nv-generate-ct-rflow

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12 scripts

Used for generating synthetic CT volumes and masks with NV-Generate-CTMR rflow-ct. Not for production training data without review.

3installs

Sourcenvidia/skills

Added on2026-06-01

NPX Install

npx skill4agent add nvidia/skills nv-generate-ct-rflow

SKILL.md Content

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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

Clone the upstream repo and point

NV_GENERATE_ROOT

at it (one-time):

bash

test -d "$HOME/nv-generate-ctmr/.git" || \
  git clone https://github.com/NVIDIA-Medtech/NV-Generate-CTMR.git $HOME/nv-generate-ctmr
export NV_GENERATE_ROOT=$HOME/nv-generate-ctmr
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):
bash
```
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

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

bash

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/nv-generate-ctmr}" && \
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

bash

# 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/nv-generate-ctmr \
  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:

bash

# 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

result_json

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

can represent MAISI label

(

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.

nv-generate-ct-rflow

NPX Install

Tags

SKILL.md Content

NV-Generate-CT (rflow-ct)

Purpose

Instructions

Available Scripts

Prerequisites

Limitations

Troubleshooting

Preconditions

Usage

Fixture catalog

Helper commands

Visual sample card