Reconstruction ecosystem

HTTomo exposes two main reconstruction pathways: a CPU pathway through TomoPy and a GPU pathway through HTTomolibGPU and TomoBAR. Their pipeline entries have the same method and module_path structure, but the packages below those entries have different responsibilities.

The module_path identifies the public function called by HTTomo. It does not necessarily identify the library that performs every numerical operation. For example, a pipeline calls httomolibgpu.recon.algorithm.FBP3d_tomobar; HTTomolibGPU prepares the call, while TomoBAR and ASTRA perform parts of the reconstruction.

Pathways at a glance

Table 1 CPU and GPU reconstruction pathways

Pathway	Pipeline entry	Execution	Implementation	Main dependencies
TomoPy	tomopy.recon.algorithm.recon	CPU, using NumPy arrays	TomoPy selects the reconstruction implementation from its algorithm parameter; gridrec is used by the CPU example pipeline.	TomoPy and its numerical/compiled dependencies; HTTomo distributes work between MPI processes.
TomoBAR	httomolibgpu.recon.algorithm.<method>	NVIDIA GPU; most 3D methods use CuPy arrays	HTTomolibGPU provides the public pipeline methods. TomoBAR supplies direct and iterative reconstruction, using ASTRA or its CuPy Fourier implementation according to the method.	HTTomolibGPU, TomoBAR, ASTRA Toolbox, CuPy, CUDA and a compatible NVIDIA driver.

httomo-backends supports both pathways, but it is not a numerical reconstruction library. It provides HTTomo with method metadata, such as the processing pattern, memory estimator, padding and output dimensions, and it generates the method templates.

The GPU pathway

The diagram shows how HTTomolibGPU presents one pipeline-facing API above the GPU reconstruction components. ASTRA supplies projection and backprojection operators, TomoBAR supplies reconstruction algorithms and CuPy provides CUDA-compatible arrays and kernels. Not every method uses every component: the log-polar method, for example, follows TomoBAR’s Fourier/CuPy route rather than the ASTRA route.

Fig. 21 The GPU reconstruction layers used by HTTomo. The boxes link to the corresponding project documentation.

Table 2 GPU method implementations

HTTomolibGPU method	Type	Data and execution model	Numerical implementation
FBP2d_astra	Analytical FBP	GPU, reconstructed slice by slice; NumPy input and output	TomoBAR’s two-dimensional direct-method wrapper calls ASTRA’s FBP_CUDA implementation.
FBP3d_tomobar	Analytical FBP	GPU volume using CuPy arrays	TomoBAR applies CuPy-based filtering and uses ASTRA for GPU backprojection.
LPRec3d_tomobar	Analytical log-polar	GPU volume using CuPy arrays	TomoBAR performs Fourier inversion on log-polar grids using CuPy; this computational route does not use ASTRA projection operators.
SIRT3d_tomobar and CGLS3d_tomobar	Iterative	GPU volume using CuPy arrays	TomoBAR implements the iteration and uses ASTRA’s GPU forward and backprojection operators.
FISTA3d_tomobar, ADMM3d_tomobar and OSEM3d_tomobar	Regularised iterative	GPU volume using CuPy arrays	TomoBAR combines data-fidelity iterations, ASTRA projection operators and CuPy regularisers.

ASTRA remains a package dependency of TomoBAR even when a particular method, such as LPRec3d_tomobar, does not use ASTRA in its computational path. The method name therefore describes the pipeline-facing implementation, not the complete dependency graph.

The CPU pathway

The TomoPy pathway is shorter: HTTomo passes NumPy data to tomopy.recon.algorithm.recon and the algorithm parameter selects the TomoPy reconstruction implementation. TomoPy may use compiled CPU kernels and local threads, while HTTomo remains responsible for MPI distribution, pipeline ordering and I/O.

Use this pathway for a CPU-only system, a small reconstruction, or when a TomoPy algorithm is specifically required. See the tomopy_gridrec entry in Choose a pipeline for a complete CPU example.

Choosing and troubleshooting a pathway

Choose the algorithm first, then confirm that the required execution stack is available:

• use an analytical method for a fast baseline and an iterative method when the data or reconstruction objective needs it;

• use the TomoPy pathway when CUDA is unavailable;

• use a TomoBAR pathway for the GPU volume methods and regularisation options;

• check Version compatibility before changing HTTomo, HTTomolibGPU, TomoBAR, ASTRA or CuPy independently; and

• validate the finished pipeline with python -m httomo check PIPELINE INPUT before a production run.

For parameter names and defaults, use Available methods. For the role of the processing libraries outside reconstruction, see Processing libraries.