Reconstructing Parallel-Beam Projections
Dragonfly's CT Reconstruction module for reconstructing parallel-beam projection data into 3D volumes includes algorithms implementing Fourier grid reconstruction ('gridrec') and algebraic reconstruction techniques ('art'), as well as pre-processing to improve image quality.
Choose Tools > CT Reconstruction on the menu bar to open the CT Reconstruction dialog, shown below. Then choose TomoPy (Parallel Beam) to access the reconstruction engine and pre-processing options for parallel-beam projections.
CT Reconstruction dialog for parallel-beam projections
The following settings and parameters are available in Dragonfly's CT Reconstruction module for reconstructing parallel-beam projection data.
The options in the Input image box, shown below, let you choose the projection dataset that you want to reconstruct and a reconstruction package.
Projections dataset… Lets you choose the projection dataset that you want to reconstruct.
Reconstruction package… Lets you choose a reconstruction package — RTK (Cone Beam) or TomoPy (Parallel Beam) — for tomographic data processing and image reconstruction.
The options in the Reconstruction engine box let you choose a reconstruction algorithm and the acquisition parameters.
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Description |
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Algorithm |
Dragonfly provides two algorithms for tomographic reconstruction of parallel-beam projections — one is based on solving linear algebra equations and the other is a Fourier grid reconstruction algorithm. art… Algebraic reconstruction technique (art) is an iterative reconstruction technique that starts with an initial estimate of the image. Then a set of projection data is estimated from the initial estimate using a mathematical process called forward projection. The resulting projections are compared with the recorded projections and the differences between the two are used to update the estimated image. The iterative process is repeated until the differences between the calculated and measured data are smaller than a specified preselected value. For more information, refer to: Kak et al. Principles of computerized tomographic imaging. Volume 33. SIAM, 1988. gridrec… Fourier grid reconstruction algorithm. For more information, refer to: Dowd et al. Developments in synchrotron x-ray computed microtomography at the national synchrotron light source. In Proc. SPIE, Volume 3772, (224–236),1999. Rivers ML. Tomorecon: high-speed tomography reconstruction on workstations using multi-threading. In Proc. SPIE, Volume 8506, (85060U–85060U–13), 2012. |
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Filter |
The quality of reconstructed images can be limited by several factors that result in poor spatial resolution, low contrast, and high noise levels. However, filtering can compensate for loss of detail in an image while reducing high-frequency noise. The filter chosen for a given image reconstruction task is mainly a compromise between the reduction of noise and the preservation of detail or contrast of the reproduced image. You should note that the Ramlak and Shepp filters are high pass filters that retain edges, while the Cosine, Hamming, and Hann filters are band pass filters. They are usually used to smooth images and remove extra edges from the image. The following filters, which are applied to data in the frequency domain, are available in the Filter drop-down menu: None… Filtering will not be applied. Butterworth… The Butterworth filter is a low-pass filter and is characterized by two parameters: the critical frequency which is the point at which the filter starts its roll off to zero and the order or power. Because of the ability of changing not only the critical frequency but also the steepness of the roll-off, the Butterworth filter can both smooth noise and preserve image resolution. Cosine… The Cosine filter is obtained by multiplying the Ramlak filter by a cosine function. The advantage of the Cosine and Hann filters is that they reduce image noise. A disadvantage is that they do not fully preserve edges in the image. Hamming… The low pass Hamming filter provides a high degree of smoothing and has only a single parameter to describe its shape — the cut-off frequency. The only difference with the Hann filter is on the amplitude at the cut-off frequency. Hann… The Hann ('Hanning') filter is a relatively simple low-pass filter that is very effective in reducing image noise. However, it does not preserve edges well. Parzen… The Parzen filter is another example of a low pass filter. Ramlak… The Ramlak filter is a high−pass filter that blocks out low frequencies, which can cause blurring. If there are areas in the image where the signal changes rapidly a high−pass filter sharpens the edges and gives better contrast. A disadvantage of Ramlak and other high−pass filters is that they let pass high frequencies and as a consequence also noise. Shepp… The Shepp filter belongs to the family of low pass filters and produces the least smoothing and has the highest resolution. |
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Min angle |
Indicates the minimum angle. |
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Max angle |
Indicates the maximum angle. |
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Mask ratio |
The ratio between the FOV of the camera and the size of object, which is used to generate the mask. |
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Rotation options |
Lets you enter the rotation manually. |
The Pre-processing options let you choose to apply flat-field corrections, median filtering, and other corrections to improve image quality.
Pre-processing options
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Description |
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Flat Field Correction |
If selected, lets you normalize projection data using the flat and dark field projections. In X-ray imaging, acquired projection images generally suffer from fixed-pattern noise, which is one of the limiting factors of image quality. In conventional flat field correction, projection images without the sample are acquired with and without the X-ray beam turned on, which are referred to as flat fields and dark fields respectively. Flat field… Lets you select the flat-field image taken with the X-ray beam turned on, but without the sample. Fixed pattern noise is removed with the assumption that the detector response did not change over the CT scan time. Dark reference… Lets you select the dark-field image taken before the X-ray beam was turned on. |
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Median |
Lets you choose to apply median filtering, at a selected kernel size and shape, to help smooth noisy data. |
Shows the matrix of the output dataset as the number of pixels and dimensions in the X, Y, and Z axes, as well as the voxel size.
Output dimensions
Lets you compute previews at the selected settings, as well as import the settings from a selected preview.
Preview options
Compute Preview… Click this button to generate a one-slice preview using the selected settings.
Import Inputs from Preview… Click this button to open the Import from Preview dialog. You can select any preview to reload the selected settings.
Lets you reconstruct projections at the selected settings.
Reconstruct and Load… Click this button to reconstruct the selected dataset and then load the computed reconstruction into Dragonfly.
Refer to the following instructions for information about reconstructing parallel-beam projections.
- Load your parallel-beam projection dataset (see Importing Image Files).
- Choose Workflows > CT Reconstruction on the menu bar.
The CT Reconstruction dialog appears.
- Select your projection dataset in the Projections dataset drop-down menu.
- Select TomoPy (Parallel Beam) as the reconstruction package in the drop-down menu.
The reconstruction engine options, pre-processing options, and output dimensions appear in the dialog.
- Choose a reconstruction algorithm, filter, and the reconstruction parameters (see Reconstruction Engine).
- Choose the required pre-processing options (see Pre-Processing).
- Review the output dimensions, optional (see Output Dimensions).
- Click the Compute Preview button, recommended, and then review of results of the one-slice reconstruction.
- If the result is acceptable, continue to the next step.
- If the result is unacceptable, modify the reconstruction parameters and/or pre-processing options and then generate another preview.
Note You can load the settings of the best preview by clicking the Import Inputs from Preview button and then selecting the best preview in the Import from Preview dialog.

- Click the Reconstruct and Load button to compute and load the reconstructed dataset.
