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nv-generate-mr-brain

Generate synthetic brain MRI volumes with configurable modality and dimensions.

Updated Apr 16, 2026
Search Tags:#wrapper#medical-imaging#gpu-accelerated#synthetic-data#mr-brain#engineering-verification
One-click install
npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nv-generate-mr-brain
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nv-generate-mr-brain
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/nv-generate-mr-brain
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nv-generate-mr-brain

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nibabel, numpy, typer, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Generates synthetic brain MRI volumes by wrapping the NV-Generate-CTMR MR brain workflow, enabling engineering teams to test pipelines without real patient data.

Core Features & Use Cases

  • Stages upstream configuration to produce MR brain volumes with configurable modality, dimensions, and randomness for reproducible runs.
  • Validates inputs, performs preflight checks (CUDA availability, VRAM estimates) and reports an execution plan.
  • Summarizes outputs with inventories, per-sample metadata, and a structured payload suitable for benchmarking and verification.

Quick Start

Run the MR brain wrapper with a sample fixture to generate synthetic brain MRI volumes.

Frequently Asked Questions about nv-generate-mr-brain

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate synthetic MR brain volumes for testing medical imaging pipelines?▼

You can generate synthetic MR brain volumes by configuring a deterministic generator that validates inputs and outputs structured metadata. This allows engineering teams to test pipelines without real patient data.

Can I use GPU acceleration for reproducible synthetic brain MRI generation?▼

Yes, GPU acceleration is supported for synthetic brain MRI generation. The workflow performs preflight checks for CUDA availability and VRAM estimates to ensure reproducible runs across experiments.

Do I need PyTorch and CUDA available to generate synthetic medical imaging data?▼

PyTorch and CUDA are required to generate synthetic medical imaging data efficiently. The workflow performs preflight checks to verify CUDA availability and estimate VRAM before executing the generation plan.

What is the best way to benchmark medical imaging pipelines without real patient data?▼

The best way to benchmark without real patient data is using a deterministic synthetic brain MRI generator. It provides configurable modalities and dimensions with structured run metadata payloads suitable for validation tasks.

Does this synthetic MR brain generator output structured metadata for benchmarking?▼

Yes, the synthetic MR brain generator outputs structured metadata payloads. It summarizes outputs with inventories and per-sample metadata, making it suitable for engineering verification and benchmarking tasks.

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