant preferences, environment notes and evidence-linked lessons. After meaningful runs or corrections, record outcomes, failures, limitations and next steps; revise scoped lessons without erasing history. Keep user preferences separate from scientific findings. Use the project memory workflow to initialize the folder or connect project CLAUDE.md / AGENTS.md. If the hub is absent, retrieve the reference from the official skill repository.

Tool setup before execution

For any application this skill uses, check for a compatible installation and follow the official upstream's setup instructions. Within the authorized task, install missing dependencies yourself in an isolated environment, run a small upstream example, then execute the user's workflow. Do not leave routine setup to the user or replace a missing tool with a homemade numerical implementation. Use established simulators/solvers; write only necessary configuration and glue. If blocked, report the actual obstacle and an established alternative. Read the tool setup guide when installing, repairing, or choosing an execution environment. If the hub is not installed, retrieve that reference from the official KeWang0622/mri-research-skill repository.

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 — install Julia/KomaMRI or its official Python interface komamripy, run an upstream example, then feed the exported .seq + phantom to the simulator and inspect the signal. Do not substitute a custom Bloch routine or an ideal spoiled-GRE formula for this simulation.
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).

EPI, diffusion preparation and DENSE

Read the hub’s sequence families and detailed guide for GRE, SE/FSE, inversion recovery, bSSFP, EPI and DENSE. Keep contrast, readout and fitted models distinct. Diffusion preparation needs b-matrix checks; DENSE needs displacement encoding and phase/tracking validation. Verify the simulator supports diffusion or motion before claiming those effects were tested. For DWI/DTI fitting and QC, use diffusion-mri.

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 and controlled aliasing

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