nv-generate-mr

Generates synthetic MR images via NV-Generate-CTMR workflow with JSON provenance reporting.

Updated Apr 16, 2026

Search Tags:#benchmark #preflight #wrapper #nifti #mri #nv-generate-mr #rflow-mr

Ready to run

npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nv-generate-mr

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nv-generate-mr
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/nv-generate-mr
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nv-generate-mr

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Provides an engineering-focused wrapper around the NV-Generate-CTMR MR workflow, enabling staging configs, validating prerequisites, and summarizing outputs without reimplementing the core model logic.

Core Features & Use Cases

Stages upstream MR configs, environment, and model weights, shells out to the upstream CLI, and collects a structured report.
Performs preflight checks (CUDA availability, model inventory, and cost estimates) with safeguards for expensive runs.
Outputs a detailed, auditable JSON payload including upstream commit, invocation, and results for benchmarking and governance.

Quick Start

Run the wrapper with a fixture or default to generate MR volumes: python skills/nv-generate-mr/scripts/run_mr.py PATH_TO_MR_CONFIG.json --output-dir runs/nv_generate_mr_demo --modality mri_t1 .

Frequently Asked Questions about nv-generate-mr

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

FAQPage Schema

How do I generate synthetic MRI volumes for benchmarking?▼

To generate synthetic MRI volumes for benchmarking, run the wrapper script with a JSON config file and output directory. It validates prerequisites, shells out to the MR workflow, and produces an auditable JSON payload summarizing results.

What is synthetic MR image synthesis used for in research pipelines?▼

Synthetic MR image synthesis is used for research, benchmarking, and governance of MR pipelines across configurations, modalities, and seeds. This wrapper provides engineering validation and structured provenance for safe, repeatable generation.

Do I need CUDA and PyTorch to run a synthetic MRI generation workflow?▼

Yes, you need CUDA and PyTorch to run synthetic MRI generation. The wrapper performs preflight checks for CUDA availability and model inventory, and it requires dependencies like torch and numpy to execute safely.

Does this MRI synthesis wrapper support NIfTI output for medical imaging?▼

Yes, the MRI synthesis wrapper supports NIfTI output for medical imaging. It depends on nibabel for handling NIfTI files and generates synthetic MR volumes validated through the configured pipeline.

How can I prevent expensive synthetic MRI generation runs from executing accidentally?▼

To prevent expensive synthetic MRI generation runs, the wrapper includes preflight checks with safeguards and cost estimates. It evaluates the configuration and environment before shelling out to the upstream CLI.

How do I track provenance and configuration for synthetic MRI generation?▼

To track provenance and configuration for synthetic MRI generation, the wrapper outputs a detailed JSON payload. This includes the upstream commit, invocation details, staged configs, and results for governance.