asl_pipeline

asl_pipeline ¶

ASL analysis pipeline.

This module provides an end-to-end ASL analysis pipeline, integrating M0 calibration and CBF quantification following the OSIPI ASL Lexicon conventions.

References

.. [1] OSIPI ASL Lexicon, https://osipi.github.io/ASL-Lexicon/ .. [2] Suzuki Y et al. MRM 2024;91(4):1411-1421.

ASLPipelineConfig `dataclass` ¶

ASLPipelineConfig(labeling_scheme=PCASL, pld=1800.0, label_duration=1800.0, t1_blood=1650.0, labeling_efficiency=0.85, m0_method='single', output_dir=None)

Configuration for ASL pipeline.

Defaults match the ISMRM ASL consensus paper (Alsop et al., MRM 2015;73:102, PMC4190138) for healthy adults under 70 years. For older adults or clinical-population studies, Alsop Table 1 recommends PLD=2000 ms; override pld accordingly.

ATTRIBUTE	DESCRIPTION
`labeling_scheme`	ASL labeling scheme. TYPE: `LabelingScheme`
`pld`	Post-labeling delay in milliseconds. Default 1800 ms (Alsop 2015 healthy-adult value; use 2000 for clinical adults). TYPE: `float`
`label_duration`	Labeling duration in milliseconds (for pCASL/CASL). Default 1800 ms (Alsop 2015 recommendation). TYPE: `float`
`t1_blood`	Blood T1 in milliseconds. Default 1650 ms (Alsop 2015 at 3T). TYPE: `float`
`labeling_efficiency`	Labeling efficiency. Default 0.85 for PCASL (Alsop 2015). TYPE: `float`
`m0_method`	M0 calibration method: 'single', 'voxelwise', or 'reference_region'. TYPE: `str`
`output_dir`	Output directory for results. TYPE: `Path \| None`

ASLPipelineResult `dataclass` ¶

ASLPipelineResult(cbf_result, m0_map, config)

Result of ASL pipeline.

ATTRIBUTE	DESCRIPTION
`cbf_result`	CBF quantification results. TYPE: `ASLQuantificationResult`
`m0_map`	M0 values used. TYPE: `NDArray \| None`
`config`	Pipeline configuration used. TYPE: `ASLPipelineConfig`

ASLPipeline ¶

ASLPipeline(config=None)

End-to-end ASL analysis pipeline.

This pipeline performs: 1. ASL difference computation (label - control) 2. M0 calibration 3. CBF quantification

Examples:

>>> from osipy.pipeline import ASLPipeline, ASLPipelineConfig
>>> from osipy.asl import LabelingScheme
>>> config = ASLPipelineConfig(
...     labeling_scheme=LabelingScheme.PCASL,
...     pld=1800.0,
... )
>>> pipeline = ASLPipeline(config)
>>> result = pipeline.run(label_images, control_images, m0_image)

Initialize ASL pipeline.

PARAMETER	DESCRIPTION
`config`	Pipeline configuration. TYPE: `ASLPipelineConfig \| None` DEFAULT: `None`

run ¶

run(label_data, control_data, m0_data, mask=None, progress_callback=None)

Run ASL analysis pipeline.

PARAMETER	DESCRIPTION
`label_data`	Label images, shape (...) or (..., n_averages). TYPE: `NDArray`
`control_data`	Control images, shape (...) or (..., n_averages). TYPE: `NDArray`
`m0_data`	M0 calibration image or single value. TYPE: `NDArray or float`
`mask`	Brain mask. TYPE: `NDArray` DEFAULT: `None`
`progress_callback`	Callback for progress updates. TYPE: `Callable` DEFAULT: `None`

RETURNS	DESCRIPTION
`ASLPipelineResult`	Pipeline results.

run_from_alternating ¶

run_from_alternating(asl_data, m0_data, label_control_order='label_first', mask=None, progress_callback=None)

Run ASL pipeline from interleaved label/control data.

PARAMETER	DESCRIPTION
`asl_data`	Interleaved ASL data, shape (..., n_pairs2). TYPE:* `NDArray`
`m0_data`	M0 calibration data. TYPE: `NDArray or float`
`label_control_order`	Order: 'label_first' or 'control_first'. TYPE: `str` DEFAULT: `'label_first'`
`mask`	Brain mask. TYPE: `NDArray` DEFAULT: `None`
`progress_callback`	Progress callback. TYPE: `Callable` DEFAULT: `None`

RETURNS	DESCRIPTION
`ASLPipelineResult`	Pipeline results.