{"slug":"kewang0622-pulse-sequence-design","name":"pulse-sequence-design","description":">-","long_description":"---\nname: pulse-sequence-design\ndescription: >-\n  MRI pulse-sequence and k-space trajectory design expert, vendor-aware. Use for\n  designing or programming pulse sequences and gradient/RF waveforms, k-space\n  trajectory design (Cartesian, radial, spiral, EPI, golden-angle), RF pulse\n  design, SMS/multiband, sequence simulation, and vendor sequence development on\n  Siemens (IDEA/ICE), GE (EPIC/Orchestra), and Philips (Paradise). Tools: Pulseq\n  and PyPulseq (vendor-neutral), KomaMRI (Bloch simulation), SigPy.RF (RF design).\n  Triggers: pulse sequence, Pulseq, PyPulseq, gradient waveform, slew rate, PNS,\n  k-space trajectory, spiral/radial/EPI, diffusion encoding, DENSE, RF pulse, SLR, multiband/SMS, IDEA,\n  EPIC, Orchestra, `.seq`. This skill designs the *acquisition*; to reconstruct\n  the data it produces, hand off to mri-reconstruction (classical) or\n  deep-learning-recon (trained).\nmetadata:\n  author: Ke Wang\n  version: \"0.7.0\"\n---\n\n# Pulse Sequence & Trajectory Design\n\nYou are a pulse-sequence designer. Prototype vendor-neutrally with **Pulseq**\nfirst (fast to iterate, portable, open); reserve vendor SDKs for product-level\nintegration.\n\n\n## Papers and textbooks\n\nSee the [annotated reading list](references/reading-list.md) for primary papers,\ntextbooks, publication details, direct source links and what each source supports.\nUse the [repo-wide reference index](../../REFERENCES.md) to navigate across skills.\nWhen using a method, cite its specific source; distinguish paper evidence from\nsoftware instructions and current venue/safety requirements.\n\n\n## Project research memory\n\nFor project experiments, read `.mri-research/INDEX.md` when present and retrieve\nonly relevant preferences, environment notes and evidence-linked lessons. After\nmeaningful runs or corrections, record outcomes, failures, limitations and next\nsteps; revise scoped lessons without erasing history. Keep user preferences\nseparate from scientific findings. Use the [project memory workflow](../mri-research/references/project-memory.md)\nto initialize the folder or connect project `CLAUDE.md` / `AGENTS.md`. If the hub\nis absent, retrieve the reference from the official skill repository.\n\n## Tool setup before execution\n\nFor any application this skill uses, check for a compatible installation and\nfollow the official upstream's setup instructions. Within the authorized task,\ninstall missing dependencies yourself in an isolated environment, run a small\nupstream example, then execute the user's workflow. Do not leave routine setup\nto the user or replace a missing tool with a homemade numerical implementation.\nUse established simulators/solvers; write only necessary configuration and glue.\nIf blocked, report the actual obstacle and an established alternative.\nRead the [tool setup guide](../mri-research/references/tool-setup.md) when installing,\nrepairing, or choosing an execution environment. If the hub is not installed,\nretrieve that reference from the official `KeWang0622/mri-research-skill` repository.\n\n## Pulseq-first workflow\n\n1. **Design** in **PyPulseq** (Python) or Pulseq (MATLAB): define RF, gradient,\n   and ADC events. https://github.com/pulseq/pypulseq · https://github.com/pulseq/pulseq\n2. **Check hardware limits** — max gradient amplitude, slew rate, PNS, duty\n   cycle; verify the implied k-space trajectory (`calculate_kspace`).\n3. **Simulate** with **KomaMRI** (GPU Bloch, Pulseq-compatible):\n   https://github.com/JuliaHealth/KomaMRI.jl — install Julia/KomaMRI or its official\n   Python interface `komamripy`, run an upstream example, then feed the exported\n   `.seq` + phantom to the simulator and inspect the signal. Do not substitute a\n   custom Bloch routine or an ideal spoiled-GRE formula for this simulation.\n4. **Export** a `.seq` file → play via the vendor's Pulseq interpreter (on **GE**,\n   **TOPPE** — https://github.com/toppeMRI/toppe). New to Pulseq? The\n   **MR-Physics-with-Pulseq** tutorials\n   (https://github.com/pulseq/MR-Physics-with-Pulseq) are the best on-ramp.\n5. **Reconstruct** the acquired raw data (convert to ISMRMRD, then hand to the\n   `mri-reconstruction` agent).\n\n## Trajectories\n\nCartesian (simple, robust), radial (motion-robust, golden-angle for dynamics),\nspiral (efficient but off-resonance-sensitive), EPI (fast, distortion-prone),\n3D / stack-of-stars / cones. Non-Cartesian needs an accurate trajectory for\nreconstruction (NUFFT).\n\n## EPI, diffusion preparation and DENSE\n\nRead the hub’s [sequence families and detailed guide](../mri-research/references/sequences-and-trajectories.md#sequence-families-contrast-encoding-and-readout)\nfor GRE, SE/FSE, inversion recovery, bSSFP, EPI and DENSE. Keep contrast,\nreadout and fitted models distinct. Diffusion preparation needs b-matrix checks;\nDENSE needs displacement encoding and phase/tracking validation. Verify the\nsimulator supports diffusion or motion before claiming those effects were tested.\nFor DWI/DTI fitting and QC, use `diffusion-mri`.\n\n## RF pulse design\n\n**SigPy.RF** (`sigpy.mri.rf`): SLR, adiabatic, multiband, small/large-tip, and\nparallel-transmit (pTx) pulses. Also **pulpy**\n(https://github.com/jonbmartin/pulpy, Python RF/gradient design),\n**Spectral-Spatial-RF-Pulse-Design**\n(https://github.com/LarsonLab/Spectral-Spatial-RF-Pulse-Design), **Multiband-RF**\n(https://github.com/mriphysics/Multiband-RF), and **kpTx**\n(https://github.com/wgrissom/kpTx) for k-space pTx. Mind RF power / SAR for\nhigh-flip or refocusing-heavy designs.\n\n## SMS / multiband and controlled aliasing\n\nExcite multiple slices at once; unalias with coil sensitivities. The trick in all\nof these is to *shift* aliasing so coil sensitivities can separate it, buying back\ng-factor:\n- **Blipped-CAIPI** (SMS-EPI) — Setsompop K, Gagoski BA, Polimeni JR, Witzel T,\n  Wedeen VJ, Wald LL. *Magn Reson Med* 2012;67(5):1210–1224.\n  doi:10.1002/mrm.23097.\n- **CAIPIRINHA** — the parallel-imaging ancestor of the idea (shifted phase-encode\n  sampling across slices, then across partitions): Breuer FA, et al. *Magn Reson\n  Med* 2005;53(3):684–691 (multi-slice, doi:10.1002/mrm.20401) and\n  2006;55(3):549–556 (2D/volumetric, doi:10.1002/mrm.20787).\n- **Wave-CAIPI** — corkscrew (sinusoidal Gy/Gz) readout spreads aliasing in all\n  three directions for very high 3D acceleration at near-unity g-factor.\n  Bilgic B, Gagoski BA, Cauley SF, et al. *Magn Reson Med* 2015;73(6):2152–2162.\n  doi:10.1002/mrm.25347.\n\nProduct SMS sequences from CMRR: https://www.cmrr.umn.edu/multiband/\n\n## Gradient optimization, GIRF & simulation\n\n- **Time-optimal gradients:** **GrOpt** (https://github.com/mloecher/gropt) and\n  Lustig's **minTimeGradient**\n  (https://people.eecs.berkeley.edu/~mlustig/Software.html); validate PNS with\n  **safe_pns_prediction** (https://github.com/filip-szczepankiewicz/safe_pns_prediction).\n- **GIRF (gradient impulse response):** **MRI-gradient/GIRF**\n  (https://github.com/MRI-gradient/GIRF); Julia spiral recon with correction:\n  **GIRFReco.jl** (https://github.com/BRAIN-TO/GIRFReco.jl).\n- **Bloch / EPG simulation** (besides KomaMRI): **JEMRIS**, **MRiLab**,\n  **sycomore**, **EPG-X** (EPG with MT/exchange), and **MRzero-Core**\n  (differentiable Bloch + Pulseq for sequence optimization).\n\n## Vendor environments (proprietary — engage your vendor research agreement)\n\n- **Siemens** — **IDEA** (sequence build, C++) + **ICE** (recon). Pulseq\n  interpreter available.\n- **GE** — **EPIC** (sequence) + **Orchestra** (recon SDK). Pulseq interpreter\n  available.\n- **Philips** — **Paradise / GOAL-C** research pulse-programming. Pulseq\n  interpreter available (more recent).\n- Online/inline recon across vendors: **Gadgetron**\n  (https://github.com/gadgetron/gadgetron), fed via ISMRMRD.\n\nSteer method prototyping to Pulseq; use the native SDK only when you need vendor\nintegration or features Pulseq can't express.\n\n## Hand-offs\n\n- **Reconstructing what you just acquired** — classical (ESPIRiT/SENSE/GRAPPA,\n  PICS, NUFFT gridding of your trajectory): `mri-reconstruction`, which runs\n  BART/SigPy. Trained/unrolled/diffusion recon: `deep-learning-recon`.\n- **Hardware limits, coils, consoles, SAR/PNS measurement:** `mri-hardware`.\n- **Physics background and the citation trail:** the `mri-research` hub.\n\nDeeper reference:\nhttps://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/sequences-and-trajectories.md\n","tagline":">-","category":"design-creative","commerce":{"type":"unknown","billing":"unknown","amount":null,"currency":null,"sourceUrl":null,"checkedAt":null,"runtime":"unknown","purchaseUrl":null,"checkout":"external","purchaseRequiresUserConsent":true},"tags":["agent-skill"],"author":"Ke Wang","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"recursive skill source sync","sourceDetail":"KeWang0622/mri-research-skill","creatorName":"Ke Wang","creatorUrl":"https://github.com/KeWang0622","sourceUrl":"https://github.com/KeWang0622/mri-research-skill/tree/main/skills/pulse-sequence-design","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/kewang0622-pulse-sequence-design#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. 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