Choose a pipeline

Start with the pipeline that most closely matches the hardware, scan geometry and reconstruction goal. Then update its loader paths and processing parameters for the input data. The table below describes the examples on Ready-to-use pipelines; it is guidance rather than a performance ranking.

Starting pipeline	Hardware	Scan or mode	Best starting point for	Main dependencies
FBP3d_tomobar	GPU	180°, analytical	Routine reconstruction with centring and stripe removal	HTTomolibGPU, TomoBAR and HTTomolib
titaren_center_pc_FBP3d_resample	GPU	180°, analytical	Phase-correlation centring and downsampled output	HTTomolibGPU, TomoBAR and HTTomolib
LPRec3d_tomobar	GPU	180°, analytical	Trying LPRec3d on compatible parallel-beam data	HTTomolibGPU, TomoBAR and HTTomolib
FBP3d_tomobar_denoising	GPU	180°, analytical	FBP followed by total-variation denoising	HTTomolibGPU, TomoBAR and HTTomolib
FISTA3d_tomobar	GPU	180°, iterative	Noisy or undersampled data where regularisation is useful	HTTomolibGPU, TomoBAR and HTTomolib
deg360_paganin_FBP3d_tomobar	GPU	360°, analytical	Overlap finding, conversion to 180° and Paganin filtering	HTTomolibGPU, TomoBAR and HTTomolib
deg360_distortion_FBP3d_tomobar	GPU	360°, analytical	Optical-distortion correction before 360° conversion	HTTomolibGPU, TomoBAR and HTTomolib
tomopy_gridrec	CPU	180°, analytical	A small CPU run or a system without a CUDA-capable GPU	TomoPy and HTTomolib
Sweep examples	GPU	Parameter search	Comparing centre-of-rotation or Paganin values	HTTomolibGPU, TomoBAR and HTTomolib

Before running an example:

1. Confirm that its libraries are installed; see Processing libraries.

2. Replace loader paths or use NXtomo automatic discovery; see Loading data.

3. Review method parameters against Available methods.

4. Validate the result with python -m httomo check PIPELINE INPUT.

5. Use the archives in Versioned downloads for a tagged HTTomo release.

Choose analytical reconstruction for a fast baseline. Iterative reconstruction is more computationally expensive but can be valuable for noisy or incomplete data. Actual speed depends on data dimensions, parameters, hardware, process count and storage, so benchmark representative data rather than relying on a general ranking.