TIV and amplitude lung space

eitprocessing.roi.tiv.TIVLungspace dataclass

TIVLungspace(*, threshold: float = 0.15)

Create a pixel mask by thresholding the mean TIV.

This defines the functional lung space as all pixels with a tidal impedance variation (TIV) of at least the provided fractional threshold of the maximum TIV.

Example usage:

>>> mask = TIVLungspace(threshold=0.15).apply(eit_data)
>>> masked_eit_data = mask.apply(eit_data)

PARAMETER	DESCRIPTION
threshold	The fraction of the maximum TIV that is used as threshold. Defaults to 0.15 (15%). TYPE: float DEFAULT: 0.15

apply

apply(
    eit_data: EITData,
    *,
    timing_data: ContinuousData | None = None,
    captures: dict | None = None
) -> PixelMask

Apply the TIV thresholding to the EIT data.

BreathDetection is used to find breaths in timing data. By default, the timing data is the summed pixel impedance. Alternative timing data, e.g., pressure data, can be provided.

Then, TIV is used to compute the TIV for each breath. The mean TIV over all breaths is computed, and pixels with a mean TIV above the threshold are included in the mask.

PARAMETER	DESCRIPTION
eit_data	The EIT data to process. TYPE: EITData
timing_data	Optionally, alternative continuous data to use for breath detection. If None, the summed pixel impedance is used. Defaults to None. TYPE: ContinuousData | None DEFAULT: None
captures	A dictionary to store intermediate results. If None, no intermediate results are stored. Defaults to None. TYPE: dict | None DEFAULT: None

eitprocessing.roi.amplitude.AmplitudeLungspace dataclass

AmplitudeLungspace(*, threshold: float = 0.15)

Create a pixel mask by thresholding the mean amplitude.

This defines the functional lung space as all pixels with an amplitude of at least the provided fractional threshold of the maximum amplitude.

Warning

A lung space based on amplitude is not recommended, as it potentially includes reconstruction artifacts. The option is provided for completeness and use in other algorithms, namely WatershedLungspace.

Example usage:

>>> mask = AmplitudeLungspace(threshold=0.15).apply(eit_data)
>>> masked_eit_data = mask.apply(eit_data)

PARAMETER	DESCRIPTION
threshold	The fraction of the maximum amplitude that is used as threshold. Defaults to 0.15 (15%). TYPE: float DEFAULT: 0.15

apply

apply(
    eit_data: EITData,
    *,
    timing_data: ContinuousData | None = None,
    captures: dict | None = None
) -> PixelMask

Apply the amplitude thresholding to the EIT data.

BreathDetection is used to find breaths in timing data. By default, the timing data is the summed pixel impedance. Alternative timing data, e.g., pressure data, can be provided.

Then, TIV is used to compute the amplitude for each breath. The mean amplitude over all breaths is computed, and pixels with a mean amplitude above the threshold are included in the mask.

PARAMETER	DESCRIPTION
eit_data	The EIT data to process. TYPE: EITData
timing_data	Optionally, alternative continuous data to use for breath detection. If None, the summed pixel impedance is used. Defaults to None. TYPE: ContinuousData | None DEFAULT: None
captures	A dictionary to store intermediate results. If None, no intermediate results are stored. Defaults to None. TYPE: dict | None DEFAULT: None