SPDHG algorithm from CIL #238
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added: copy, conjugate closes Hackathon-SIRF/#7
notice that it's not taking into consideration SyneRBI/Hackathon-SIRF#2 (comment)
for MR images dot operator calculates the inner product with conjugate by itself.
changed AcquisitionModel to inherit from object.
closes #7
fixes an assertion error
…ckathon-SIRF into convert_spdhg_to_cil
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The PR looks good to me. A general question here is where in STIR/SIRF should all this code go? I.e. the algorithm Also there are a few "hacks" to quickly code things up in examples/Python/PET/interactive/spdhgutils.py like the |
| import pSTIR as pet | ||
| from ccpi.optimisation.spdhg import spdhg | ||
| from ccpi.optimisation.spdhg import KullbackLeibler | ||
| from ccpi.optimisation.spdhg import KullbackLeiblerConvexConjugate |
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The KullbackLeiblerConvexConjugate is not needed here. It is been used implicitly through KullbackLeibler.
| @@ -1202,6 +1212,10 @@ def backward(self, ad): | |||
| (self.handle, ad.handle) | |||
| check_status(image.handle) | |||
| return image | |||
| def direct(self, image): | |||
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Why does CoilImageData have a method direct? This is unintuitive to me.
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what about adjoint? Should it be there?
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Well, I don't know. To me this is a data object and not an operator. So it should neither have direct, forward nor adjoint. But it could be that I did not see any specific CCP-PETMR point of view on this. @KrisThielemans ?
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agree with Matthias - this class should have neither direct nor adjoint
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I'm quite confused about this. The line where this comment is pinned to (here is part of AcquisitionModel. CoilImageData doesn't have forward/direct/backward/adjoint in this PR, as it should be.
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| def PowerMethodNonsquare(op, numiters, x0=None): | ||
| # Initialise random |
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better remove all the comments
| @@ -130,6 +130,10 @@ def main(): | |||
| print('norm of image*10: %f' % image.norm()) | |||
| diff = image.clone() - image | |||
| print('norm of image.clone() - image: %f' % diff.norm()) | |||
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| # test clone vs copy | |||
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this is a test. should it be in an example? I'd rather put it in a test
| g_noreg = ZeroFun() | ||
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| g_reg = FGP_TV_SIRF(lambdaReg=.3, |
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some doc please what this does
| # Author: Matthias J Ehrhardt, Edoardo Pasca | ||
| # | ||
| ## CCP PETMR Synergistic Image Reconstruction Framework (SIRF) | ||
| ## Copyright 2015 - 2018 Rutherford Appleton Laboratory STFC |
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this will need some change!
STIR.py:
adds:
* get_background_term
* saves background term in self.bt member of AcquisitionModel
* get_linear_acquisition_model
* direct
* adjoint
* is_linear
* is_affine
addresses #237 (comment)
added get_constant_term
added get_additive_term
PET acquisition model that relates an image x to the
acquisition data y as
(F) y = [1/n] (G x + [a]) + [b]
where:
G is the geometric (ray tracing) projector from the image voxels
to the scanner's pairs of detectors (bins);
a and b are otional additive and background terms representing
the effects of noise and scattering;
Returns [a] + [b]
added copy method to SIRF.DatContainer class
Adds an example on using the SPDHG algorithm with OS from CIL
Requires SyneRBI/Hackathon-SIRF-SuperBuild#1 which I am going to port from the hackathon repo to this.