API Reference

The following section outlines the API of shimming-toolbox.

Fieldmapping 

shimmingtoolbox.prepare_fieldmap.complex_difference(phase1, phase2)

Calculates the complex difference between 2 phase arrays (phase2 - phase1)

Parameters:

phase1 (numpy.ndarray) -- Array containing phase data in radians
phase2 (numpy.ndarray) -- Array containing phase data in radians. Must be the same shape as phase1.

Returns:

The difference in phase between each voxels of phase2 and phase1 (phase2 - phase1)

Return type:

numpy.ndarray

shimmingtoolbox.prepare_fieldmap.correct_2pi_offset(unwrapped, mag, mask, validity_threshold)

Removes 2*pi offsets from unwrapped for a time series. If there is no offset, it returns the same array. The: 'correct' offset is assumed to be at time 0.

Parameters:

unwrapped (numpy.ndarray) -- 4d array of the spatially unwrapped phase
mag (numpy.ndarray) -- 4d array containing the magnitude values of the phase
mask (numpy.ndarray) -- 4d mask of the unwrapped phase array
validity_threshold (float) -- Threshold to create a mask on each timepoints and assume as reliable phase data

Returns:

4d array of the unwrapped phase corrected if there were n*2*pi offsets between time points

Return type:

numpy.ndarray

shimmingtoolbox.prepare_fieldmap.prepare_fieldmap(list_nii_phase, echo_times, mag, unwrapper='prelude', mask=None, threshold=0.05, gaussian_filter=False, sigma=1, fname_save_mask=None)

Creates fieldmap (in Hz) from phase images. This function accommodates multiple echoes (2 or more) and phase difference. This function also accommodates 4D phase inputs, where the 4th dimension represents the time, in case multiple field maps are acquired across time for the purpose of real-time shimming experiments.

Parameters:

list_nii_phase (list) -- List of nib.Nifti1Image phase values. The array can be [x, y], [x, y, z] or [x, y, z, t]. The values must range from [-pi to pi].
echo_times (list) -- List of echo times in seconds for each echo. The number of echotimes must match the number of echoes. It input is a phasediff (1 phase), input 2 echotimes.
unwrapper (str) -- Unwrapper to use for phase unwrapping. Supported: prelude.
mag (numpy.ndarray) -- Array containing magnitude data relevant for phase input. Shape must match phase[echo].
mask (numpy.ndarray) -- Mask for masking output fieldmap. Must match shape of phase[echo].
threshold -- Threshold for masking if no mask is provided. Allowed range: [0, 1] where all scaled values lower than the threshold are set to 0.
gaussian_filter (bool) -- Option of using a Gaussian filter to smooth the fieldmaps (boolean)
sigma (float) -- Standard deviation of gaussian filter.
fname_save_mask (str) -- Filename of the mask calculated by the unwrapper

Returns: numpy.ndarray: Unwrapped fieldmap in Hz.

Wrapper to different unwrapping algorithms.

shimmingtoolbox.unwrap.unwrap_phase.unwrap_phase(nii_phase_wrapped, unwrapper='prelude', mag=None, mask=None, threshold=None, fname_save_mask=None)

Calls different unwrapping algorithms according to the specified unwrapper parameter. The function also allows to call the different unwrappers with more flexibility regarding input shape.

Parameters:

nii_phase_wrapped (nib.Nifti1Image) -- 2D, 3D or 4D radian values [-pi to pi] to perform phase unwrapping. Supported shapes: [x, y], [x, y, z] or [x, y, z, t].
unwrapper (str, optional) -- Unwrapper algorithm name. Possible values: prelude.
mag (numpy.ndarray) -- 2D, 3D or 4D magnitude data corresponding to phase data. Shape must be the same as phase.
mask (numpy.ndarray) -- numpy array of booleans with shape of phase to mask during phase unwrapping.
threshold (float) -- Prelude parameter, see prelude for more detail.
fname_save_mask (str) -- Filename of the mask calculated by the unwrapper

Returns:

Unwrapped phase image.

Return type:

numpy.ndarray

Wrapper to FSL Prelude (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FUGUE/Guide#PRELUDE_.28phase_unwrapping.29)

shimmingtoolbox.unwrap.prelude.prelude(nii_wrapped_phase, mag=None, mask=None, threshold=None, is_unwrapping_in_2d=False, fname_save_mask=None)

wrapper to FSL prelude

This function enables phase unwrapping by calling FSL prelude on the command line. A mask can be provided to mask the phase image provided. 2D unwrapping can be turned off. The output path can be specified. The temporary niis can optionally be saved.

Parameters:

nii_wrapped_phase (nib.Nifti1Image) -- 2D or 3D radian numpy array to perform phase unwrapping. (2 pi interval)
mag (numpy.ndarray) -- 2D or 3D magnitude numpy array corresponding to the phase array
mask (numpy.ndarray, optional) -- numpy array of booleans with shape of complex_array to mask during phase unwrapping
threshold -- Threshold value for automatic mask generation (Use either mask or threshold, not both)
is_unwrapping_in_2d (bool, optional) -- prelude parameter to unwrap slice by slice
fname_save_mask (str) -- Filename of the mask calculated by the unwrapper

Returns:

3D array with the shape of complex_array of the unwrapped phase output from prelude

Return type:

numpy.ndarray

Masking 

Image mask with shape API

shimmingtoolbox.masking.shapes.shape_cube(data, len_dim1, len_dim2, len_dim3, center_dim1=None, center_dim2=None, center_dim3=None)

Creates a cube mask. Returns mask with the same shape as data.

Parameters:

data (numpy.ndarray) -- Data to mask, must be 3 dimensional array.
len_dim1 (int) -- Length of the side of the square along first dimension (in pixels).
len_dim2 (int) -- Length of the side of the square along second dimension (in pixels).
len_dim3 (int) -- Length of the side of the square along third dimension (in pixels).
center_dim1 (int) -- Center of the square along first dimension (in pixels). If no center is provided, the middle is used.
center_dim2 (int) -- Center of the square along second dimension (in pixels). If no center is provided, the middle is used.
center_dim3 (int) -- Center of the square along third dimension (in pixels). If no center is provided, the middle is used.

Returns:

Mask with booleans. True where the cube is located and False in the background.

Return type:

numpy.ndarray

shimmingtoolbox.masking.shapes.shape_sphere(data, radius, center_dim1=None, center_dim2=None, center_dim3=None)

Creates a spherical mask. Returns mask with the same shape as data.

Parameters:

data (numpy.ndarray) -- Data to mask, must be 3 dimensional array.
radius (int) -- Radius of the sphere (in pixels).
center_dim1 (int) -- Center of the sphere along the first dimension (in pixels). If no center is provided, the middle is used.
center_dim2 (int) -- Center of the sphere along the second dimension (in pixels). If no center is provided, the middle is used.
center_dim3 (int) -- Center of the sphere along the third dimension (in pixels). If no center is provided, the middle is used.

Returns:

Mask with booleans. True where the sphere is located and False in the background.

Return type:

numpy.ndarray

shimmingtoolbox.masking.shapes.shape_square(data, len_dim1, len_dim2, center_dim1=None, center_dim2=None)

Creates a square mask. Returns mask with the same shape as data.

Parameters:

data (numpy.ndarray) -- Data to mask, must be 2 dimensional array.
len_dim1 (int) -- Length of the side of the square along first dimension (in pixels).
len_dim2 (int) -- Length of the side of the square along second dimension (in pixels).
center_dim1 (int) -- Center of the square along first dimension (in pixels). If no center is provided, the middle is used.
center_dim2 (int) -- Center of the square along second dimension (in pixels). If no center is provided, the middle is used.

Returns:

Mask with booleans. True where the square is located and False in the background.

Return type:

numpy.ndarray

shimmingtoolbox.masking.shapes.shapes(data, shape, **kargs)

Wrapper to different shape masking functions.

Parameters:

data (numpy.ndarray) -- Data to mask.
shape (str) -- Shape to mask, implemented shapes include: {'square', 'cube', 'sphere'}.
**kargs -- Refer to the specific function in this file for the specific arguments for each shape. See example section for more details.

Returns:

Mask with booleans. True where the shape is located and False in the background.

Return type:

numpy.ndarray

Examples

>>> dummy_data = np.ones([4,3])
>>> dummy_mask = shapes(dummy_data, 'square', center_dim1=1, center_dim2=1, len_dim1=1, len_dim2=3)

Image thresholding API

shimmingtoolbox.masking.threshold.threshold(data, thr=30, scaled_thr=False)

Threshold an image

Parameters:

data (threshold. For complex) -- Data to be masked
thr (float) -- Value to threshold the data: voxels will be set to zero if their value is equal or less than this
data --
values. (threshold is applied on the absolute) --
scaled_thr (bool) -- Specifies if the threshold is absolute or scaled [0, 1].

Returns:

Boolean mask with same dimensions as data

Return type:

numpy.ndarray

shimmingtoolbox.masking.mask_utils.dilate_binary_mask(mask, shape='sphere', size=3)

Dilates a binary mask according to different shapes and kernel size

Parameters:

mask (numpy.ndarray) -- 3d array containing the binary mask.
shape (str) -- 3d kernel to perform the dilation. Allowed shapes are: 'sphere', 'cross', 'line', 'cube', 'None'. 'line' uses 3 line kernels to extend in each directions by "(size - 1) / 2" only if that direction is smaller than (size - 1) / 2
size (int) -- Length of a side of the 3d kernel. Must be odd.

Returns:

Dilated mask.

Return type:

numpy.ndarray

Notes

Kernels for

'cross' size 3:

np.array([[[0, 0, 0],
           [0, 1, 0],
           [0, 0, 0]],
          [[0, 1, 0],
           [1, 1, 1],
           [0, 1, 0]],
          [[0, 0, 0],
           [0, 1, 0],
           [0, 0, 0]]])

'sphere' size 5:

np.array([[[0 0 0 0 0],
           [0 0 0 0 0],
           [0 0  1 0 0],
           [0 0 0 0 0],
           [0 0 0 0 0]],
          [[0 0 0 0 0],
           [0 0 1 0 0],
           [0 1 1 1 0],
           [0 0 1 0 0],
           [0 0 0 0 0]],
          [[0 0 1 0 0],
           [0 1 1 1 0],
           [1 1 1 1 1],
           [0 1 1 1 0],
           [0 0 1 0 0]],
          [[0 0 0 0 0],
           [0 0 1 0 0],
           [0 1 1 1 0],
           [0 0 1 0 0],
           [0 0 0 0 0]],
          [[0 0 0 0 0],
           [0 0 0 0 0],
           [0 0 1 0 0],
           [0 0 0 0 0],
           [0 0 0 0 0]]]

'cube' size 3:

np.array([[[1, 1, 1],
           [1, 1, 1],
           [1, 1, 1]],
          [[1, 1, 1],
           [1, 1, 1],
           [1, 1, 1]],
          [[1, 1, 1],
           [1, 1, 1],
           [1, 1, 1]]])

'line' size 3:

np.array([[[0, 0, 0],
           [0, 1, 0],
           [0, 0, 0]],
          [[0, 0, 0],
           [0, 1, 0],
           [0, 0, 0]],
          [[0, 0, 0],
           [0, 1, 0],
           [0, 0, 0]]])

shimmingtoolbox.masking.mask_utils.resample_mask(nii_mask_from, nii_target, from_slices=None, dilation_kernel='None', dilation_size=3, path_output=None)

Select the appropriate slices from nii_mask_from using from_slices and resample onto nii_target

Parameters:

nii_mask_from (nib.Nifti1Image) -- Mask to resample from. False or 0 signifies not included.
nii_target (nib.Nifti1Image) -- Target image to resample onto.
from_slices (tuple) -- Tuple containing the slices to select from nii_mask_from. None selects all the slices.
dilation_kernel (str) -- kernel used to dilate the mask. Allowed shapes are: 'sphere', 'cross', 'line' 'cube'. See dilate_binary_mask() for more details.
dilation_size (int) -- Length of a side of the 3d kernel to dilate the mask. Must be odd. For example, a kernel of size 3 will dilate the mask by 1 pixel.
path_output (str) -- Path to output debug artefacts.

Returns:

Mask resampled with nii_target.shape and nii_target.affine.

Return type:

nib.Nifti1Image

Coils 

class shimmingtoolbox.coils.coil.Coil(profile, affine, constraints)

Coil profile object that stores coil profiles and there constraints

dim

Dimension along specific axis. dim: 0,1,2 are spatial axes, while dim: 3 corresponds to the coil channel.

Type:: Tuple[int]

profile

(dim1, dim2, dim3, channels) 4d array of N 3d coil profiles

Type:: np.ndarray

affine

4x4 array containing the affine transformation associated with the NIfTI file of the coil profile. This transformation relates to the physical coordinates of the scanner (qform).

Type:: np.ndarray

required_constraints

List containing the required keys for constraints

Type:: list

coef_sum_max

Contains the maximum value for the sum of the coefficients

Type:: float

coef_channel_minmax

List of (min, max) pairs for each coil channels. (None, None) is used to specify no bounds.

Type:: list

name

Name of the coil.

Type:: str

__init__(profile, affine, constraints)

Initialize Coil

Parameters:

profile (np.ndarray) -- Coil profile (dim1, dim2, dim3, channels) 4d array of N 3d coil profiles
affine (np.ndarray) -- 4x4 array containing the qform affine transformation for the coil profiles
constraints (dict) --
dict containing the constraints for the coil profiles. Required keys:
- name (str): Name of the coil.
- coef_sum_max (float): Contains the maximum value for the sum of the coefficients. None is used to specify no bounds
- coef_channel_max (list): List of (min, max) pairs for each coil channels. (None, None) is used to specify no bounds.

Examples

# Example of constraints
constraints = {
    'name': "dummy coil",
    'coef_sum_max': 10,
    # 8 channel coil
    'coef_channel_minmax': [(-2, 2), (-2, 2), (-2, 2), (-2, 2), (-3, 3), (-3, 3), (-3, 3), (-3, 3)],
}

load_constraints(constraints): Loads the constraints named in required_constraints as attribute to this class

class shimmingtoolbox.coils.coil.ScannerCoil(coord_system, dim_volume, affine, constraints, order)

Coil class for scanner coils as they require extra arguments

__init__(coord_system, dim_volume, affine, constraints, order)

Initialize Coil

Parameters:

profile (np.ndarray) -- Coil profile (dim1, dim2, dim3, channels) 4d array of N 3d coil profiles
affine (np.ndarray) -- 4x4 array containing the qform affine transformation for the coil profiles
constraints (dict) --
dict containing the constraints for the coil profiles. Required keys:
- name (str): Name of the coil.
- coef_sum_max (float): Contains the maximum value for the sum of the coefficients. None is used to specify no bounds
- coef_channel_max (list): List of (min, max) pairs for each coil channels. (None, None) is used to specify no bounds.

Examples

# Example of constraints
constraints = {
    'name': "dummy coil",
    'coef_sum_max': 10,
    # 8 channel coil
    'coef_channel_minmax': [(-2, 2), (-2, 2), (-2, 2), (-2, 2), (-3, 3), (-3, 3), (-3, 3), (-3, 3)],
}

shimmingtoolbox.coils.coil.convert_to_mp(shim_setting, manufacturers_model_name)

Converts the ShimSettings tag from the json BIDS sidecar to the scanner units.: (i.e. For the Prisma fit DAC --> uT/m, uT/m^2 (1st order, 2nd order))

Parameters:

shim_setting (list) -- List of coefficients. Found in the json BIDS sidecar under 'ShimSetting'.
manufacturers_model_name (str) -- Name of the model of the scanner. Found in the json BIDS sidecar under ManufacturersModelName'. Supported names: 'Prisma_fit'.

Returns:

Coefficients with units converted.

Return type:

list

shimmingtoolbox.coils.spherical_harmonics.spherical_harmonics(orders, x, y, z)

Returns an array of spherical harmonic basis fields with the order/degree index along the 4th dimension.

Parameters:

orders (numpy.ndarray) -- Degrees of the desired terms in the series expansion, specified as a vector of non-negative integers (np.array(range(0, 3)) yields harmonics up to (n-1)-th order). Must be non negative.
x (numpy.ndarray) -- 3-D arrays of grid coordinates
y (numpy.ndarray) -- 3-D arrays of grid coordinates (same shape as x)
z (numpy.ndarray) -- 3-D arrays of grid coordinates (same shape as x)

Returns:

4d basis set of spherical harmonics with order/degree ordered along 4th dimension

Return type:

numpy.ndarray

Examples

Initialize grid positions

>>> [x, y, z] = np.meshgrid(np.array(range(-10, 11)), np.array(range(-10, 11)), np.array(range(-10, 11)), indexing='ij')

0th-to-2nd order terms inclusive

>>> orders = np.array(range(0, 3))
>>> basis = spherical_harmonics(orders, x, y, z)

Notes

basis[:, :, :,0] corresponds to the 0th-order constant term (globally=unity)
- 0: c
basis[:, :, :, 1:3] to 1st-order linear terms
- 1: y
- 2: z
- 3: x
basis[:, :, :, 4:8] to 2nd-order terms
- 4: xy
- 5: zy
- 6: z2
- 7: zx
- 8: x2y2

Based on

spherical_harmonics.m by topfer@ualberta.ca
calc_spherical_harmonics_arb_points_cz.m by jaystock@nmr.mgh.harvard.edu

shimmingtoolbox.coils.siemens_basis.siemens_basis(x, y, z, orders=(1, 2))

The function first wraps shimmingtoolbox.coils.spherical_harmonics to generate 1st and 2nd order spherical harmonic basis fields at the grid positions given by arrays X,Y,Z. Following Siemens convention, basis is then:

Reordered along the 4th dimension as X, Y, Z, Z2, ZX, ZY, X2-Y2, XY

Rescaled to Hz/unit-shim, where "unit-shim" refers to the measure displayed in the Adjustments card of the Syngo console UI, namely:

1 micro-T/m for X,Y,Z gradients (= 0.042576 Hz/mm)

1 micro-T/m^2 for 2nd order terms (= 0.000042576 Hz/mm^2)

The returned basis is thereby in the form of ideal "shim reference maps", ready for optimization.

Parameters:

x (numpy.ndarray) -- 3-D arrays of grid coordinates, "Left->Right" grid coordinates in the patient coordinate system (i.e. NIfTI reference (RAS), units of mm)
y (numpy.ndarray) -- 3-D arrays of grid coordinates (same shape as x). "Posterior->Anterior" grid coordinates in the patient coordinate system (i.e. NIfTI reference (RAS), units of mm)
z (numpy.ndarray) -- 3-D arrays of grid coordinates (same shape as x). "Inferior->Superior" grid coordinates in the patient coordinate system (i.e. NIfTI reference, units of mm)
orders (tuple) -- Degrees of the desired terms in the series expansion, specified as a vector of non-negative integers ((0:1:n) yields harmonics up to n-th order, implemented 1st and 2nd order)

Returns:

4-D array of spherical harmonic basis fields

Return type:

numpy.ndarray

Notes

For now, orders is, in fact, as default [1:2]—which is suitable for the Prisma (presumably other Siemens systems as well) however, the 3rd-order shims of the Terra should ultimately be accommodated too. (Requires checking the Adjustments/Shim card to see what the corresponding terms and values actually are). So, basis will return with 8 terms along the 4th dim if using the 1st and 2nd order.

shimmingtoolbox.coils.coordinates.generate_meshgrid(dim, affine)

Generate meshgrid of size dim, with coordinate system defined by affine. :param dim: x, y and z dimensions. :type dim: tuple :param affine: 4x4 affine matrix :type affine: numpy.ndarray

Returns:: List of numpy.ndarray containing meshgrid of coordinates
Return type:: list

shimmingtoolbox.coils.coordinates.get_main_orientation(cosines: list)

Returns the orientation of the slice axis by looking at the ImageOrientationPatientDICOM JSON tag

Parameters:

cosines (list) -- list of 6 elements. The first 3 represent the x, y, z cosines of the first row. The last 3
x (represent the) --
y --
it (z cosines of the first column. This can be found in ImageOrientationPatientDICOM so) --
coordinates. (should be LPS) --

Returns:

'SAG', 'COR' or 'TRA'

Return type:

str

shimmingtoolbox.coils.coordinates.phys_gradient(data, affine)

Calculate the gradient of data along physical coordinates defined by affine

Parameters:

data (numpy.ndarray) -- 3d array containing data to apply gradient
affine (numpy.ndarray) -- 4x4 array containing affine transformation

Returns: numpy.ndarray: 3D matrix containing the gradient along the x direction in the physical coordinate system numpy.ndarray: 3D matrix containing the gradient along the y direction in the physical coordinate system numpy.ndarray: 3D matrix containing the gradient along the z direction in the physical coordinate system

shimmingtoolbox.coils.coordinates.phys_to_vox_coefs(gx, gy, gz, affine)

Calculate the vector sum along the image coordinates defined by affine with coefficients in the patient coordinate system.

Parameters:

gx (numpy.ndarray) -- 3D matrix containing the coefs along the x direction in the patient coordinate system
gy (numpy.ndarray) -- 3D matrix containing the coefs along the y direction in the patient coordinate system
gz (numpy.ndarray) -- 3D matrix containing the coefs along the z direction in the patient coordinate system
affine (numpy.ndarray) -- 4x4 array containing affine transformation

Returns:

3D matrix containing the coefs along the x direction in the image coordinate system numpy.ndarray: 3D matrix containing the coefs along the y direction in the image coordinate system numpy.ndarray: 3D matrix containing the coefs along the z direction in the image coordinate system

Return type:

numpy.ndarray

shimmingtoolbox.coils.coordinates.resample_from_to(nii_from_img, nii_to_vox_map, order=2, mode='nearest', cval=0.0, out_class=<class 'nibabel.nifti1.Nifti1Image'>)

Wrapper to nibabel's resample_from_to function. Resample image from_img to mapped voxel space to_vox_map. The wrapper adds support for 2D input data (adds a singleton) and for 4D time series. For more info, refer to nibabel.processing.resample_from_to.

Parameters:

nii_from_img (nibabel.Nifti1Image) -- Nibabel object with 2D, 3D or 4D array. The 4d case will be treated as a timeseries.
nii_to_vox_map (nibabel.Nifti1Image) -- Nibabel object with
order (int) -- Refer to nibabel.processing.resample_from_to
mode (str) -- Refer to nibabel.processing.resample_from_to
cval (scalar) -- Refer to nibabel.processing.resample_from_to
out_class -- Refer to nibabel.processing.resample_from_to

Returns:

Return a Nibabel object with the resampled data. The 4d case will have an extra dimension: for the different time points.

Return type:

nibabel.Nifti1Image

shimmingtoolbox.coils.biot_savart.biot_savart(centers, normals, radii, segment_numbers, fov_min, fov_max, fov_n)

Creates coil profiles for arbitrary loops, for use in multichannel shim examples that do not match spherical harmonics :param centers: List of 3D float center points for each loop in mm :type centers: list :param normals: List of 3D float normal vectors for each loop in mm :type normals: list :param radii: List of float radii for each loop in mm :type radii: list :param segment_numbers: List of integer number of segments for each loop approximation :type segment_numbers: list :param fov_min: Low 3D float corner of coil profile field of view (x, y, z) in mm :type fov_min: tuple :param fov_max: Inclusive high 3D float corner of coil profile field of view (x, y, z) in mm :type fov_max: tuple :param fov_n: Integer number of points for each dimension (x, y, z) in mm :type fov_n: tuple

Returns:: (X, Y, Z, centers) coil profiles of magnetic field z-component in Hz/A -- (X, Y, Z, Channel)
Return type:: numpy.ndarray

Shim 

shimmingtoolbox.shim.realtime_shim.realtime_shim(nii_fieldmap, nii_anat, pmu, json_fmap, nii_mask_anat_riro=None, nii_mask_anat_static=None, path_output=None)

This function will generate static and dynamic (due to respiration) Gz components based on a fieldmap time series and respiratory trace information obtained from Siemens bellows (PMUresp_signal.resp). An additional multi-gradient echo (MGRE) magnitude image is used to generate an ROI and resample the static and dynamic Gz component maps to match the MGRE image. Lastly the mean Gz values within the ROI are computed for each slice.

Parameters:

nii_fieldmap (nibabel.Nifti1Image) -- Nibabel object containing fieldmap data in 4d where the 4th dimension is the timeseries. Fieldmap should be in Hz.
nii_anat (nibabel.Nifti1Image) -- Nibabel object containing a 3d image of the target data to shim.
pmu (PmuResp) -- PmuResp object containing the respiratory trace information.
json_fmap (dict) -- dict of the json sidecar corresponding to the fieldmap data (Used to find the acquisition timestamps).
nii_mask_anat_static (nibabel.Nifti1Image) -- Nibabel object containing the mask to specify the shimming region for the static component.
nii_mask_anat_riro (nibabel.Nifti1Image) -- Nibabel object containing the mask to specify the shimming region for the riro component.
path_output (str) -- Path to output figures and temporary variables. If none is provided, no debug output is provided.

Returns:

tuple containing:

numpy.ndarray: 1D array of the x static_correction. The correction is in mT/m for each slice.

numpy.ndarray: 1D array of the y static_correction. The correction is in mT/m for each slice.

numpy.ndarray: 1D array of the z static_correction. The correction is in mT/m for each slice.

numpy.ndarray: 1D array of the x dynamic riro_correction. The correction is in (mT/m)*rms_pressure for each slice.

numpy.ndarray: 1D array of the y dynamic riro_correction. The correction is in (mT/m)*rms_pressure for each slice.

numpy.ndarray: 1D array of the z dynamic riro_correction. The correction is in (mT/m)*rms_pressure for each slice.

float: Average pressure of the pmu

float: RMS of the pmu pressure

Return type:

(tuple)

shimmingtoolbox.shim.sequencer.define_slices(n_slices: int, factor=1, method='sequential')

Define the slices to shim according to the output convention. (list of tuples)

Parameters:

n_slices (int) -- Number of total slices.
factor (int) -- Number of slices per shim.
method (str) -- Defines how the slices should be sorted, supported methods include: 'interleaved', 'sequential', 'volume'. See Examples for more details.

Returns:

1D list containing tuples of dim3 slices to shim. (dim1, dim2, dim3)

Return type:

list

Examples

slices = define_slices(10, 2, 'interleaved')
print(slices)  # [(0, 5), (1, 6), (2, 7), (3, 8), (4, 9)]

slices = define_slices(20, 5, 'sequential')
print(slices)  # [(0, 1, 2, 3, 4), (5, 6, 7, 8, 9), (10, 11, 12, 13, 14), (15, 16, 17, 18, 19)]

slices = define_slices(20, method='volume')
# 'volume' ignores the 'factor' option
print(slices)  # [(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)]

shimmingtoolbox.shim.sequencer.extend_fmap_to_kernel_size(nii_fmap_orig, dilation_kernel_size, path_output=None)

Load the fmap and expand its dimensions to the kernel size

Parameters:

nii_fmap_orig (nib.Nifti1Image) -- 3d (dim1, dim2, dim3) or 4d (dim1, dim2, dim3, t) nii to be extended
dilation_kernel_size -- Size of the kernel
path_output (str) -- Path to save the debug output

Returns:

Nibabel object of the loaded and extended fieldmap

Return type:

nibabel.Nifti1Image

shimmingtoolbox.shim.sequencer.extend_slice(nii_array, n_slices=1, axis=2)

Adds n_slices on each side of the selected axis. It uses the nearest slice and copies it to fill the values. Updates the affine of the matrix to keep the input array in the same location.

Parameters:

nii_array (nib.Nifti1Image) -- 3d or 4d array to extend the dimensions along an axis.
n_slices (int) -- Number of slices to add on each side of the selected axis.
axis (int) -- Axis along which to insert the slice(s), Allowed axis: 0, 1, 2.

Returns:

Array extended with the appropriate affine to conserve where the original pixels were located.

Return type:

nib.Nifti1Image

Examples

print(nii_array.get_fdata().shape)  # (50, 50, 1, 10)
nii_out = extend_slice(nii_array, n_slices=1, axis=2)
print(nii_out.get_fdata().shape)  # (50, 50, 3, 10)

shimmingtoolbox.shim.sequencer.new_bounds_from_currents(currents, old_bounds)

Uses the currents to determine the appropriate bounds for the next optimization. It assumes that "old_coef + next_bound < old_bound".

Parameters:

currents (np.ndarray) -- 2D array (n_shims x n_channels). Direct output from _optimize().
old_bounds (list) -- 1d list (n_channels) of tuples (min, max) containing the merged bounds of the previous optimization.

Returns:

2d list (n_shim_groups x n_channels) of bounds (min, max) corresponding to each shim group and channel.

Return type:

list

shimmingtoolbox.shim.sequencer.parse_slices(fname_nifti)

Parse the BIDS sidecar associated with the input nifti file.

Parameters:: fname_nifti (str) -- Full path to a NIfTI file
Returns:: 1D list containing tuples of dim3 slices to shim. (dim1, dim2, dim3)
Return type:: list

shimmingtoolbox.shim.sequencer.select_optimizer(method, unshimmed, affine, coils: List[Coil], opt_criteria, pmu: PmuResp | None = None, reg_factor=0)

Select and initialize the optimizer

Parameters:

method (str) -- Supported optimizer: 'least_squares', 'pseudo_inverse', 'least_squares_rt'
unshimmed (numpy.ndarray) -- 3D B0 map
affine (np.ndarray) -- 4x4 array containing the affine transformation for the unshimmed array
coils (ListCoil) -- List of Coils containing the coil profiles
opt_criteria (str) -- Criteria for the optimizer 'least_squares'. Supported: 'mse': mean squared error, 'mae': mean absolute error, 'std': standard deviation.
pmu (PmuResp) -- PmuResp object containing the respiratory trace information. Required for method 'least_squares_rt'.
reg_factor (float) -- Regularization factor for the current when optimizing. A higher coefficient will penalize higher current values while a lower factor will lower the effect of the regularization. A negative value will favour high currents (not preferred).

Returns:

Initialized Optimizer object

Return type:

Optimizer

shimmingtoolbox.shim.sequencer.shim_max_intensity(nii_input, nii_mask=None)

Find indexes of the 4th dimension of the input volume that has the highest signal intensity for each slice.: Based on: https://onlinelibrary.wiley.com/doi/10.1002/hbm.26018

Parameters:

nii_input (nib.Nifti1Image) -- 4d volume where 4th dimension was acquired with different shim values
nii_mask (nib.Nifti1Image) -- Mask defining the spatial region to shim. If None: consider all voxels of nii_input.

Returns:

1d array containing the index of the volume that maximizes signal intensity for each slice

Return type:

np.ndarray

shimmingtoolbox.shim.sequencer.shim_realtime_pmu_sequencer(nii_fieldmap, json_fmap, nii_anat, nii_static_mask, nii_riro_mask, slices, pmu: PmuResp, coils: List[Coil], opt_method='least_squares', opt_criteria='mse', reg_factor=0, mask_dilation_kernel='sphere', mask_dilation_kernel_size=3, path_output=None)

Performs realtime shimming using one of the supported optimizers and an external respiratory trace.

Parameters:

nii_fieldmap (nibabel.Nifti1Image) -- Nibabel object containing fieldmap data in 4d where the 4th dimension is the timeseries. Also contains an affine transformation.
json_fmap (dict) -- Dict of the json sidecar corresponding to the fieldmap data (Used to find the acquisition timestamps).
nii_anat (nibabel.Nifti1Image) -- Nibabel object containing anatomical data in 3d.
nii_static_mask (nibabel.Nifti1Image) -- 3D anat mask used for the optimizer to shim the region for the static component.
nii_riro_mask (nibabel.Nifti1Image) -- 3D anat mask used for the optimizer to shim the region for the riro component.
slices (list) -- 1D array containing tuples of dim3 slices to shim according to the anat where the shape of anat: (dim1, dim2, dim3). Refer to shimmingtoolbox.shim.sequencer.define_slices().
pmu (PmuResp) -- PmuResp object containing the respiratory trace information.
coils (ListCoil) -- List of Coils containing the coil profiles. The coil profiles and the fieldmaps must have matching units (if fmap is in Hz, the coil profiles must be in hz/unit_shim). Refer to shimmingtoolbox.coils.coil.Coil. Make sure the extent of the coil profiles are larger than the extent of the fieldmap. This is especially true for dimensions with only 1 voxel(e.g. (50x50x1x10). Refer to shimmingtoolbox.shim.sequencer.extend_slice()/ shimmingtoolbox.shim.sequencer.update_affine_for_ap_slices()
opt_method (str) -- Supported optimizer: 'least_squares', 'pseudo_inverse'. Note: refer to their specific implementation to know limits of the methods in: shimmingtoolbox.optimizer
opt_criteria (str) -- Criteria for the optimizer 'least_squares'. Supported: 'mse': mean squared error, 'mae': mean absolute error, 'std': standard deviation.
reg_factor (float) -- Regularization factor for the current when optimizing. A higher coefficient will penalize higher current values while a lower factor will lower the effect of the regularization. A negative value will favour high currents (not preferred). Only relevant for 'least_squares' opt_method.
mask_dilation_kernel (str) -- kernel used to dilate the mask. Allowed shapes are: 'sphere', 'cross', 'line' 'cube'. See shimmingtoolbox.masking.mask_utils.dilate_binary_mask() for more details.
mask_dilation_kernel_size (int) -- Length of a side of the 3d kernel to dilate the mask. Must be odd. For example, a kernel of size 3 will dilate the mask by 1 pixel.
path_output (str) -- Path to the directory to output figures. Set logging level to debug to output debug artefacts.

Returns:

tuple containing:

numpy.ndarray: Static coefficients of the coil profiles to shim (len(slices) x channels) e.g. [Hz]

numpy.ndarray: Riro coefficients of the coil profiles to shim (len(slices) x channels)
e.g. [Hz/unit_pressure]

float: Mean pressure of the respiratory trace.

float: Root mean squared of the pressure. This is provided to compare results between scans, multiply the
riro coefficients by rms of the pressure to do so.

Return type:

(tuple)

shimmingtoolbox.shim.sequencer.shim_sequencer(nii_fieldmap, nii_anat, nii_mask_anat, slices, coils: List[Coil], method='least_squares', opt_criteria='mse', mask_dilation_kernel='sphere', mask_dilation_kernel_size=3, reg_factor=0, path_output=None)

Performs shimming according to slices using one of the supported optimizers and coil profiles.

Parameters:

nii_fieldmap (nibabel.Nifti1Image) -- Nibabel object containing fieldmap data in 3d and an affine transformation.
nii_anat (nibabel.Nifti1Image) -- Nibabel object containing anatomical data in 3d.
nii_mask_anat (nibabel.Nifti1Image) -- 3D anat mask used for the optimizer to shim in the region of interest. (only consider voxels with non-zero values)
slices (list) -- 1D array containing tuples of dim3 slices to shim according to the anat, where the shape of anat is: (dim1, dim2, dim3). Refer to shimmingtoolbox.shim.sequencer.define_slices().
coils (ListCoil) -- List of Coils containing the coil profiles. The coil profiles and the fieldmaps must have matching units (if fmap is in Hz, the coil profiles must be in hz/unit_shim). Refer to shimmingtoolbox.coils.coil.Coil. Make sure the extent of the coil profiles are larger than the extent of the fieldmap. This is especially true for dimensions with only 1 voxel(e.g. (50x50x1). Refer to shimmingtoolbox.shim.sequencer.extend_slice()/ shimmingtoolbox.shim.sequencer.update_affine_for_ap_slices()
method (str) -- Supported optimizer: 'least_squares', 'pseudo_inverse'. Note: refer to their specific implementation to know limits of the methods in: shimmingtoolbox.optimizer
opt_criteria (str) -- Criteria for the optimizer 'least_squares'. Supported: 'mse': mean squared error, 'mae': mean absolute error, 'std': standard deviation.
mask_dilation_kernel (str) -- kernel used to dilate the mask. Allowed shapes are: 'sphere', 'cross', 'line' 'cube'. See shimmingtoolbox.masking.mask_utils.dilate_binary_mask() for more details.
mask_dilation_kernel_size (int) -- Length of a side of the 3d kernel to dilate the mask. Must be odd. For example, a kernel of size 3 will dilate the mask by 1 pixel.
reg_factor (float) -- Regularization factor for the current when optimizing. A higher coefficient will penalize higher current values while a lower factor will lower the effect of the regularization. A negative value will favour high currents (not preferred). Only relevant for 'least_squares' opt_method.
path_output (str) -- Path to the directory to output figures. Set logging level to debug to output debug artefacts.

Returns:

Coefficients of the coil profiles to shim (len(slices) x n_channels)

Return type:

numpy.ndarray

shimmingtoolbox.shim.sequencer.update_affine_for_ap_slices(affine, n_slices=1, axis=2)

Updates the input affine to reflect an insertion of n_slices on each side of the selected axis

Parameters:

affine (numpy.ndarray) -- 4x4 qform affine matrix representing the coordinates
n_slices (int) -- Number of pixels to add on each side of the selected axis
axis (int) -- Axis along which to insert the slice(s)

Returns:

4x4 updated affine matrix

Return type:

(numpy.ndarray)

shimmingtoolbox.shim.b1shim.b1shim(b1, mask=None, algorithm=1, target=None, q_matrix=None, sar_factor=1.5)

Computes static optimized shim weights that minimize the B1+ field coefficient of variation over the masked region.

Parameters:

b1 (numpy.ndarray) -- 4D array corresponding to the measured B1+ field. (x, y, n_slices, n_channels)
mask (numpy.ndarray) -- 3D array corresponding to the region where shimming will be performed. (x, y, n_slices)
algorithm (int) -- Number from 1 to 4 specifying which algorithm to use for B1+ optimization: 1 - Reduce the coefficient of variation of the B1+ field. Favors high B1+ efficiency. 2 - Magnitude least square (MLS) optimization targeting a specific B1+ value. Target value required. 3 - Maximizes the SAR efficiency (B1+/sqrt(SAR)). Q matrices required. 4 - Phase-only shimming.
target (float) -- Target B1+ value used by algorithm 2 in nT/V.
q_matrix (numpy.ndarray) -- Matrix used to constrain local SAR. If no matrix is provided, unconstrained optimization is performed, which might result in SAR excess at the scanner (n_channels, n_channels, n_vop).
sar_factor (float) -- Factor (=> 1) to which the maximum local SAR after shimming can exceed the phase-only shimming maximum local SAR. Values between 1 and 1.5 should work with Siemens scanners. High factors allow more shimming liberty but are more likely to result in SAR excess at the scanner.

Returns:

Optimized and normalized 1D vector of complex shimming weights of length n_channels.

Return type:

numpy.ndarray

shimmingtoolbox.shim.b1shim.combine_maps(b1_maps, weights)

Combines the B1 field distribution of several channels into one map representing the total B1 field magnitude.

Parameters:

b1_maps (numpy.ndarray) -- Complex B1 field for different channels (x, y, n_slices, n_channels).
weights (numpy.ndarray) -- 1D complex array of length n_channels.

Returns:

B1 field distribution obtained when applying the provided shim weights.

Return type:

numpy.ndarray

shimmingtoolbox.shim.b1shim.complex_to_vector(weights)

Separates the real and imaginary components of a complex vector into a twice as long vector.

Parameters:: weights (numpy.ndarray) -- 1D complex array of length n_channels.
Returns:: 1D array of length 2*n_channels. First/second half: real/imaginary.
Return type:: numpy.ndarray

shimmingtoolbox.shim.b1shim.load_siemens_vop(fname_sar_file)

Reads in a Matlab file in which the VOP matrices are stored and returns them as a numpy array.

Parameters:: fname_sar_file -- Path to the 'SarDataUser.mat' file containing the scanner's VOPs. This file should be available at the scanner in 'C:/Medcom/MriProduct/PhysConfig'.
Returns:: VOP matrices (n_coils, n_coils, n_VOPs)
Return type:: numpy.ndarray

shimmingtoolbox.shim.b1shim.max_sar(weights, q_matrix)

Returns the maximum local SAR corresponding to a set of shim weight and a set of Q matrices.

Parameters:

weights (numpy.ndarray) -- 1D vector of complex shim weights. (length: n_channel)
q_matrix (numpy.ndarray) -- Q matrices used to compute the local energy deposition in the tissues.
(n_channels --
n_channels --
n_voxel) --

Returns:

maximum local SAR.

Return type:

float

shimmingtoolbox.shim.b1shim.phase_only_shimming(b1_maps, init_phases=None)

Performs a phase-only RF-shimming to find a set of phases that homogenizes the B1+ field.

Parameters:

b1_maps (numpy.ndarray) -- 4D array corresponding to the measured B1 field. (x, y, n_slices, n_channels)
init_phases (numpy.ndarray) -- 1D array of initial phase values used for optimization.

Returns:

Optimized and normalized 1D vector of complex shimming weights of length n_channels.

Return type:

numpy.ndarray

shimmingtoolbox.shim.b1shim.vector_to_complex(weights)

Combines real and imaginary values contained in a vector into a half long complex vector.

Parameters:: weights (numpy.ndarray) -- 1D array of length 2*n_channels. First/second half: real/imaginary.
Returns:: 1D complex array of length n_channels.
Return type:: numpy.ndarray

Optimizer 

class shimmingtoolbox.optimizer.basic_optimizer.Optimizer(coils: List[Coil], unshimmed, affine)

Optimizer object that stores coil profiles and optimizes an unshimmed volume given a mask. Use optimize(args) to optimize a given mask. For basic optimizer, uses unbounded pseudo-inverse.

coils

List of Coil objects containing the coil profiles and related constraints

Type:: ListCoil

unshimmed

3d array of unshimmed volume

Type:: numpy.ndarray

unshimmed_affine

4x4 array containing the qform affine transformation for the unshimmed array

Type:: numpy.ndarray

merged_coils

4d array containing all coil profiles resampled onto the target unshimmed array concatenated on the 4th dimension. See self.merge_coils() for more details

Type:: numpy.ndarray

merged_bounds

list of bounds corresponding to each merged coils: merged_bounds[3] is the (min, max) bound for merged_coils[..., 3]

Type:: list

__init__(coils: List[Coil], unshimmed, affine)

Initializes coils according to input list of Coil

Parameters:

coils (ListCoil) -- List of Coil objects containing the coil profiles and related constraints
unshimmed (numpy.ndarray) -- 3d array of unshimmed volume
affine (numpy.ndarray) -- 4x4 array containing the affine transformation for the unshimmed array

merge_bounds()

Merge the coil profile bounds into a single array.

Returns:: list of bounds corresponding to each merged coils
Return type:: list

merge_coils(unshimmed, affine)

Uses the list of coil profiles to return a resampled concatenated list of coil profiles matching the unshimmed image. Bounds are also concatenated and returned.

Parameters:

unshimmed (numpy.ndarray) -- 3d array of unshimmed volume
affine (numpy.ndarray) -- 4x4 array containing the affine transformation for the unshimmed array

optimize(mask)

Optimize unshimmed volume by varying current to each channel

Parameters:

mask (numpy.ndarray) -- 3d array of integers marking volume for optimization. Must be the same shape as unshimmed

Returns:

Coefficients corresponding to the coil profiles that minimize the objective function.: The shape of the array returned has shape corresponding to the total number of channels

Return type:

numpy.ndarray

set_merged_bounds(merged_bounds)

Changes the default bounds set in the coil profile

Parameters:: merged_bounds -- Concatenated coil profile bounds

set_unshimmed(unshimmed, affine)

Set the unshimmed array to a new array. Resamples coil profiles accordingly.

Parameters:

unshimmed (numpy.ndarray) -- 3d array of unshimmed volume
affine -- (numpy.ndarray): 4x4 array containing the qform affine transformation for the unshimmed array

class shimmingtoolbox.optimizer.lsq_optimizer.LsqOptimizer(coils: List[Coil], unshimmed, affine, opt_criteria='mse', reg_factor=0)

Bases: Optimizer

Optimizer object that stores coil profiles and optimizes an unshimmed volume given a mask. Use optimize(args) to optimize a given mask. The algorithm uses a least squares solver to find the best shim. It supports bounds for each channel as well as a bound for the absolute sum of the channels.

__init__(coils: List[Coil], unshimmed, affine, opt_criteria='mse', reg_factor=0)

Initializes coils according to input list of Coil

Parameters:

coils (ListCoil) -- List of Coil objects containing the coil profiles and related constraints
unshimmed (numpy.ndarray) -- 3d array of unshimmed volume
affine (numpy.ndarray) -- 4x4 array containing the affine transformation for the unshimmed array
opt_criteria (str) -- Criteria for the optimizer 'least_squares'. Supported: 'mse': mean squared error, 'mae': mean absolute error, 'std': standard deviation.
reg_factor (float) -- Regularization factor for the current when optimizing. A higher coefficient will penalize higher current values while a lower factor will lower the effect of the regularization. A negative value will favour high currents (not preferred).

get_initial_guess()

Calculates the initial guess according to the self.initial_guess_method

Returns:: 1d array (n_channels) containing the initial guess for the optimization
Return type:: np.ndarray

merge_bounds()

Merge the coil profile bounds into a single array.

Returns:: list of bounds corresponding to each merged coils
Return type:: list

merge_coils(unshimmed, affine)

Uses the list of coil profiles to return a resampled concatenated list of coil profiles matching the unshimmed image. Bounds are also concatenated and returned.

Parameters:

unshimmed (numpy.ndarray) -- 3d array of unshimmed volume
affine (numpy.ndarray) -- 4x4 array containing the affine transformation for the unshimmed array

optimize(mask)

Optimize unshimmed volume by varying current to each channel

Parameters:

mask (numpy.ndarray) -- 3D integer mask used for the optimizer (only consider voxels with non-zero values).

Returns:

Coefficients corresponding to the coil profiles that minimize the objective function.: The shape of the array returned has shape corresponding to the total number of channels

Return type:

numpy.ndarray

set_merged_bounds(merged_bounds)

Changes the default bounds set in the coil profile

Parameters:: merged_bounds -- Concatenated coil profile bounds

set_unshimmed(unshimmed, affine)

Set the unshimmed array to a new array. Resamples coil profiles accordingly.

Parameters:

unshimmed (numpy.ndarray) -- 3d array of unshimmed volume
affine -- (numpy.ndarray): 4x4 array containing the qform affine transformation for the unshimmed array

class shimmingtoolbox.optimizer.lsq_optimizer.PmuLsqOptimizer(coils, unshimmed, affine, opt_criteria, pmu: PmuResp, reg_factor=0)

Bases: LsqOptimizer

Optimizer for the realtime component (riro) for this optimization: field(i_vox) = riro(i_vox) * (acq_pressures - mean_p) + static(i_vox) Unshimmed must be in units: [unit_shim/unit_pressure], ex: [Hz/unit_pressure]

This optimizer bounds the riro results to the coil bounds by taking the range of pressure that can be reached by the PMU.

__init__(coils, unshimmed, affine, opt_criteria, pmu: PmuResp, reg_factor=0)

Initializes coils according to input list of Coil

Parameters:

coils (ListCoil) -- List of Coil objects containing the coil profiles and related constraints
unshimmed (numpy.ndarray) -- 3d array of unshimmed volume
affine (numpy.ndarray) -- 4x4 array containing the affine transformation for the unshimmed array
opt_criteria (str) -- Criteria for the optimizer 'least_squares'. Supported: 'mse': mean squared error, 'mae': mean absolute error, 'std': standard deviation.
pmu (PmuResp) -- PmuResp object containing the respiratory trace information.

get_initial_guess()

Calculates the initial guess according to the self.initial_guess_method

Returns:: 1d array (n_channels) containing the initial guess for the optimization
Return type:: np.ndarray

merge_bounds()

Merge the coil profile bounds into a single array.

Returns:: list of bounds corresponding to each merged coils
Return type:: list

merge_coils(unshimmed, affine)

Uses the list of coil profiles to return a resampled concatenated list of coil profiles matching the unshimmed image. Bounds are also concatenated and returned.

Parameters:

unshimmed (numpy.ndarray) -- 3d array of unshimmed volume
affine (numpy.ndarray) -- 4x4 array containing the affine transformation for the unshimmed array

optimize(mask)

Optimize unshimmed volume by varying current to each channel

Parameters:

mask (numpy.ndarray) -- 3D integer mask used for the optimizer (only consider voxels with non-zero values).

Returns:

Coefficients corresponding to the coil profiles that minimize the objective function.: The shape of the array returned has shape corresponding to the total number of channels

Return type:

numpy.ndarray

set_merged_bounds(merged_bounds)

Changes the default bounds set in the coil profile

Parameters:: merged_bounds -- Concatenated coil profile bounds

set_unshimmed(unshimmed, affine)

Set the unshimmed array to a new array. Resamples coil profiles accordingly.

Parameters:

unshimmed (numpy.ndarray) -- 3d array of unshimmed volume
affine -- (numpy.ndarray): 4x4 array containing the qform affine transformation for the unshimmed array

Image manipulation 

shimmingtoolbox.image.concat_data(list_nii: List[Nifti1Image], axis=3, pixdim=None)

Concatenate data

Parameters:

list_nii -- list of Nifti1Image
axis -- axis: 0, 1, 2, 3, 4.
pixdim -- pixel resolution to join to image header

Returns:

concatenated image

Return type:

ListNii

Numerical model 

Create numerical model data for multi-echo B0 field mapping data

This module is for numerically simulating multi-echo B0 field mapping data. It considers features like: background B0 field, flip angle, echo time, and noise.

Typical usage example:

from shimmingtoolbox.simulate import *

b0_sim = NumericalModel(model="shepp-logan")

# Generate a background B0
b0_field = 13 # (Hz)
b0_sim.generate_deltaB0("linear", [0.0, b0_field])

# Simulate the signal data
FA = 15 # (degrees)
TE = [0.003, 0.015] # (seconds)
SNR = 50
b0_sim.simulate_measurement(FA, TE, SNR)

# Save simulation as NIfTI file (JSON sidecar also exported with parameters)
b0_sim.save('Phase', 'b0_mapping_data.nii', format='nifti')

class shimmingtoolbox.simulate.numerical_model.NumericalModel(model=None, num_vox=128)

Multi-echo B0 field mapping data numerical simulator.

Simulate multi-echo B0 field mapping data in the presence of a B0 field. Can simulate data under ideal conditions or with noise. Export simulations in a NIfTI or .mat file formats.

gamma

Gyromagnetic ratio in rad * Hz / Tesla.

Type:: float

field_strength

Static field strength in Tesla.

Type:: float

handedness: Orientation of the cross-product for the Larmor equation. The value of this attribute is MRI vendor-dependent.

measurement: Simulated measurement data array.

proton_density: Default assumed brain proton density in %.

T2_star: Default assumed brain T2* values in seconds at 3T.

generate_deltaB0(field_type, params)

Generates a background B0 field.

Defines the starting volume. Sets the background B0 field to zeros.

Parameters:

field_type -- Type of field to be generated. Available implementations are: 'linear'.
params -- List of parameters defining the field for the selected field type. If field_type = 'linear', then params are [m b] where m (Hz/pixel) is the slope and b is the floor field (Hz).

save(data_type, file_name, format=None)

Exports simulated data to a file with a JSON sidecar.

Resets the measurement class attribute to zero before simulating. Simulates the signal for each echo-time provided. If defined, adds noise to the complex simulated signal measurements using an SNR value.

Parameters:

data_type -- Export data type. "Magnitude", "Phase", "Real", or "Imaginary".
file_name -- Filename of exported file, with or without file extension.
format -- File format for exported data. If no value given, will attempt to extract format from filename file extension, otherwise default to NIfTI.

simulate_measurement(FA, TE, SNR=None)

Simulates a multi-echo measurement for field mapping

Resets the measurement class attribute to zero before simulating. Simulates the signal for each echo-time provided. If defined, adds noise to the complex simulated signal measurements using an SNR value.

Parameters:

FA -- Flip angle in degrees.
TE -- Echo-times in seconds. Can be either a single value, list, or array.
SNR -- Signal-to-noise ratio used to define noise. If not set, no noise is added to the measurements.

Miscellaneous 

shimmingtoolbox.dicom_to_nifti.dicom_to_nifti(path_dicom, path_nifti, subject_id='sub-01', fname_config_dcm2bids='/home/docs/checkouts/readthedocs.org/user_builds/shimming-toolbox-py/checkouts/latest/config/dcm2bids.json', remove_tmp=False)

Converts dicom files into nifti files by calling dcm2bids

Parameters:

path_dicom (str) -- Path to the input DICOM folder.
path_nifti (str) -- Path to the output NIfTI folder.
subject_id (str) -- Name of the imaged subject.
fname_config_dcm2bids (str) -- Path to the dcm2bids config JSON file.
remove_tmp (bool) -- If True, removes the tmp folder containing the NIfTI files created by dcm2niix.

shimmingtoolbox.dicom_to_nifti.fix_tfl_b1(nii_b1, json_data)

Un-shuffles and rescales the magnitude and phase of complex B1+ maps acquired with Siemens' standard B1+ mapping sequence. Also computes a corrected affine matrix allowing the B1+ maps to be visualized in FSLeyes. :param nii_b1: Array of dimension (x, y, n_slices, 2*n_channels) as created by dcm2niix. :type nii_b1: numpy.ndarray :param json_data: Contains the different fields present in the json file corresponding to the nifti file. :type json_data: dict

Returns:: NIfTI object containing the complex rescaled B1+ maps (x, y, n_slices, n_channels).
Return type:: nib.Nifti1Image

shimmingtoolbox.load_nifti.get_acquisition_times(nii_data, json_data)

Return the acquisition timestamps from a json sidecar. This assumes BIDS convention.

Parameters:

nii_data (nibabel.Nifti1Image) -- Nibabel object containing the image timeseries.
json_data (dict) -- Json dict corresponding to a nifti sidecar.

Returns:

Acquisition timestamps in ms.

Return type:

numpy.ndarray

shimmingtoolbox.load_nifti.load_nifti(path_data, modality='phase')

Load data from a directory containing NIFTI type file with nibabel. :param path_data: Path to the directory containing the file(s) to load :type path_data: str :param modality: Modality to read nifti (can be phase or magnitude) :type modality: str

Returns:: List containing headers for every Nifti file dict: List containing all information in JSON format from every Nifti image numpy.ndarray: 5D array of all acquisition in time (x, y, z, echo, volume)
Return type:: nibabel.Nifti1Image.Header

Note

If 'path' is a folder containing niftis, directly output niftis. It 'path' is a folder containing acquisitions, ask the user for which acquisition to use.

shimmingtoolbox.load_nifti.read_nii(fname_nifti, auto_scale=True)

Reads a nifti file and returns the corresponding image and info. Also returns the associated json data. :param fname_nifti: direct path to the .nii or .nii.gz file that is going to be read :type fname_nifti: str :param auto_scale: Tells if scaling is done before return :type auto_scale: bool, optional

Returns:: Objet containing various data about the nifti file (returned by nibabel.load) json_data (dict): Contains the different fields present in the json file corresponding to the nifti file image (numpy.ndarray): For B0-maps, image contained in the nifti. Siemens phase images are rescaled between 0 and 2pi.
Return type:: info (Nifti1Image)

shimmingtoolbox.download.download_data(urls)

Download the binaries from a URL and return the destination filename Retry downloading if either server or connection errors occur on a SSL connection

Parameters:: urls -- list of several urls (mirror servers) or single url (string)

shimmingtoolbox.download.install_data(url, dest_folder, keep=False)

Download a data bundle from a URL and install in the destination folder.

Parameters:

url -- URL or sequence thereof (if mirrors).
dest_folder -- destination directory for the data (to be created).
keep -- whether to keep existing data in the destination folder.

Returns:

NoneType

Note

The function tries to be smart about the data contents.

Examples:

If the archive only contains a README.md, and the destination folder is ${dst}, ${dst}/README.md will be created. Note: an archive not containing a single folder is commonly known as a "tarbomb" because it puts files anywhere in the current working directory.

If the archive contains a ${dir}/README.md, and the destination folder is ${dst}, ${dst}/README.md will be created. Note: typically the package will be called ${basename}-${revision}.zip and contain a root folder named ${basename}-${revision}/ under which all the other files will be located. The right thing to do in this case is to take the files from there and install them in ${dst}.

Uses download_data() to retrieve the data.
Uses unzip() to extract the bundle.

shimmingtoolbox.download.unzip(compressed, dest_folder)

Extract compressed file to the dest_folder. Can handle .zip, .tar.gz. If none of this extension is found, simply copy the file in dest_folder.

Parameters:

compressed -- the compressed .zip or .tar.gz file
dest_folder -- the destination dir that expanded files are written to

class shimmingtoolbox.pmu.PmuResp(fname_pmu)

PMU object containing the pressure values of a Siemens .resp file

fname

Filename of the Siemens .resp file

Type:: str

data

Pressure values ranging from 0 to 4095

Type:: numpy.ndarray

start_time_mdh

Start time in milliseconds past midnight (mdh clock is expected to be the closest to the image header)

Type:: int

stop_time_mdh

Stop time in milliseconds past midnight (mdh clock is expected to be the closest to the image header)

Type:: int

start_time_mpcu

Start time in milliseconds past midnight

Type:: int

stop_time_mpcu

Stop time in milliseconds past midnight

Type:: int

interp_resp_trace(acquisition_times)

Interpolates data to the specified acquisition_times

Parameters:: acquisition_times (numpy.ndarray) -- 1D array of the times in milliseconds past midnight of the desired times to interpolate the resp_trace. Times must be within self.start_time_mdh and self.stop_time_mdh
Returns:: 1D array with interpolated times
Return type:: numpy.ndarray

read_resp(fname_pmu)

Read a Siemens Physiological Log file. Returns a tuple with the logging data as numpy integer array and times in the form of milliseconds past midnight.

Parameters:

fname_pmu -- Filename of the Siemens .resp file

Returns:

A dict containing the fname_pmu infos. Contains the following keys:

fname

data

start_time_mdh

stop_time_mdh

start_time_mpcu

stop_time_mpcu

Return type:

dict

shimmingtoolbox.utils.add_suffix(fname, suffix)

Add suffix between end of file name and extension.

Parameters:

fname -- absolute or relative file name. Example: t2.nii
suffix -- suffix. Example: _mean

Return:

file name string with suffix. Example: t2_mean.nii

Examples:

add_suffix(t2.nii, _mean) -> t2_mean.nii
add_suffix(t2.nii.gz, a) -> t2a.nii.gz

shimmingtoolbox.utils.create_fname_from_path(path, file_default)

Given a path, make sure it is not a directory, if it is add the default filename, if not, return the path

Parameters:

path (str) -- filename or path to add the file_default to.
file_default (str) -- Name of the file + ext (example.nii.gz) to add to the path if the path is a directory.

Returns:

Absolute path of a file

Return type:

str

shimmingtoolbox.utils.create_output_dir(path_output, is_file=False, output_folder_name='output')

Given a path, create the directory if it doesn't exist.

Parameters:

path_output (str) -- Full path to either a folder or a file.
is_file (bool) -- True if the path_output is for a file, else False.
output_folder_name (str) -- Name of sub-folder.

shimmingtoolbox.utils.fill(data, invalid=None)

Replace the value of invalid 'data' cells (indicated by 'invalid') by the value of the nearest valid data cell

Parameters:

data (numpy.ndarray)) -- array of any dimension
invalid (numpy.ndarray) -- a binary array of same shape as 'data'. True cells set where data value should be replaced. If None (default), use: invalid = np.isnan(data)

Returns:

Return a filled array.

Return type:

numpy.ndarray

shimmingtoolbox.utils.iso_times_to_ms(iso_times)

Convert dicom acquisition times to ms

Parameters:: iso_times (numpy.ndarray) -- 1D array of time strings from dicoms. Suported formats: "HHMMSS.mmmmmm" or "HH:MM:SS.mmmmmm"
Returns:: 1D array of times in milliseconds
Return type:: numpy.ndarray

shimmingtoolbox.utils.montage(X)

Concatenates images stored in a 3D array :param X: 3D array with the last dimension being the one in which the images are concatenated. :type X: numpy.ndarray

Returns:: 2D array of concatenated images.
Return type:: numpy.ndarray

shimmingtoolbox.utils.run_subprocess(cmd)

Wrapper for subprocess.run().

Parameters:: cmd (list) -- list of arguments to be passed to the command line

shimmingtoolbox.utils.save_nii_json(nii, json_data, fname_output)

Save the nii to a nifti file and dict to a json file.

Parameters:

nii (nib.Nifti1Image) -- Nibabel object containing data save.
json_data (dict) -- Dictionary containing the json sidecar associated with the nibabel object.
fname_output (str) -- Output filename, supported types : '.nii', '.nii.gz'

shimmingtoolbox.utils.set_all_loggers(verbose, list_exclude=('matplotlib',))

Set all loggers in the root manager to the verbosity level. Exclude any logger with the name in list_exclude

Parameters:

verbose (str) -- Verbosity level: 'info', 'debug', 'warning', 'critical', 'error'
list_exclude -- List of string to exclude from logging

shimmingtoolbox.utils.splitext(fname)

Split a fname (folder/file + ext) into a folder/file and extension.

Note: for .nii.gz the extension is understandably .nii.gz, not .gz (os.path.splitext() would want to do the latter, hence the special case).

shimmingtoolbox.utils.st_progress_bar(*args, **kwargs): Thin wrapper around tqdm.tqdm which checks SCT_PROGRESS_BAR muffling the progress bar if the user sets it to no, off, or false (case insensitive).

shimmingtoolbox.utils.timeit(func): Decorator to time a function. Decorate a function: @timeit on top of the function definition. The elapsed time will output in debug mode