Quick Start

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Quick Start#

MBIRJAX is designed to give reconstructions using just a few lines of code. Most functions can be accessed by importing mbirjax and creating a model or through mbirjax directly. Assuming your sinogram is a numpy array in the shape (views, rows, channels) from a parallel beam projection, you can create and visualize a reconstruction using:

import mbirjax
ct_model = mbirjax.ParallelBeamModel(sinogram.shape, angles)
recon, recon_params = ct_model.recon(sinogram)
mbirjax.slice_viewer(recon, title='MBIRJAX Recon')

Demos#

The best way to start is to:

  • Install MBIRJAX using the instructions provided on the Installation Page. We recommend installing in a conda environment or pip virtual environment.

  • View and run demos in Demos and FAQs

You can then adapt these demos to suit your needs.

Application#

Below are simple instructions on how to do your first reconstruction:

  • Get your data:

    • Import your sinogram data as a 3D numpy array organized by (views, detector rows, detector channels (columns)).

    • Create a 1D numpy array called angles that contains the rotation angle in radians of each view.

    Note that each row of sinogram data is assumed to be perpendicular to the rotation axis and each view is assumed to be in conventional raster order (i.e., left-to-right, top-to-bottom) looking through the object from the source to the detector.

    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.

  • Initialize a model:

    • Run model = mbirjax.ParallelBeamModel(sinogram.shape, angles) to initialize a parallel beam model.

    You will then use the model object to perform the various reconstruction functions.

  • Reconstruct and visualize:

    • Run recon, recon_params = model.recon(sinogram) to reconstruct a volume in (rows, columns, slices) format using MBIR.

    • Call mbirjax.slice_viewer(recon, title='MBIRJAX reconstruction')

Even the default parameter settings will usually produce a good quality reconstruction.

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