A lightweight toolbox for downloading and processing mridata from mridata.org
Project description
mridataPy
mridataPy is
- a lightweight toolbox for downloading and processing mridata from mridata.org
mridataPy supports to
- download dataset from either mridata.org or old.mridata.org
- load mridata to NumPy arrays, which can be stored to .npy files
- generate sampling masks that can densely sample the center region in k-space while subsample the outer region based on acceleration factor
- provide the ground truth reconstructed by applying RSS coil-combination to fullysampled data
- evaluate reconstructions with MSE, NMSE, PSNR, SSIM metrics
Quickstart
Download one case of Stanford Fullysampled 3D FSE Knees (totally, 20 cases) from mridata.org:
import mridatapy
mridata = mridatapy.data.MRIData()
mridata.download(num=1)
Dependencies and Installation
Package Dependencies
pip
will handle all package dependencies.
Install mridataPy
$ pip install mridatapy
Documentation
Module data
MRIData
class mridatapy.data.MRIData(data_type=None, path=None)
-
urls
Attribute: Whole lists of download URLs corresponding to mridata of the given data type.
-
filenames
Attribute: Whole lists of download filenames corresponding to mridata of the given data type.
-
type
Attribute: Data type of mridata.
-
dir
Attribute: Directory to the folder "mridata/" as the default path for mridata.
-
download(num=None)
Instance method: Downloads mridata of the given data type.
-
to_np(num=None, stack=None)
Instance method: Loads mridata to complex-valued k-space NumPy arrays. If not exist, download first.
-
to_npy(path=None, num=None, stack=None)
Instance method: Converts mridata to .npy files. If not exist, download first.
-
get(data_type)
Static method: Gets whole lists of download URLs and filenames corresponding to mridata of the given data type to be downloaded.
-
fetch(url, filename, path)
Static method: Fetches mridata given the specific pair of download URL and filename.
-
ismrmrd_to_np(file, filter=None, prewhiten=None, first_slice=None)
Static method: Loads .h5 ISMRMRD file to complex-valued k-space NumPy array.
-
ismrmrd_to_npy(file, path=None, filter=None, prewhiten=None, first_slice=None)
Static method: Converts .h5 ISMRMRD file to .npy file.
-
cfl_to_np(file)
Static method: Loads .cfl file to complex-valued k-space NumPy array.
-
cfl_to_npy(file, path=None)
Static method: Converts .cfl file to .npy file.
-
unzip(file, path=None, remove=None)
Static method: Unzips .zip file.
-
load_npy(file)
Static method: Loads .npy file.
RandomLine
class mridatapy.data.RandomLine(acceleration_factor, center_fraction)
Generates a sampling mask of the given shape that can densely sample the center region in k-space while subsample the outer region based on acceleration factor. The mask randomly selects a subset of columns from input k-space data.
-
__call__(shape, dtype=numpy.complex64, max_attempts=30, tolerance=0.1, seed=None)
Magic method enables instances to behave like functions.
EquispacedLine
class mridatapy.data.EquispacedLine(acceleration_factor, center_fraction)
Generates a sampling mask of the given shape that can densely sample the center region in k-space while subsample the outer region based on acceleration factor. The mask selects a roughly equispaced subset of columns from input k-space data.
-
__call__(shape, dtype=numpy.complex64, max_attempts=30, tolerance=0.1, seed=None)
Magic method enables instances to behave like functions.
PoissonDisk
class mridatapy.data.PoissonDisk(acceleration_factor, center_fraction)
Generates a sampling mask of the given shape that can densely sample the center region in k-space while subsample the outer region based on acceleration factor. The mask selects a subset of points from input k-space data, characterized by the Poisson disk sampling pattern.
-
__call__(shape, dtype=numpy.complex64, max_attempts=30, tolerance=0.1, seed=None)
Magic method enables instances to behave like functions.
Module utils
fft_centered
function mridatapy.utils.fft_centered(input, shape=None, dim=None, norm=None)
Computes the centered N dimensional discrete Fourier transform (FFT) of input.
ifft_centered
function mridatapy.utils.ifft_centered(input, shape=None, dim=None, norm=None)
Computes the centered N dimensional inverse discrete Fourier transform (IFFT) of input.
root_sum_squares
function mridatapy.utils.root_sum_squares(input, dim, complex=False)
Computes the Root Sum of Squares (RSS) of input along the a given dimension (coil dimension).
Module metrics
mean_squared_error
function mridatapy.metrics.mean_squared_error(gt, pred)
Computes the Mean Squared Error (MSE) between two images.
normalized_mse
function mridatapy.metrics.normalized_mse(gt, pred)
Computes the Normalized Mean Squared Error (NMSE) between two images.
peak_signal_noise_ratio
function mridatapy.metrics.peak_signal_noise_ratio(gt, pred, data_range=None)
Computes the Peak Signal to Noise Ratio (PSNR) between two images.
structural_similarity
function mridatapy.metrics.structural_similarity(gt, pred, data_range=None)
Computes the Structural Similarity Index (SSIM) between two images.
Related Projects
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