osipy 0.1.2

OSIPI-compliant MRI perfusion analysis library

osipy

OSIPI-compliant MRI perfusion analysis library.

[!CAUTION]

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[!WARNING] osipy is under active development and has not been validated for clinical, research, or any other use. Do not use in professional imaging pipelines. APIs may change between releases.

A Python library implementing OSIPI standards for MRI perfusion data analysis across four modalities: DCE-MRI, DSC-MRI, ASL, and IVIM. Supports multi-vendor DICOM, NIfTI, and BIDS data formats with optional GPU acceleration.

Features

• DCE-MRI — T1 mapping (VFA, Look-Locker), signal-to-concentration conversion, pharmacokinetic modeling (Tofts, Extended Tofts, Patlak, 2CXM, 2CUM), arterial delay fitting
• DSC-MRI — Delta-R2* conversion, SVD deconvolution (sSVD/cSVD/oSVD/Tikhonov), leakage correction, CBV/CBF/MTT/Tmax maps
• ASL — PCASL/PASL/CASL labeling schemes, M0 calibration, single and multi-PLD CBF quantification, ATT estimation
• IVIM — Bi-exponential diffusion-perfusion fitting with segmented, simultaneous, and Bayesian approaches
• Standards — Parameter naming, units, and symbols follow CAPLEX (DCE/DSC) and the ASL Lexicon
• Extensible — Registry-driven architecture: add models, fitters, or methods with a single decorator
• GPU-ready — Transparent CPU/GPU portability via array module abstraction (NumPy/CuPy)
• Multi-vendor — DICOM metadata extraction for GE, Siemens, and Philips scanners

Installation

pip install osipy

For GPU acceleration (CUDA 12.x):

pip install osipy[gpu]

Requires Python 3.12+.

Quick Start

Programmatic API

from osipy.pipeline import run_analysis

# Unified entry point for any modality
result = run_analysis(data, modality="dce", model="extended_tofts", aif=aif, time=time)

# Access parameter maps
ktrans = result.parameter_maps["Ktrans"]
print(ktrans.statistics())

Modality-Specific Usage

DCE-MRI:

from osipy.dce import fit_model, compute_t1_map, list_models

# Available models
print(list_models())  # ['tofts', 'extended_tofts', 'patlak', '2cxm', '2cum']

# T1 mapping
t1_map = compute_t1_map(signal, flip_angles, tr, method="vfa")

# Pharmacokinetic fitting
result = fit_model("extended_tofts", concentration=conc, aif=aif, time=time, mask=mask)

DSC-MRI:

from osipy.dsc import compute_perfusion_maps, get_deconvolver

maps = compute_perfusion_maps(delta_r2, aif, time, mask=mask, deconvolution_method="oSVD")
# maps.cbv, maps.cbf, maps.mtt, maps.ttp, maps.tmax

ASL:

from osipy.asl import quantify_cbf

cbf = quantify_cbf(
    difference_image, labeling_scheme="pcasl",
    pld=1800, label_duration=1800, m0=m0_data,
)

IVIM:

from osipy.ivim import fit_ivim

result = fit_ivim(dwi_data, b_values, fitting_method="segmented", b_threshold=200)
# result.d_map, result.d_star_map, result.f_map

CLI

# Run a pipeline from a YAML config
osipy config.yaml /path/to/data --output results/

# Print a default config template
osipy --dump-defaults dce

# Validate a config file
osipy --validate config.yaml

# Interactive setup wizard
osipy --help-me-pls

Data Loading

from osipy.common.io import load_perfusion

# Auto-detects format (NIfTI, DICOM, BIDS)
dataset = load_perfusion("/path/to/data", modality="dce")

Extending osipy

All components use a registry pattern. Adding a custom model is one file, one decorator:

from osipy.dce.models.registry import register_model
from osipy.dce.models.base import BasePerfusionModel

@register_model("my_model")
class MyModel(BasePerfusionModel):
    ...

# Now usable everywhere:
result = fit_model("my_model", concentration=conc, aif=aif, time=time)

Registries exist for models, fitters, deconvolvers, AIF detectors, leakage correctors, T1 methods, convolution methods, and more. See the extension points documentation for the full list.

Population AIFs

from osipy.common.aif import get_population_aif, list_aifs

print(list_aifs())  # ['parker', 'fritz_hansen', 'georgiou', 'mcgrath']
aif = get_population_aif("parker")

GPU Acceleration

from osipy.common.backend import is_gpu_available, set_backend, GPUConfig

if is_gpu_available():
    set_backend(GPUConfig(force_cpu=False))
# All numerical code automatically uses GPU when available

Development

pytest                    # Run all tests
pytest tests/unit/        # Unit tests only
pytest tests/integration/ # Integration tests
ruff check .              # Lint
mypy osipy                # Type check
mkdocs serve              # Local docs at http://127.0.0.1:8000

License

MIT License

References

• OSIPI (Open Science Initiative for Perfusion Imaging)
• Dickie BR et al. CAPLEX: a standardized reporting framework for perfusion MRI. MRM 2024;91(5):1761-1773. doi:10.1002/mrm.29840
• Suzuki Y et al. OSIPI ASL Lexicon. MRM 2024;91(5):1743-1760. doi:10.1002/mrm.29815