About

Imported from luokai0/ai-agent-skills-by-luo-kai (ai-agent-skills/16-engineering (by Luo Kai)/biomedical-imaging-expert/SKILL.md). Install upstream with npx skills add luokai0/ai-agent-skills-by-luo-kai --skill biomedical-imaging-expert. Copyright stays with the author.

Biomedical Imaging Expert

Name: biomedical-imaging-expert
Author: luokai0

Before Starting

Which imaging modality?
Clinical diagnosis or research application?
Image acquisition or image processing focus?

Core Expertise Areas

X-ray and CT

X-ray production: electrons accelerated to anode, bremsstrahlung and characteristic radiation. Attenuation: Beer-Lambert law, tissue contrast from differential attenuation. CT reconstruction: filtered backprojection or iterative algorithms from projections. Hounsfield units: CT number scale, air -1000, water 0, bone +1000. Dose: effective dose in mSv, CT higher than plain X-ray, ALARA principle.

MRI

Nuclear magnetic resonance: protons align in B0 field, RF pulse tips magnetization. T1 relaxation: longitudinal recovery, fat bright on T1. T2 relaxation: transverse decay, water bright on T2. K-space: raw MRI data in frequency domain, Fourier transform gives image. Sequences: spin echo, gradient echo, EPI, each with different contrast and speed.

Ultrasound

Pulse-echo: transducer transmits pulse, receives echoes from tissue interfaces. Acoustic impedance: Z = rho times c, mismatch causes reflection. Frequency trade-off: higher frequency gives better resolution but less penetration. Doppler: frequency shift from moving blood cells, measures flow velocity. Modes: A-mode, B-mode, M-mode, Doppler, 3D ultrasound.

Nuclear Medicine

PET: positron emission tomography, FDG tracer, metabolic imaging. SPECT: single photon emission CT, lower resolution than PET but cheaper. Radiotracer: injected, accumulates in target tissue, emits detectable radiation. PET-CT: combined anatomical and functional imaging, standard in oncology.

Best Practices

Match imaging modality to clinical question and patient safety
Use appropriate acquisition protocols to optimize image quality vs dose
Validate image processing algorithms on representative clinical datasets
Consider motion artifacts and plan acquisition to minimize them

Common Pitfalls

Pitfall	Fix
Ignoring partial volume effect in CT	Account for voxel size when measuring small structures
MRI metal artifacts	Screen patients for implants, use metal artifact reduction sequences
Ultrasound shadowing from bone or gas	Interpret artifacts in clinical context
Registration errors in multi-modal imaging	Use rigid or deformable registration as appropriate

Related Skills

medical-devices-expert
signal-processing-expert
machine-learning-expert

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/luokai0-ai-agent-skills-by-luo-kai-biomedical-imaging-expert/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

luokai0-ai-agent-skills-by-luo-kai-biomedical-imaging-expert.ocm.jsonjson

{
  "ocm": "1",
  "id": "luokai0-ai-agent-skills-by-luo-kai-biomedical-imaging-expert",
  "kind": "skill",
  "name": "biomedical-imaging-expert",
  "description": "Expert-level biomedical imaging covering X-ray, CT, MRI, ultrasound, nuclear medicine, optical imaging, and image processing for clinical and research applications.",
  "publisher": "luokai0",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "medical"
    ],
    "tags": [
      "skill-md",
      "biomedical-imaging",
      "mri",
      "ct",
      "ultrasound",
      "x-ray",
      "pet",
      "image-processing",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Expert-level biomedical imaging covering X-ray, CT, MRI, ultrasound, nuclear medicine, optical imaging, and image processing for clinical and research applications."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/luokai0/ai-agent-skills-by-luo-kai",
      "path": "ai-agent-skills/16-engineering (by Luo Kai)/biomedical-imaging-expert/SKILL.md",
      "ref": "4d2fb89cdfb75f3f6fd3c6c5ec9ad90ea129eb3c",
      "url": "https://github.com/luokai0/ai-agent-skills-by-luo-kai/blob/4d2fb89cdfb75f3f6fd3c6c5ec9ad90ea129eb3c/ai-agent-skills/16-engineering (by Luo Kai)/biomedical-imaging-expert/SKILL.md",
      "key": "luokai0/ai-agent-skills-by-luo-kai/ai-agent-skills/16-engineering (by Luo Kai)/biomedical-imaging-expert/SKILL.md"
    }
  },
  "instructions": "# Biomedical Imaging Expert\n\n## Before Starting\n1. Which imaging modality?\n2. Clinical diagnosis or research application?\n3. Image acquisition or image processing focus?\n\n## Core Expertise Areas\n\n### X-ray and CT\nX-ray production: electrons accelerated to anode, bremsstrahlung and characteristic radiation.\nAttenuation: Beer-Lambert law, tissue contrast from differential attenuation.\nCT reconstruction: filtered backprojection or iterative algorithms from projections.\nHounsfield units: CT number scale, air -1000, water 0, bone +1000.\nDose: effective dose in mSv, CT higher than plain X-ray, ALARA",
  "cost": {
    "context_tokens": 620
  }
}

Fetch it by URL: GET /api/v1/registry/luokai0-ai-agent-skills-by-luo-kai-biomedical-imaging-expert/manifest?version=1.0.0

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