Overview#
MBIRJAX is a Python package for Model Based Iterative Reconstruction (MBIR) of images from tomographic data.
Here are the reasons to use MBIRJAX:
Image quality: MBIR offers the best image quality because it uses a forward (sensor) and prior (image) model.
Ease of use: MBIRJAX has built-in automatic parameter selection, so it will produce a good reconstruction the first time.
Speed: MBIRJAX is fast (for MBIR) because:
GPU power: Uses JAX to harness the power of GPUs, CPUs, and clusters.
VCD algorithm: The vectorized coordinate descent algorithm has fast convergence to both reduce reconstruction time and improve image quality.
Flexibility:
OO Python interface: Based on object-oriented python, so it is modular and easy to use.
Plug-and-Play prior models: Supports proximal map interfaces, so it can be used with PnP deep neural net priors.
In Demos and FAQs, we provide several demos as Jupyter notebooks and python scripts that make it easy to get started. You can use pip install to install mbirjax or install from source as described in Installation.
Geometries
MBIRJAX supports the parallel-beam and cone-beam imaging geometries shown below. However, new geometries can be added by constructing a new class with the associated sparse forward and back projection code.
