Nv Generate Ct Rflow

NV-Generate-CT (rflow-ct)

Purpose

• Used for generating synthetic CT volumes and masks with NV-Generate-CTMR rflow-ct. Not for production training data without review.
• 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, label mapping evidence, and validation.
• Manifest I/O: inputs are config_infer_override; outputs are synthetic_ct_volumes and result_json.

Instructions

• Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
• Run scripts/run_rflow_ct.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_rflow_ct.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/_anatomy.py | Internal helper used by the primary entrypoint. | Imported only; do not call directly. |

| scripts/_summary_card.py | Internal helper used by the primary entrypoint. | Imported only; do not call directly. |

| scripts/list_anatomies.py | Helper command for catalog or anatomy lookup. | [--region REGION] [--filter TEXT] [--controllable] |

| scripts/run_rflow_ct.py | Primary entrypoint declared by skill_manifest.yaml. | CONFIG_INFER.json --output-dir OUT_DIR [--random-seed N] [--version rflow-ct] [--yes] |

| scripts/run_ct_mask.py | Advanced diagnostic helper for standalone raw MAISI mask generation. | REQUEST.json --output-dir OUT_DIR [--random-seed N] [--preflight-only] [--yes] |

| scripts/run_ct_from_mask.py | Advanced helper for CT image generation from a MAISI label mask. | REQUEST.json --output-dir OUT_DIR [--random-seed N] [--yes] |

| scripts/run_ct_image.py | Advanced helper for CT image-only generation without paired labels. | MODEL_CONFIG.json --output-dir OUT_DIR [--version rflow-ct] [--random-seed N] [--yes] |

Prerequisites

• Required environment variables: NV_GENERATE_ROOT.
• 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.inference. Do not modify code under $NV_GENERATE_ROOT.
• rflow-ct requires CUDA and ≈ 16 GB VRAM minimum for the default 256³ output_size. Larger output_size (e.g. 512×512×768) needs an A100/H100.
• Output volumes are synthetic. They are not safe to use as training data for production medtech models without an independent 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

rectified-flow synthesis pipeline. The wrapper does not reimplement diffusion,

sampling, or autoencoder decoding — it shells out to the upstream

scripts.inference entry point exactly as the project's README documents and

inspects the produced image/mask pairs.

Preconditions

• If NV_GENERATE_ROOT already names a local checkout, the wrapper uses it

and records its current commit in the result. Otherwise, create the

recommended pinned default checkout (one-time):

   if [ -z "${NV_GENERATE_ROOT:-}" ]; then
     export NV_GENERATE_COMMIT=61c4ec709b84cad468852243c48e250bec732074
     export NV_GENERATE_ROOT="$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7"
     if [ ! -d "$NV_GENERATE_ROOT/.git" ]; then
       git clone https://github.com/NVIDIA-Medtech/NV-Generate-CTMR.git "$NV_GENERATE_ROOT"
       git -C "$NV_GENERATE_ROOT" checkout --detach "$NV_GENERATE_COMMIT"
     fi
   fi
   python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt"

• Download the rflow-ct weights and the mask-candidate datasets

into the clone (one-time, ≈ 5.5 GB):

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

The mask candidates (datasets/all_masks_flexible_size_and_spacing_4000)

condition the diffusion sampler; omitting them via --model_only will

make the inference script fail with a missing-file error at startup.

The anatomy-size condition file is also part of the full CT download and is

needed for controllable mask generation.

• NVIDIA GPU with ≥ 16 GB VRAM and CUDA. There is no CPU fallback.

For agent-generated user run commands, prefer the short wrapper command in

Usage. Do not prepend clone or model-download setup steps when NV_GENERATE_ROOT

or the repo-local upstream cache is already present. In a fresh Python

environment, still include python -m 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. Run

the wrapper from the medical-AI-skills repo root. If setup requires cd "$NV_GENERATE_ROOT", return to the Medical AI Skills repo before invoking

skills/nv-generate-ct-rflow/scripts/run_rflow_ct.py.

Usage

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-ct-rflow/scripts/run_rflow_ct.py \
  PATH_TO_CONFIG_INFER.json \
  --output-dir runs/nv_generate_ct_rflow_demo \
  --random-seed 0 \
  --version rflow-ct

Replace PATH_TO_CONFIG_INFER.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 case request is at

runs/.../chest_lung_tumor_controllable.json", that exact path is the first

positional argument to scripts/run_rflow_ct.py.

The fixture argument is a config_infer.json override file: it can replace

num_output_samples, body_region, anatomy_list, controllable_anatomy_size,

output_size, and spacing. Pass default to use the upstream config

verbatim. The wrapper stages the override into the upstream tree before

running.

Fixture catalog

fixtures/ ships curated configs for common paired synthesis use cases: chest

lung lobes, chest with controllable lung tumor, abdomen solid organs,

abdomen with controllable hepatic tumor, head + cervical spine, pelvis.

See fixtures/README.md for the full table.

Helper commands

# Browse the 132-class label_dict grouped by body region.
python skills/nv-generate-ct-rflow/scripts/list_anatomies.py --region chest
python skills/nv-generate-ct-rflow/scripts/list_anatomies.py --controllable
python skills/nv-generate-ct-rflow/scripts/list_anatomies.py --filter tumor

# Validate a fixture and preview cost without launching inference.
NV_GENERATE_ROOT=$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7 \
  python skills/nv-generate-ct-rflow/scripts/run_rflow_ct.py \
    skills/nv-generate-ct-rflow/fixtures/abdomen_liver_spleen.json \
    --output-dir runs/preview --preflight-only

Advanced helpers stay inside this skill for debugging and less-common CT

generation modes. Use them only when the user explicitly asks for that mode:

# Raw MAISI mask diagnostic, useful for checking lung tumor -> label 23.
python skills/nv-generate-ct-rflow/scripts/run_ct_mask.py \
  skills/nv-generate-ct-rflow/fixtures/ct_mask_lung_tumor.json \
  --output-dir runs/ct_mask_debug --preflight-only

# CT image from an existing MAISI label mask with body label 200.
python skills/nv-generate-ct-rflow/scripts/run_ct_from_mask.py \
  skills/nv-generate-ct-rflow/fixtures/ct_from_mask_request_example.json \
  --output-dir runs/ct_from_mask_demo

# CT image-only generation without paired labels.
python skills/nv-generate-ct-rflow/scripts/run_ct_image.py \
  skills/nv-generate-ct-rflow/fixtures/ct_image_only_default.json \
  --output-dir runs/ct_image_only_demo --version rflow-ct

The wrapper runs preflight on every invocation (regardless of

--preflight-only): config-schema bounds, anatomy names matched

against the upstream label_dict, body_region in the supported set,

controllable_anatomy_size constraints, upstream CT output-size/spacing

contracts, body-region-aware x/y FOV minimums, dataset presence under

$NV_GENERATE_ROOT/datasets/, CUDA available, and an estimated peak VRAM /

wall-time. Runs estimated to exceed 5 min wall-time or 30 GB VRAM peak require

--yes to proceed.

Each invocation runs `python -m scripts.inference -t configs/config_network_rflow.json

-i configs/config_infer.json -e configs/environment_rflow-ct.json --random-seed <s>

--version rflow-ct`. Output evidence records the upstream git commit, model

checkpoint hashes, the rendered config, per-sample image/mask geometry, mask

label set, image HU range summary, and per-class voxel volumes.

When controllable_anatomy_size is non-empty, upstream ignores the broader

anatomy_list for the saved paired label map and filters labels to the

controllable anatomy names. The saved paired label values are local 1..N

ordinals, not raw MAISI label IDs. Read output.output_label_mapping in

result_json to map saved output labels back to source labels; for example,

output label 1 can represent MAISI label 23 (lung tumor).

For curated lung-tumor examples, prefer a controllable size around 0.5 or

larger; smaller requests such as 0.2 can produce absent or extremely small

label-23 components for some seeds.

For FOV and setup details, see references/fov-and-downloads.md. For

advanced helper label-space details, see

references/ct-mask-label-space.md and references/ct-from-mask-format.md.

Visual sample card

Alongside the NIfTI pairs, the wrapper writes summary.html to the

output directory: a per-sample mid-slice triptych (axial / coronal /

sagittal) with label overlay, plus a table of the rendered config and

verifier-facing aggregates. Lets you eyeball the result without firing

up 3D Slicer. Pass --no-summary-card to skip.

Anatomy plausibility (label-set sanity, voxel HU range as CT, image/mask

geometry match, declared output labels present, lung-lobe HU floor) is checked by

verifiers/ct_synthesis_quality_v1.

Not for clinical interpretation, training data for production deployment, or

any non-synthetic-research use.