Source code for cil.plugins.astra.processors.FBP
# Copyright 2019 United Kingdom Research and Innovation
# Copyright 2019 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Authors:
# CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
from cil.framework import DataProcessor
from cil.framework.labels import ImageDimension, AcquisitionDimension, AcquisitionType
from cil.plugins.astra.processors.FBP_Flexible import FBP_Flexible
from cil.plugins.astra.processors.FDK_Flexible import FDK_Flexible
from cil.plugins.astra.processors.FBP_Flexible import FBP_CPU
[docs]
class FBP(DataProcessor):
"""
FBP configures and calls an appropriate ASTRA FBP or FDK algorithm for your dataset.
The best results will be on data with circular trajectories of a 2PI angular range and equally spaced small angular steps.
Parameters
----------
image_geometry : ImageGeometry, default used if None
A description of the area/volume to reconstruct
acquisition_geometry : AcquisitionGeometry
A description of the acquisition data
device : string, default='gpu'
'gpu' will run on a compatible CUDA capable device using the ASTRA FDK_CUDA algorithm
'cpu' will run on CPU using the ASTRA FBP algorithm - see Notes for limitations
Example
-------
>>> from cil.plugins.astra import FBP
>>> fbp = FBP(image_geometry, data.geometry)
>>> fbp.set_input(data)
>>> reconstruction = fbp.get_output()
Notes
-----
A CPU version is provided for simple 2D parallel-beam geometries only, any offsets and rotations in the acquisition geometry will be ignored.
This uses the ram-lak filter only.
"""
def __init__(self, image_geometry=None, acquisition_geometry=None, device='gpu'):
if acquisition_geometry is None:
raise TypeError("Please specify an acquisition_geometry to configure this processor")
if image_geometry is None:
image_geometry = acquisition_geometry.get_ImageGeometry()
AcquisitionDimension.check_order_for_engine('astra', acquisition_geometry)
ImageDimension.check_order_for_engine('astra', image_geometry)
if device == 'gpu':
if acquisition_geometry.geom_type == 'parallel':
processor = FBP_Flexible(image_geometry, acquisition_geometry)
else:
processor = FDK_Flexible(image_geometry, acquisition_geometry)
else:
UserWarning("ASTRA back-projector running on CPU will not make use of enhanced geometry parameters")
if acquisition_geometry.geom_type == 'cone':
raise NotImplementedError("Cannot process cone-beam data without a GPU")
if AcquisitionType.DIM2 & acquisition_geometry.dimension:
processor = FBP_CPU(image_geometry, acquisition_geometry)
else:
raise NotImplementedError("Cannot process 3D data without a GPU")
if acquisition_geometry.channels > 1:
raise NotImplementedError("Cannot process multi-channel data")
#processor_full = ChannelwiseProcessor(processor, self.acquisition_geometry.channels, dimension='prepend')
#self.processor = operator_full
super(FBP, self).__init__( image_geometry=image_geometry, acquisition_geometry=acquisition_geometry, device=device, processor=processor)
def get_input(self):
return self.processor.get_input()
[docs]
def get_output(self, out=None):
return self.processor.get_output(out=out)
def check_input(self, dataset):
return self.processor.check_input(dataset)
def check_output(self, out):
return self.processor.check_output(out=out)
def process(self, out=None):
return self.processor.process(out=out)
