NV-Generate-MR

Purpose

• Used for generating synthetic body MRI volumes with NV-Generate-CTMR rflow-mr. Not for paired masks or production training data.
• Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
• Do not write custom inference code for normal runs. The wrapper owns config staging, output paths, and validation.
• Manifest I/O: inputs are model_config_override; outputs are synthetic_mr_volumes and result_json.

Instructions

• Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
• Run scripts/run_mr.py through the documented command below; keep outputs under a caller-provided run directory.
• If a host agent exposes run_script, use run_script("scripts/run_mr.py", args=[...]); otherwise run the Bash/Python command shown below.
• Emit a single bash code block, and keep the python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" step in that same command — the runtime may be a fresh environment without nibabel/MONAI, so dropping the install fails with ModuleNotFoundError.
• Do not add rm, mkdir, or any cleanup of --output-dir; the wrapper creates it. Use a fresh --output-dir instead of deleting one.
• Check the emitted JSON and paired verifier guidance before treating the run as evidence.

Available Scripts

| Script | Purpose | Arguments | |—|—|—| | scripts/run_mr.py | Primary entrypoint declared by skill_manifest.yaml. | MODEL_CONFIG.json --output-dir OUT_DIR --modality mri_t1 [--random-seed N] [--yes] |

Prerequisites

• Runtime requirements: GPU/CUDA when declared by the manifest; Python packages listed in runtime.side_effects.pip_packages.
• Side effects: writes generated outputs under the caller’s --output-dir, may cache model assets under ~/.cache/huggingface/, and may contact https://huggingface.co or https://github.com during setup.
• Run commands from the repository root unless an existing section below says otherwise.

Limitations

• This is a thin wrapper. Inference, sampling, and decoding are delegated entirely to NVIDIA-Medtech/NV-Generate-CTMR’s scripts.diff_model_infer. Do not modify code under $NV_GENERATE_ROOT or the repo-local fallback at .workbench_data/upstreams/NV-Generate-CTMR.
• rflow-mr generates image-only synthetic MRI volumes. It does not emit paired segmentation masks.
• The upstream README recommends rflow-mr-brain instead for brain MRI synthesis; use skills/nv-generate-mr-brain for that path.
• NV-Generate-MR weights are listed by upstream as NVIDIA Non-Commercial. Do not use outputs as production training data without legal and quality review.
• Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission.

Troubleshooting

| Error | Cause | Fix | |—|—|—| | Missing dependency or import error | Runtime package drift from skill_manifest.yaml. | Install the packages declared in the manifest or use the documented setup command. | | Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. | | Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |

Wraps the upstream NVIDIA-Medtech/NV-Generate-CTMR MR image-only generation workflow. The wrapper does not reimplement diffusion sampling or autoencoder decoding. It stages config overrides, runs the documented python -m scripts.diff_model_infer command for rflow-mr, then summarizes the generated NIfTI volume.

Exact Runnable Surface

For user run commands in a fresh benchmark environment, use this setup plus repo-root wrapper command exactly:

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-.workbench_data/upstreams/NV-Generate-CTMR}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-mr/scripts/run_mr.py PATH_TO_MR_CONFIG.json --output-dir OUT_DIR --modality mri_t1 --random-seed 0

Do not invent generate.sh, infer.py, Medical AI Skills run, or python -m nv_generate_mr commands. PATH_TO_MR_CONFIG.json must be the user’s supplied request path.

Preconditions

Clone and install the upstream repo once. In this Medical AI Skills checkout, prefer the repo-local cache path when it exists:

mkdir -p .workbench_data/upstreams
test -d .workbench_data/upstreams/NV-Generate-CTMR/.git || \
  git clone https://github.com/NVIDIA-Medtech/NV-Generate-CTMR.git \
    .workbench_data/upstreams/NV-Generate-CTMR
export NV_GENERATE_ROOT=.workbench_data/upstreams/NV-Generate-CTMR
pip install -r "$NV_GENERATE_ROOT/requirements.txt"

Download the MR weights:

cd "$NV_GENERATE_ROOT"
python -m scripts.download_model_data --version rflow-mr --root_dir ./ --model_only

Runtime needs an NVIDIA GPU with at least 16 GB VRAM. There is no CPU fallback in the upstream path.

The wrapper also searches .workbench_data/upstreams/NV-Generate-CTMR if NV_GENERATE_ROOT is unset or points at a stale clone.

For agent-generated user run commands, use the command in Usage. Do not prepend clone or model-download setup steps when the repo-local upstream cache already exists. In a fresh Python environment, still include pip install -r "$NV_GENERATE_ROOT/requirements.txt" before the wrapper unless the active environment has already proven those imports are available; cached weights do not imply cached Python packages. If setup requires cd "$NV_GENERATE_ROOT", return to the Medical AI Skills repo before invoking skills/nv-generate-mr/scripts/run_mr.py.

Usage

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-.workbench_data/upstreams/NV-Generate-CTMR}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-mr/scripts/run_mr.py \
  PATH_TO_MR_CONFIG.json \
  --output-dir runs/nv_generate_mr_demo \
  --modality mri_t1 \
  --random-seed 0

Replace PATH_TO_MR_CONFIG.json with the user’s actual request/config path. Do not copy the fixture path from this document unless the user explicitly asked to run that fixture. If the user says “the request is at runs/.../default_mri_t1.json”, that exact path is the first positional argument to scripts/run_mr.py.

Supported rflow-mr modality names are mri, mri_t1, mri_t2, and mri_flair, matching the upstream MR image-generation guide. The upstream README recommends rflow-mr-brain instead when synthesizing brain images; use skills/nv-generate-mr-brain for that path. For FOV and setup details, see references/fov-and-downloads.md.

The fixture argument is a small JSON override for configs/config_maisi_diff_model_rflow-mr.json. Pass default to use the upstream defaults plus the CLI modality and random seed. Common override keys are dim, spacing, num_inference_steps, cfg_guidance_scale, and modality.

Each run records the staged config, model inventory, upstream command, output geometry, spacing, affine, intensity range, and non-constant / finite-data checks. Output volumes are synthetic and are not safe as production training data without independent review.

Not for clinical interpretation, production deployment, autonomous diagnosis, or regulatory submission.