dcccpy

dcccpy is a Python wrapper for the DCCCcore command-line tool from DCCCSlicer. It keeps the native C++ core as the execution engine and provides a small Python API for running common PET biomarker workflows.

Install

The default package is slim:

pip install dcccpy

The first call downloads the matching DCCCcore release package into the local user cache if no native runtime is otherwise available. The slim package also installs nibabel for image loading and nibabel-style image inputs.

The downloaded GitHub release is the full DCCCcore runtime. The optional PyPI runtime wheels are smaller and omit the fast_and_acc registration model/config and ADAD decoupler ONNX ensemble.

Users who prefer pip install to include the native runtime and avoid first-run download can use the platform-selecting runtime extra:

pip install "dcccpy[runtime]"

Platform-specific runtime extras are also available:

pip install "dcccpy[linux-runtime]"
pip install "dcccpy[windows-runtime]"
pip install "dcccpy[macos-runtime]"

The platform-specific extras are guarded by environment markers, so they only install a runtime wheel on the matching platform. Linux x86_64 uses dcccpy-linux-runtime; Linux aarch64 (including NVIDIA DGX Spark) uses dcccpy-linux-arm64-runtime.

Python API

import dcccpy

result = dcccpy.centiloid("amyloid_pet.nii", skip_normalization=True)
print(result.metrics)
print(result.output)

The output argument is optional for single-image Python calls. When omitted, dcccpy creates a temporary output NIfTI path and returns it in the result. Relative input and output paths are resolved from the current Python working directory before DCCCcore is invoked.

result = dcccpy.centiloid("amyloid_pet.nii")
print(result.output)           # temporary output path
print(result.metrics.get("fbp"))

The wrapper accepts nibabel-style image objects with to_filename():

import nibabel as nib
import dcccpy

image = nib.load("amyloid_pet.nii.gz")
result = dcccpy.centiloid(image, skip_normalization=True)
output_image = result.load_output()

Common helpers mirror DCCCcore subcommands:

dcccpy.centiloid("amyloid.nii", suvr=True)
dcccpy.centaurz("tau.nii", report_detailed_regions=True)
dcccpy.fillstates("fdg.nii", tracer="fdg")
dcccpy.adni_pet_core("fdg.nii", tracer="fdg")
dcccpy.normalize("pet.nii", iterative=True)
dcccpy.pet_motion_correct(
    "dynamic_pet.nii.gz",
    "averaged_pet.nii.gz",
    save_corrected_dynamic="corrected.nii.gz",
    motion_output="motion.tsv",
)
dcccpy.run(["centiloid", "--input", "a.nii", "--output", "b.nii"])

Each helper returns DCCCResult with:

• returncode
• stdout / stderr
• output
• metrics, parsed from numeric lines in stdout
• load_output(), which loads the output with nibabel

Command Line

dcccpy also forwards raw arguments to DCCCcore:

dcccpy --help
dcccpy centiloid --input amyloid_pet.nii --output result.nii
dcccpy adni-pet-core --input fdg.nii --output fdg_adni.nii --tracer fdg

The Python helper accepts the same staged exports. Pass one integer or a sequence; only the requested stages are saved:

dcccpy.adni_pet_core(
    "dynamic_pet.nii.gz",
    "standardized.nii.gz",
    tracer="fdg",
    level=(1, 3),
)

Level 1 is the motion-corrected dynamic PET, Level 2 is its 3D average, and Level 3 is the standardized ADNI-style image. The output argument names the highest selected level, while selected lower levels receive _Coreg or _Coreg_Avg suffixes.

adni_pet_core deliberately has no default tracer. Use abeta, tau, or fdg; FDG selects ADNI's iterative masked global-mean normalization, while Aβ and tau retain cerebellar gray normalization.

Downloaded ADNI preprocessed FDG DICOMs can differ slightly from the exact published iteration. See the repository's ADNI FDG compatibility note.

Runtime lookup

At runtime, dcccpy looks for DCCCcore in this order:

1. DCCCPY_DCCCCORE environment variable.
2. A vendored binary inside the installed dcccpy wheel.
3. A binary from dcccpy-linux-runtime, dcccpy-linux-arm64-runtime, dcccpy-windows-runtime, or dcccpy-macos-runtime, installed by dcccpy[runtime] or the platform-specific runtime extras.
4. The local dcccpy cache populated by automatic download.
5. DCCCcore on PATH.
6. Automatic download from GitHub releases, unless disabled.

Useful environment variables:

• DCCCPY_DCCCCORE: exact path to a DCCCcore executable.
• DCCCPY_AUTO_DOWNLOAD=0: disable first-run automatic download.
• DCCCPY_CACHE_DIR: override the runtime cache directory.
• DCCCPY_RELEASE_REPO: override the GitHub release repository.
• DCCCPY_DCCCCORE_URL: override the release asset URL.

macOS Gatekeeper

On macOS, downloaded command-line executables may be blocked by Gatekeeper until the user explicitly allows them. If DCCCcore fails to start with an error such as "Operation not permitted" or "developer cannot be verified", dcccpy prints a hint with the exact runtime path.

You can allow the runtime from System Settings > Privacy & Security, or remove the quarantine flag in Terminal:

xattr -dr com.apple.quarantine /path/to/DCCCcore

Run the same command for the path shown in the dcccpy error message, then run the calculation again.

Runtime Packaging

Release wheels should vendor the matching DCCCcore runtime tree before build:

python scripts/vendor_dccccore.py --version 4.5.0-alpha --release-platform ubuntu-latest-x64
python -m build --wheel

The Linux ARM64 runtime package uses the corresponding release asset:

cd python/dcccpy-linux-arm64-runtime
python scripts/vendor_dccccore.py --version 4.5.0-alpha --release-platform ubuntu-latest-arm64
python -m build --wheel

The source tree intentionally does not commit the vendored runtime because it contains large ONNX model and NIfTI runtime assets.

The preferred distribution layout is:

• dcccpy: slim Python package with nibabel; downloads runtime on first use.
• dcccpy-linux-runtime: optional Linux runtime wheel.
• dcccpy-linux-arm64-runtime: optional Linux aarch64 runtime wheel.
• dcccpy-windows-runtime: optional Windows runtime wheel.
• dcccpy-macos-runtime: optional macOS arm64 runtime wheel.
• dcccpy[runtime]: installs the matching runtime package on supported platforms.

Packaging note

The current optional runtime wheels use a PyPI-size profile for DCCCcore version 4.4.0. They omit the fast_and_acc registration model/config and the ADAD decoupler ONNX ensemble, while keeping the default spatial normalization model and assets needed by common Centiloid/CenTauR/CenTauRz workflows.

The dcccpy 0.3 alpha package targets DCCCcore 4.5.0-alpha. Because the optional runtime wheels still contain DCCCcore 4.4.0, this alpha wrapper ignores those older runtimes and downloads the full matching GitHub release on first use.