Matlab: inverse Radon transform

In summary, The conversation is about using the iradon function in the image processing toolbox to reconstruct an image from a sinogram. The person is unsure about how to get the projection angle data since they were not given a set of angles to work with. However, it is possible to define your own theta and use more or less projections than the image provides.
  • #1
tandoorichicken
245
0
Hopefully someone know image processing toolbox well enough to answer my question!

I need to reconstruct an image from a sinogram, and I'm using the iradon(S,theta) function, where S is the sinogram image and theta is either a scalar or vector quantity of projection angles. However, we weren't given a set of projection angles to work with. Is there a way to look at the sinogram using image processing toolbox and extract the projection angle data?
 
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  • #2
Nevermind, I got it.

In case anyone is wondering,
find the length of the matrix that defines the sinogram,
then specify theta as going from 0:(length(S)-1), where S is your sinogram matrix.

Apparently in the newest version of image processing toolbox you can define your own theta and simply use more or less projections than the image gives you.
 
  • #3


Yes, there is a way to extract the projection angle data from the sinogram using the image processing toolbox. You can use the function "improfile" to extract the intensity values along a line in the sinogram image. By plotting the intensity values against the distance along the line, you can see the peaks and valleys which correspond to the projection angles. You can then use this information to input the correct projection angles into the iradon function. Additionally, you can also use the "imfindcircles" function to automatically detect the projection angles in the sinogram image. I hope this helps!
 

Related to Matlab: inverse Radon transform

1. What is the purpose of the inverse Radon transform in Matlab?

The inverse Radon transform in Matlab is used to reconstruct an image from its projection data. It is commonly used in medical imaging to produce images from CT scan data, as well as in other fields such as seismic imaging and non-destructive testing.

2. How does the inverse Radon transform work in Matlab?

The inverse Radon transform works by taking the projection data, which represents the intensity of x-rays passing through the object at different angles, and mathematically reconstructing the original image. This is done using algorithms such as the filtered back projection method or the Fourier slice theorem.

3. What are the main challenges when using the inverse Radon transform in Matlab?

One of the main challenges when using the inverse Radon transform in Matlab is dealing with artifacts, which are errors in the reconstructed image that can occur due to uneven sampling or noise in the projection data. Another challenge is determining the appropriate level of filtering to apply during the reconstruction process.

4. Are there any limitations to the inverse Radon transform in Matlab?

Yes, there are some limitations to the inverse Radon transform in Matlab. It works best when the projection data is uniformly sampled and the object being imaged is relatively simple in shape. It may also struggle with highly attenuating or low contrast objects, as well as images with a large amount of noise.

5. How can the quality of the reconstructed image be improved when using the inverse Radon transform in Matlab?

There are several ways to improve the quality of the reconstructed image when using the inverse Radon transform in Matlab. These include using more projections, implementing more advanced filtering techniques, and applying corrections for artifacts. It may also be helpful to use other imaging techniques in combination with the inverse Radon transform to produce a more accurate image.

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