Source code for src.original.DK_OGC_AmsterdamUMC.utils.data_processing.processors.SignalCuration

import numpy as np
import torch
import torchio
from torchio.transforms import Transform




class SignalCuration(Transform):

    def __init__(self, qmri_application, **kwargs):
        self.qmri_application = qmri_application
        super().__init__(**kwargs)



    def apply_transform(self, subject):
        """
        curates signals Image of subject
        Args:
            subject: Subject

        Returns:
            subject: Subject
        """
        images_dict = subject.get_images_dict()
        if self.qmri_application == 'IVIM' or 'ivim':
            signals = images_dict['signals'].numpy()
            xvals = np.squeeze(images_dict['xvals'].numpy())
            valid_mask = self.ivim_selection(signals, xvals)
            subject.add_image(torchio.Image(tensor=torch.Tensor(np.expand_dims(valid_mask, 0))), 'valid_mask')
            return subject
        else:
            raise NotImplementedError




    @staticmethod
    def ivim_selection(signals, xvals):
        """
        returns only those signals exhibiting ivim decay
        Args:
            signals: signals for corresponding xvals
            xvals: xvals

        Returns:
            normalized_valid_signals: normalized_valid_signals that exhibit ivim-like decay
            masked_signals: normalized_signals where signals not exhibiting ivim-like decay are set to 0
        """

        # get average b0 signals and set signals with S0 of 0 to nan
        mean_S0 = np.nanmean(signals[xvals <= 0.0001, ...], axis=0)

        # select only those voxels with average S0 larger than half of median S0 of voxels with S0 larger than 0
        valid_idcs_median_value = mean_S0 > (0.5 * np.nanmedian(mean_S0[mean_S0 > 0]))

        # check if signal is ivim like
        signals[signals > 1.5] = 1.5
        valid_idcs_ivim_curve1 = np.percentile(signals[xvals * 100 < 50, ...], 95,
                                               axis=0) < 1.3
        valid_idcs_ivim_curve2 = np.percentile(signals[xvals * 100 > 50, ...], 95,
                                               axis=0) < 1.2
        valid_idcs_ivim_curve3 = np.percentile(signals[xvals * 100 > 150, ...], 95,
                                               axis=0) < 1.0
        mask_signals = valid_idcs_median_value & valid_idcs_ivim_curve1 & valid_idcs_ivim_curve2 & valid_idcs_ivim_curve3

        return mask_signals