Module ukat.mapping.mtr
Classes
class MTR (pixel_array, affine, mask=None)-
Generates a Magnetisation Transfer Ratio (MTR) map from a series of volumes collected with 2 different magnetisation transfer values (ON and OFF).
Attributes
mtr_map:np.ndarray- The estimated magnetisation transfer ratio values. Each value should be a float between 0 and 1.
shape:tuple- The shape of the MTR map.
mt_on:np.ndarray- The array corresponding to the magnetisation transfer value ON.
mt_off:np.ndarray- The array corresponding to the magnetisation transfer value OFF.
mask:np.ndarray- A boolean mask of the voxels to fit.
Initialise a MTR class instance.
Parameters
pixel_array:np.ndarray- A 3D/4D array containing the image at each magnetisation transfer
value, i.e. the dimensions of the array are (x, y, 2) or
(x, y, z, 2). The assumption is that
mt_offcomes first inpixel_arrayandmt_oncomes second inpixel_array, i.e.pixel_array[…, 0]corresponds tomt_offandpixel_array[…, 1]corresponds tomt_on. affine:np.ndarray- A matrix giving the relationship between voxel coordinates and world coordinates.
mask:np.ndarray, optional- A boolean mask of the voxels to fit. Should be the shape of the desired MTR map rather than the raw data i.e. omit the on/off dimension.
Expand source code
class MTR: """ Generates a Magnetisation Transfer Ratio (MTR) map from a series of volumes collected with 2 different magnetisation transfer values (ON and OFF). Attributes ---------- mtr_map : np.ndarray The estimated magnetisation transfer ratio values. Each value should be a float between 0 and 1. shape : tuple The shape of the MTR map. mt_on : np.ndarray The array corresponding to the magnetisation transfer value ON. mt_off : np.ndarray The array corresponding to the magnetisation transfer value OFF. mask : np.ndarray A boolean mask of the voxels to fit. """ def __init__(self, pixel_array, affine, mask=None): """Initialise a MTR class instance. Parameters ---------- pixel_array : np.ndarray A 3D/4D array containing the image at each magnetisation transfer value, i.e. the dimensions of the array are (x, y, 2) or (x, y, z, 2). The assumption is that `mt_off` comes first in `pixel_array` and `mt_on` comes second in `pixel_array`, i.e. `pixel_array[..., 0]` corresponds to `mt_off` and `pixel_array[..., 1]` corresponds to `mt_on`. affine : np.ndarray A matrix giving the relationship between voxel coordinates and world coordinates. mask : np.ndarray, optional A boolean mask of the voxels to fit. Should be the shape of the desired MTR map rather than the raw data i.e. omit the on/off dimension. """ assert (pixel_array.shape[-1] == 2), 'The input should contain 2 MT ' \ 'values (ON / OFF). The last ' \ 'dimension of the input ' \ 'pixel_array must be 2.' if np.sum(pixel_array[..., 1]) >= np.sum(pixel_array[..., 0]): warnings.warn('The average intensity of the MT_ON image is more ' 'than the average intensity of the MT_OFF image. ' 'This will lead to negative MTR values which is not ' 'usually desirable. Please check that you\'ve input ' 'MT_ON and MT_OFF in the correct order in ' '`pixel_array`.') self.pixel_array = pixel_array self.shape = pixel_array.shape[:-1] self.affine = affine # Generate a mask if there isn't one specified if mask is None: self.mask = np.ones(self.shape, dtype=bool) else: self.mask = mask # The assumption is that MT_OFF comes first in `pixel_array` self.mt_off = np.squeeze(self.pixel_array[..., 0] * self.mask) # The assumption is that MT_ON comes second in `pixel_array` self.mt_on = np.squeeze(self.pixel_array[..., 1] * self.mask) # Magnetisation Transfer Ratio calculation self.mtr_map = np.squeeze(np.nan_to_num(((self.mt_off - self.mt_on) / self.mt_off), posinf=0, neginf=0)) def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', maps='all'): """Exports some of the MTR class attributes to NIFTI. Parameters ---------- output_directory : string, optional Path to the folder where the NIFTI files will be saved. base_file_name : string, optional Filename of the resulting NIFTI. This code appends the extension. Eg., base_file_name = 'Output' will result in 'Output.nii.gz'. maps : list or 'all', optional List of maps to save to NIFTI. This should either the string "all" or a list of maps from ["mtr_map", "mt_on", "mt_off", "mask"]. """ os.makedirs(output_directory, exist_ok=True) base_path = os.path.join(output_directory, base_file_name) if maps == 'all' or maps == ['all']: maps = ['mtr_map', 'mt_on', 'mt_off', 'mask'] if isinstance(maps, list): for result in maps: if result == 'mtr' or result == 'mtr_map': mtr_nifti = nib.Nifti1Image(self.mtr_map, affine=self.affine, dtype=float) nib.save(mtr_nifti, base_path + '_mtr_map.nii.gz') elif result == 'mt_on': mt_on_nifti = nib.Nifti1Image(self.mt_on, affine=self.affine, dtype=float) nib.save(mt_on_nifti, base_path + '_mt_on.nii.gz') elif result == 'mt_off': mt_off_nifti = nib.Nifti1Image(self.mt_off, affine=self.affine, dtype=float) nib.save(mt_off_nifti, base_path + '_mt_off.nii.gz') elif result == 'mask': mask_nifti = nib.Nifti1Image(self.mask.astype(np.uint16), affine=self.affine) nib.save(mask_nifti, base_path + '_mask.nii.gz') else: raise ValueError('No NIFTI file saved. The variable "maps" ' 'should be "all" or a list of maps from ' '"["mtr_map", "mt_on", "mt_off", "mask"]".') returnMethods
def to_nifti(self, output_directory='/home/runner/work/ukat/ukat', base_file_name='Output', maps='all')-
Exports some of the MTR class attributes to NIFTI.
Parameters
output_directory:string, optional- Path to the folder where the NIFTI files will be saved.
base_file_name:string, optional- Filename of the resulting NIFTI. This code appends the extension. Eg., base_file_name = 'Output' will result in 'Output.nii.gz'.
maps:listor'all', optional- List of maps to save to NIFTI. This should either the string "all" or a list of maps from ["mtr_map", "mt_on", "mt_off", "mask"].