Demos and FAQs

Demos

Here are some demos to illustrate the basics of MBIRJAX along with some more advanced features.

1. Basic Demo: Jupyter notebook or Python script

2. Large Object: Jupyter notebook or Python script

3. Cropped Center: Jupyter notebook or Python script

4. Wrong Rotation: Jupyter notebook or Python script

5. FBP/FDK: Jupyter notebook or Python script

First browse the notebooks, then copy and run in your own notebook environment, or follow the installation instructions at Installation and run the scripts directly.

Then adjust some of the parameters to better understand how the code works. If you have a GPU, you can increase the problem size by changing num_views, num_det_rows, and num_det_channels.

The separate repo mbirjax_applications provides a wider variety of examples using real data.

FAQs

Q: Why is there a bright ring around my reconstruction?

A: If the object does not project completely inside the detector, then MBIR will produce a bright ring around the edge of the reconstruction to account for the portion of the object that projects to the detector in only some views. See Demo 2: Large Object for an example of this. You can improve the reconstruction by increasing recon_shape:

ct_model.scale_recon_shape(row_scale=1.2, col_scale=1.2)

Note that the scale factor need only be large enough to give some padding around the region of valid projection – it does not need to match the size of the true object. Larger scale factors will lead to increased time and memory.

Q: Why is my reconstruction blurry?

A: If your reconstruction is blurry, the first thing to try is to increase the sharpness parameter. Values of sharpness=1.0 or sharpness=2.0 are typical, but larger values can further improve sharpness. You can also increase the assumed SNR by setting the parameter snr_db=35 or snr_db=40. This is similar to increasing sharpness but will also create higher contrast edges in the reconstruction.

If the reconstruction remains blurry, it is often the case that some geometry parameter is incorrectly set for your data. Typical problems include an incorrect center of rotation (change det_channel_offset), incorrect rotation direction (reverse the angles using angles[::-1]), or an incorrect source_detector_dist or source_iso_dist for cone beam reconstructions.

Q: How can I do larger reconstructions?

A: MBIRJAX runs on both CPU and GPU computers, but we strongly recommend the use of GPUs for large reconstructions since they are much faster. On a GPU, the size of the reconstruction is typically limited by the amount of GPU memory. So you should find a fast GPU with the largest possible memory. These days that is typically 40GB to 80GB of GPU memory. The GPU will be hosted on a CPU, and it is best if that CPU also has even a larger amount of memory, ideally greater than 200GB.

Note that a 2K x 2K x 2K reconstruction occupies 32GB of memory, not counting the sinogram or memory needed for processing. If your reconstruction is too large for your GPU memory, MBIRJAX will use CPU memory for some processing and then transfer to the GPU as needed; this reduces memory use but increases reconstruction time. If you have no GPU or your GPU memory is small relative to the problem size, then all processing is done on the CPU.

If you have a parallel beam system, you can select a subset of rows of your sinogram, reconstruct them separately, and then concatenate them at the end. If you have a cone beam system, you can reconstruct a subset of the central slices. In either case, you can do a center cropped reconstruction as in Demo 3: Cropped Center, although as seen in that demo, this can introduce an intensity shift and other artifacts.

We continue to improve the time and memory efficiency of MBIRJAX and will investigate multi-GPU/multi-CPU solutions. So stay tuned for further improvements.

Q: Why does my reconstruction have artifacts?

There are many reasons that a reconstruction may have artifacts including noise, blurring, streaks, cupping, etc.

First, make sure you are using the geometry (parallel or cone) that matches your data. Parallel beam geometry is faster and could be used for cone beam data, but it may not be accurate if the source is too close to the object.

For transmission tomography, it is critically important to preprocess the raw photon measurements by normalizing by an air-scan and taking the negative log of the ratio. We provide simple preprocessing utilities in mbirjax.preprocess for doing this, and we plan to provide more utilities for specific instruments in the future.

In conebeam scans, it is sometimes the case that the rotation direction is reversed. This can cause the reconstruction to look blurry or distorted. You can correct this by simply taking the negative of your view angles. See Demo 3: Wrong Rotation Direction above for an example of what can happen if the rotation direction is incorrect.

A common artifact is rings near the center of the reconstruction that are generated when the center-of-rotation is not in the center of the detector. This can be corrected by setting the parameter det_channel_offset to reposition the center-of-rotation.

If your reconstruction is blurry, see the FAQ above.

If the reconstruction is too noisy, you might try reducing the value of the sharpness or snr_db parameters (discussed more in the FAQ above on blurry reconstructions). You can also improve reconstruction quality by using the weights array that can be generated using the gen_weights() method. The weights provide information on the reliability of the sinogram values, with larger weights indicating higher reliability.

Streaks are often caused by metal in the object being scanned. One advantage of MBIR is that it generally has fewer metal artifacts, but some artifacts typically remain. Using weights will reduce metal artifacts, and the function gen_weights_mar() can be used to generate weights that further reduce metal artifacts.

Cupping is typically caused by beam hardening with polychromatic X-ray sources. This can be partially corrected with a low order polynomial correction. We are working on utilities to do beam hardening correction in the future.

Ring artifacts away from the center of reconstruction are typically caused by detector nonuniformity. Detector nonuniformity results from the variation in detector sensitivity from pixel to pixel. This variation is taken out to some degree by air scan normalization, but some variation may remain. These variations will lead to concentric rings in the reconstruction. We are working on preprocessing utilities for reducing these ring artifacts.

Q: How can I shift region-of-reconstruction up or down for a conebeam reconstruction?

A: You can shift the region of reconstruction up or down using ct_model.set_params(recon_slice_offset=offset) before calling recon. Positive values of offset will shift the region down relative to the detector. This is useful if you would like to reconstruct the top or bottom half of a conebeam reconstruction in order to save memory.

Q: What are the differences between (iterative) recon and fbp_recon/fdk_recon?

A: The primary reconstruction method in MBIRJAX is iterative reconstruction (mbirjax.TomographyModel.recon) using a Bayesian formulation that balances a data-fitting loss function with a prior function on the reconstruction that reduces noise while maintaining sharp edges. This approach updates the reconstruction multiple times in order to minimize the sum of these two loss functions.

In contrast, FBP (mbirjax.ParallelBeamModel.fbp_recon) and FDK (mbirjax.ConeBeamModel.fdk_recon) are direct methods, in which the sinograms are filtered and then backprojected once to form the reconstruction. In this case, there is no prior information and no attempt to denoise the sinogram or the reconstruction.

In general, FBP and FDK work well when the number of views is large (at least as large as the number of channels in the detector) and the sinograms have little noise. Iterative reconstruction typically works better when there are relatively few views and/or the sinograms are noisy. Iterative reconstruction takes more time and memory than FBP/FDK but can produce significantly better reconstructions when the collected data is less than ideal.