def mri_knee_data_8_channel():
"""Raw data for 8 channel MRI of a knee.
This is an SE measurement of the knee of a healthy volunteer.
The data has been rescaled so that the reconstruction fits approximately in
[0, 1].
See the data source with DOI `10.5281/zenodo.800529`_ or the
`project webpage`_ for further information.
See Also
--------
mri_knee_inverse_8_channel
References
----------
.. _10.5281/zenodo.800529: https://zenodo.org/record/800529
.. _project webpage: http://imsc.uni-graz.at/mobis/internal/\
platform_aktuell.html
"""
# TODO: Store data in some ODL controlled url
url = 'https://zenodo.org/record/800529/files/3_rawdata_knee_8ch.mat'
dct = get_data('3_rawdata_knee_8ch.mat', subset=DATA_SUBSET, url=url)
# Change axes to match ODL definitions
data = flip(np.swapaxes(dct['rawdata'], 0, -1) * 9e3, 2)
return data
def mri_head_data_32_channel():
"""Raw data for 32 channel MRI of a head.
This is a T2 weighted TSE scan of a healthy volunteer.
The data has been rescaled so that the reconstruction fits approximately in
[0, 1].
See the data source with DOI `10.5281/zenodo.800527`_ or the
`project webpage`_ for further information.
See Also
--------
mri_head_inverse_32_channel
References
----------
.. _10.5281/zenodo.800527: https://zenodo.org/record/800527
.. _project webpage: http://imsc.uni-graz.at/mobis/internal/\
platform_aktuell.html
"""
# TODO: Store data in some ODL controlled url
url = 'https://zenodo.org/record/800527/files/2_rawdata_brainT2_32ch.mat'
dct = get_data('2_rawdata_brainT2_32ch.mat', subset=DATA_SUBSET, url=url)
# Change axes to match ODL definitions
data = flip(np.swapaxes(dct['rawdata'], 0, -1) * 7e3, 2)
return data
def walnut_data():
"""Tomographic X-ray data of a walnut.
Notes
-----
See the article `Tomographic X-ray data of a walnut`_ for further
information.
See Also
--------
walnut_geometry
References
----------
.. _Tomographic X-ray data of a walnut: https://arxiv.org/abs/1502.04064
"""
# TODO: Store data in some ODL controlled url
url = 'http://www.fips.fi/dataset/CT_walnut_v1/FullSizeSinograms.mat'
dct = get_data('walnut.mat', subset=DATA_SUBSET, url=url)
# Change axes to match ODL definitions
data = np.swapaxes(dct['sinogram1200'], 0, 1)[::-1, ::-1]
data = data.astype('float')
# Very crude gain normalization
data = -np.log(data / np.max(data, axis=1)[:, None])
return data
def lotus_root_data():
"""Tomographic X-ray data of a lotus root.
Notes
-----
See the article `Tomographic X-ray data of a lotus root filled with
attenuating objects`_ for further information.
See Also
--------
lotus_root_geometry
References
----------
.. _Tomographic X-ray data of a lotus root filled with attenuating objects:
https://arxiv.org/abs/1609.07299
"""
# TODO: Store data in some ODL controlled url
url = 'http://www.fips.fi/dataset/CT_Lotus_v1/sinogram.mat'
dct = get_data('lotus_root.mat', subset=DATA_SUBSET, url=url)
# Change axes to match ODL definitions
data = np.swapaxes(dct['sinogram'], 0, 1)[:, :]
data = data.astype('float')
return data
def building(shape=None, gray=False):
"""Photo of the Centre for Mathematical Sciences in Cambridge.
Returns
-------
An image with the following properties:
image type: color (or gray scales if `gray=True`)
size: [442, 331] (if not specified by `size`)
scale: [0, 1]
type: float64
"""
# TODO: Store data in some ODL controlled url
name = 'cms.mat'
url = URL_CAM + name
dct = get_data(name, subset=DATA_SUBSET, url=url)
im = np.rot90(dct['im'], k=3)
return convert(im, shape, gray=gray)
def brain_phantom(shape=None):
"""Brain phantom for FDG PET simulations.
Returns
-------
An image with the following properties:
image type: gray scales
shape: [1024, 1024] (if not specified by `size`)
scale: [0, 1]
type: float64
"""
# TODO: Store data in some ODL controlled url
name = 'PET_phantom.mat'
url = URL_CAM + name
dct = get_data(name, subset=DATA_SUBSET, url=url)
im = np.rot90(dct['im'], k=3)
return convert(im, shape)
def rings(shape=None, gray=False):
"""Photo of married couple holding hands.
Returns
-------
An image with the following properties:
image type: color (or gray scales if `gray=True`)
size: [3264, 2448] (if not specified by `size`)
scale: [0, 1]
type: float64
"""
# TODO: Store data in some ODL controlled url
name = 'rings.mat'
url = URL_CAM + name
dct = get_data(name, subset=DATA_SUBSET, url=url)
im = np.rot90(dct['im'], k=2)
return convert(im, shape, gray=gray)
def resolution_phantom(shape=None):
"""Resolution phantom for tomographic simulations.
Returns
-------
An image with the following properties:
image type: gray scales
shape: [1024, 1024] (if not specified by `size`)
scale: [0, 1]
type: float64
"""
# TODO: Store data in some ODL controlled url
# TODO: This can be also done with ODL's ellipse_phantom
name = 'phantom_resolution.mat'
url = URL_CAM + name
dct = get_data(name, subset=DATA_SUBSET, url=url)
im = np.rot90(dct['im'], k=3)
return convert(im, shape)
def blurring_kernel(shape=None):
"""Blurring kernel for convolution simulations.
The kernel is scaled to sum to one.
Returns
-------
An image with the following properties:
image type: gray scales
size: [100, 100] (if not specified by `size`)
scale: [0, 1]
type: float64
"""
# TODO: Store data in some ODL controlled url
name = 'motionblur.mat'
url = URL_CAM + name
dct = get_data(name, subset=DATA_SUBSET, url=url)
return convert(255 - dct['im'], shape, normalize='sum')
