Hello Simon and Cyril,Thanks for the reply.You are right Simon. I did not
notice it too in the literature. The main problem as you said is the storage.
Actually I developed the conjugate gradient (CG), quasi-Newton and Newton
optimization methods for optical tomography and I intended to apply them to the
CT reconstruction as well. I implemented the Newton's methods (Gauss-Newton and
Levenberg-Marquardt) in a Jacobian-Free-Newton-Krylov approaches to avoid the
matrix multiplication of Jacobians (sensitivity). It means we only need to
store the Jacobian matrix for the these methods (the matrix R that Cyril was
mentioned), that is still a big matrix for practical problems in CT
reconstruction. For the quasi-Newton I adapted an L-BFGS algorithm that only
need the 3 or 8 last iterations of the gradient vector to calculate the Hessian
matrix. In my case, the L-BFGS and Newton's methods was much faster than the CG
as you know because of using the second order derivative (hessian matrix). I
saw in your last paper you implement the conjugate gradient method, so I
thought it might be easy to extract the gradient vector from CG modules and
solve the cost function within the quasi-Newton/Newton methods. I will look at
the codes to see what I can do.Thanks again for the reply.
@Cyril:Please correct me if I am wrong. you mean the output of
backProjectionFilter is the gradient of defined cost function?
Regards,Vahid
On Wednesday, November 2, 2016 2:53 AM, Cyril Mory
<[email protected]> wrote:
Hi Vahid, Welcome to RTK :) Indeed, there are several iterative methods
already implemented in RTK, but none of the filters allows you to easily
extract the gradient of the least squares function there are minimizing.
If you need to minimize the classical non-regularized tomographic cost
function, ie || R f - p ||², with R the forward projection operator, f the
volume you are looking for, and p the measured projections, my best advice
would be to copy some part of the pipeline of
rtkSARTConeBeamReconstructionFilter to get the job done, ie the following part
(copy-paste this into webgraphviz.com)
digraph SARTConeBeamReconstructionFilter {
Input0 [ label="Input 0 (Volume)"];
Input0 [shape=Mdiamond];
Input1 [label="Input 1 (Projections)"];
Input1 [shape=Mdiamond];
node [shape=box];
ForwardProject [ label="rtk::ForwardProjectionImageFilter" URL="\ref
rtk::ForwardProjectionImageFilter"];
Extract [ label="itk::ExtractImageFilter" URL="\ref itk::ExtractImageFilter"];
MultiplyByZero [ label="itk::MultiplyImageFilter (by zero)" URL="\ref
itk::MultiplyImageFilter"];
AfterExtract [label="", fixedsize="false", width=0, height=0, shape=none];
Subtract [ label="itk::SubtractImageFilter" URL="\ref
itk::SubtractImageFilter"];
MultiplyByLambda [ label="itk::MultiplyImageFilter (by lambda)" URL="\ref
itk::MultiplyImageFilter"];
Divide [ label="itk::DivideOrZeroOutImageFilter" URL="\ref
itk::DivideOrZeroOutImageFilter"];
GatingWeight [ label="itk::MultiplyImageFilter (by gating weight)" URL="\ref
itk::MultiplyImageFilter", style=dashed];
Displaced [ label="rtk::DisplacedDetectorImageFilter" URL="\ref
rtk::DisplacedDetectorImageFilter"];
ConstantProjectionStack [ label="rtk::ConstantImageSource" URL="\ref
rtk::ConstantImageSource"];
ExtractConstantProjection [ label="itk::ExtractImageFilter" URL="\ref
itk::ExtractImageFilter"];
RayBox [ label="rtk::RayBoxIntersectionImageFilter" URL="\ref
rtk::RayBoxIntersectionImageFilter"];
ConstantVolume [ label="rtk::ConstantImageSource" URL="\ref
rtk::ConstantImageSource"];
BackProjection [ label="rtk::BackProjectionImageFilter" URL="\ref
rtk::BackProjectionImageFilter"];
OutofInput0 [label="", fixedsize="false", width=0, height=0, shape=none];
OutofBP [label="", fixedsize="false", width=0, height=0, shape=none];
BeforeBP [label="", fixedsize="false", width=0, height=0, shape=none];
BeforeAdd [label="", fixedsize="false", width=0, height=0, shape=none];
Input0 -> OutofInput0 [arrowhead=none];
OutofInput0 -> ForwardProject;
ConstantVolume -> BeforeBP [arrowhead=none];
BeforeBP -> BackProjection;
Extract -> AfterExtract[arrowhead=none];
AfterExtract -> MultiplyByZero;
AfterExtract -> Subtract;
MultiplyByZero -> ForwardProject;
Input1 -> Extract;
ForwardProject -> Subtract;
Subtract -> MultiplyByLambda;
MultiplyByLambda -> Divide;
Divide -> GatingWeight;
GatingWeight -> Displaced;
ConstantProjectionStack -> ExtractConstantProjection;
ExtractConstantProjection -> RayBox;
RayBox -> Divide;
Displaced -> BackProjection;
BackProjection -> OutofBP [arrowhead=none];
}
As you can see, it is a very large part of the SART reconstruction filter, so
yoiu might be better off just copying the whole
SARTConeBeamReconstructionFilter and modifying it.
Of course, you could also look into ITK's cost function class, and see if one
of the classes inherited from it suits your needs, implement your cost function
this way, and use ITK's off-the-shelf solvers to minimize it. See the
inheritance diagram in
https://itk.org/Doxygen/html/classitk_1_1CostFunctionTemplate.html if you want
to try this approach.
Best regards,
Cyril
On 11/01/2016 05:50 PM, vahid ettehadi via Rtk-users wrote:
Hello RTK users and developers,
I already implemented the RTK and reconstructed some images with the FDK
algorithm implemented in RTK. It works well. Thanks to RTK developers.
Now, I am trying to develop a model-based image reconstruction for our
cone-beam micro-CT. I see already that some iterative algorithms like ART and
its modifications and conjugate-gradient (CG) method are implemented in the
RTK. I want to develop a model-based reconstruction through the
Newton/quasi-Newton optimizations methods. I was wondering is it possible to
extract the gradient of least square function from implemented algorithms like
CG module? Any recommendation will be appreciated.
Best Regards, Vahid
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