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Tomography Partitioning Tools (TPT)

TPT is a software library and tools for data partitioning of large tomographic reconstruction problems.

It uses a modular system, where kernels, geometries, algorithmic skeletons are independent and can be mixed and matched. We support a general model for distributed computing, enabling the resulting algorithms to run on clusters There is support for 2D, 3D or higher dimensional reconstructions, and support for arbitrary floating point types.


  • tpt_grcb partitioning tool based on an updated geometric recursive coordinate bisectioning algorithm.
  • tpt_stats stats tool to evaluate partitionings.
  • tpt_partition (deprecated), a partitioning tool based on the original geometric recursive coordinate bisectioning algorithm.

It is also possible to perform partitioning on geometries from the ASTRA toolbox. This can be done using the Python bindings, see python/examples/


Code examples for reconstruction tasks

In addition to the partitioning tools, you can run reconstructions using TPT. For example, to reconstruct a Shepp-Logan phantom using SIRT:

#include "tpt/tpt.hpp"

using namespace tpt;

int main() {
    using T = float;
    constexpr dimension D = 2_D;

    int size = 128;
    auto v = volume<D, T>(size);
    auto g = geometry::parallel<D, T>(v, size, size);
    auto f = modified_shepp_logan_phantom<T>(v);
    auto k = dim::joseph<D, T>(v);
    auto p = forward_projection<D, T>(f, g, k);

    auto x = reconstruction::sirt(v, g, k, p);

Geometries and kernels can be used interchangeably. The reason is that we take the following approach to these concepts:

  • A geometry acts as nothing more than a container of lines, so you can write:
for (auto line : geometry) {
    // use line
  • A discrete integration method takes a line, and produces a number of 'matrix elements', that contain the voxel (as an index), and the attenuation coefficient (value of the matrix element):
for (auto element : projector(line)) {
    // element.index is the voxel
    // element.value is the coefficient

Algorithms can be written in an efficient but flexible manner. There are also some standard algorithms implemented, including ART, SART, CGLS, and SIRT.


The Python bindings expose the different concepts (images, volumes, geometries and dims) as well as the standard implemented algorithms.



The following libraries are required:


  • glm header only mathematics library
  • (optional) RECAST3D as a visualization server

Provided as submodules

  • Catch, for unit tests
  • fmt as an iostream replacement
  • bulk for distributed computing
  • cpptoml for reading specification and configuration files
  • (optional) zeromq for communicating with visualization servers
  • (optional) pybind11 to generate Python bindings
  • (optional) MPI for distributed reconstruction

The following build tools should be available on the system:

  • CMake (>= 3.0)
  • Modern C++ compiler (with support for at least C++17), e.g. GCC >= 7.0 or clang >= 4.0

The library is being tested on Fedora 28, but should be portable to other Linux distributions.

Building process

The core of the library is header only, so it does not have to be built itself. We start with initializing the submodules:

git submodule init
git submodule update --remote

To build the examples:

cd build
cmake ..

The resulting binaries will be in the bin folder.

Building the Python bindings

To generate and use the Python bindings:

cd build
cmake .. -DPYTHON_BINDINGS=on 
cd ../python
pip install -e .

python examples/

Building with optional features

To build the ZMQ and MPI based examples, run the following instead of cmake ...

cmake -DDISTRIBUTED=on ..