src.original.preprocessing.EP_GU package

Submodules

src.original.preprocessing.EP_GU.brain_pipeline module

src.original.preprocessing.EP_GU.outlier_removal module

Methods for outlier removal. Reproduced with permission from Oscar Jalngefjord: https://github.com/oscarjalnefjord/ivim/tree/outlier by Elina Petersson

src.original.preprocessing.EP_GU.outlier_removal.at_least_1d(pars: list) → list

Check that each parameter is atleast one dimension in shape.

src.original.preprocessing.EP_GU.outlier_removal.ballistic(b: ndarray[tuple[Any, ...], dtype[float64]], c: ndarray[tuple[Any, ...], dtype[float64]], D: ndarray[tuple[Any, ...], dtype[float64]], f: ndarray[tuple[Any, ...], dtype[float64]], vd: ndarray[tuple[Any, ...], dtype[float64]], S0: ndarray[tuple[Any, ...], dtype[float64]] = 1, K: ndarray[tuple[Any, ...], dtype[float64]] = 0) → ndarray[tuple[Any, ...], dtype[float64]]

Return MR signal based on the ballistic IVIM model.

src.original.preprocessing.EP_GU.outlier_removal.check_regime(regime: str) → None

Check that the regime is valid.

src.original.preprocessing.EP_GU.outlier_removal.diffusive(b: ndarray[tuple[Any, ...], dtype[float64]], D: ndarray[tuple[Any, ...], dtype[float64]], f: ndarray[tuple[Any, ...], dtype[float64]], Dstar: ndarray[tuple[Any, ...], dtype[float64]], S0: ndarray[tuple[Any, ...], dtype[float64]] = 1, K: ndarray[tuple[Any, ...], dtype[float64]] = 0) → ndarray[tuple[Any, ...], dtype[float64]]

Return MR signal based on the diffusive IVIM model.

src.original.preprocessing.EP_GU.outlier_removal.kurtosis(b: ndarray[tuple[Any, ...], dtype[float64]], D: ndarray[tuple[Any, ...], dtype[float64]], K: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

Return the kurtosis signal representation.

src.original.preprocessing.EP_GU.outlier_removal.monoexp(b: ndarray[tuple[Any, ...], dtype[float64]], D: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

Return the monoexponential e^(-b*D).

src.original.preprocessing.EP_GU.outlier_removal.roi_based(im_file: str, bval_file: str, roi_file: str, outbase: str, regime: str, fig: bool = False, cval_file: str | None = None)

Identify outliers by fit to ROI average.

Arguments:

im_file: path to nifti image file bval_file: path to .bval file roi_file: path to nifti file defining a region-of-interest (ROI) in which the correction is calculated and applied outbase: basis for output filenames, i.e. filename without file extension to which .nii.gz, .bval, etc. is added regime: IVIM regime to model: no (= sIVIM), diffusive (long encoding time) or ballistic (short encoding time) fig: (optional) if True, a diagnostic figure is output cval_file: (optional) path to .cval file

src.original.preprocessing.EP_GU.outlier_removal.sIVIM(b: ndarray[tuple[Any, ...], dtype[float64]], D: ndarray[tuple[Any, ...], dtype[float64]], f: ndarray[tuple[Any, ...], dtype[float64]], S0: ndarray[tuple[Any, ...], dtype[float64]] = 1, K: ndarray[tuple[Any, ...], dtype[float64]] = 0) → ndarray[tuple[Any, ...], dtype[float64]]

Return MR signal based on the simplified IVIM (sIVIM) model.

Module contents