New nDTomo software from our Research & Development Lead Scientist Dr Antony Vamvakeros

We are pleased to announce the launch of new nDTomo software from our Research & Development Lead Scientist, Dr Antony Vamvakeros. You can find nDTomo available on PyPI.

nDTomo is an open-source Python software suite for simulation, visualisation, reconstruction, and analysis of chemical imaging and X-ray tomography data. It is especially useful for hyperspectral datasets like XRD-CT.

This software has been seven years in the making. Antony started the project during his postdoctoral at the ESRF – The European Synchrotron building GUIs for handling XRD-CT data at beamline ID15A. Over the years he has developed nDTomo to be far from a GUI – now it’s an integrated platform for researchers and students in materials science, catalysis, batteries, and synchrotron applications.

nDTomo features include:

🔍 Interactive visualization of chemical tomography data via the nDTomoGUI

🧪 Generation of multi-dimensional synthetic phantoms

🎯 Simulation of pencil beam CT acquisition strategies

🧼 Pre-processing and correction of sinograms

🛠️ CT image reconstruction using algorithms like filtered back-projection and SIRT

🧠 Dimensionality reduction and clustering for unsupervised chemical phase analysis

📈 Pixel-wise peak fitting using Gaussian, Lorentzian, and Pseudo-Voigt models

🤖 Peak fitting using the self-supervised PeakFitCNN

🔄 Simultaneous peak fitting and tomographic reconstruction using the DLSR approach with PyTorch GPU acceleration

 

Antony worked on this software alongside Finden colleagues; Dr Evangelos Papoutsellis and Dr Hongyang Dong.

nDTomo is a helpful new tool if you are working with XRD-CT, chemical tomography, or hyperspectral imaging, so try it for yourself.

More information at:

📚 Docs: https://ndtomo.readthedocs.io/en/master/

⭐ GitHub: https://github.com/antonyvam/nDTomo

 

The new software release includes:

• 📚 Full API documentation + 10 Jupyter notebooks, including recent work we have done at the TLDR group at Dyson School of Design Engineering with Ronan Docherty and Prof Sam Cooper on a self-supervised neural network for peak fitting

• 🧠 Transition from tensorflow to PyTorch for all neural-network and GPU-based tools (e.g. PeakFitCNN, DLSR)

• 🧪 Major GUI upgrades, including XRD-CT phantom generator + Embedded IPython console

• 🧹 Refactored, cleaned, and simplified codebase

• 🚀 First stable release to PyPI