This project is a Python 3 porting of our previous project Occiput.io, which is now no longer supported

TomoLab is (or will be) a tomographic reconstruction software for PET, PET-MRI and SPECT in 2D, 3D (volumetric) and 4D (spatio-temporal) for Python 3.x.

The software provides high-speed reconstruction using Graphics Processing Units (GPU). Note: an NVidia CUDA-compatible GPU is required.

TomoLab can be utilized with arbitrary scanner geometries. It can be utilized for abstract tomographic reconstruction experiments to develop new algorithms and explore new system geometries, or to connect to real-world scanners, providing production quality image reconstruction with standard (MLEM, OSEM, Ordinary Poisson OSEM) and advanced algorithms.

TomoLab implements algorithms for motion correction (direct motion estimation), kinetic imaging, multi-modal reconstruction, respiratory and cardiac gated imaging. The source code contains Jupyter notebooks with examples.

Given the early stage of this project, no installation instruction are currently available, as a lot of the code originally written for Python 2.7 still needs to be ported to Python 3.x

If you want to try out TomoLab while it is being developed, we provide a (devlopment) Docker Image, build to natively support current version of TomoLab. For more information about this, please refer to TomographyLab/DockerImage.

If, instead, you would like to install TomoLab directly in your system, you can (for the time being) have a look at this DockerFile to see what the main dependencies are.

If you have troubles with any of these steps, please just open an Issue here and we will try to sort it out.

Examples and demos of the features of TomoLab are in the /tomolab/Examples folder. A better documentation, and instruction about the best order in which you can study those notebooks will come (hopefully) very soon.

For more information check out our website: it is still based on Occiput.io, previous version of this project, but it should still be a valid starting point to understand the ideas behind this project, and to access some of the publications produced thanks to it.

• Reorganization of the code

  • integrating major (Occiput.io's) dependencies within the main TomoLab project
  • switching to relative imports throughout the code
  • consistently following PEP8 style rules
  • choosing a naming convention for modules, classes and variable (in therm of Captialization, usage of underscores, and so on) and keeping it consistent

• Simulation

  • Several synthetic phantoms ready to be generated. A set of routines allow to create complex geometries (which may also be combined together by addition or subtraction), specifying the desired size and shape.
  • Prepared a documentation notebook to showcase synthetic phantom generation capabilities
  • Python 3.x interfaces to projection and backprojection operation successfully built on top of NityRec low level (C++, CUDA) libraries.

• PET reconstruction

  • Static reconstruction using OSEM and MLEM
  • Implementing basic smoothing prior for OSL-MAP-OSEM
  • Dynamic Reconstruction
  • Class for efficiently handling 2D+t reconstruction (for research purpose)
  • Cyclic Scan Reconstruction, informed of motion information coming from MR vNAV data

• MR reconstruction

  • Everything still needs to be checked after moving from python 2 to python 3

• SPECT reconstruction

  • Everything still needs to be checked after moving from python 2 to python 3

• CT reconstruction

  • CT reconstruction is not yet available. Anyway, it should be straightforward to leverage PE ray-tracing system for CT reconstruction, in the next future.

• Image registration

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• (PET and DCE-MRI) Kinetic modeling

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