In this section
@article{Vamvakeros:2025:10.1039/D5DD00252D,
author = {Vamvakeros, A and Vamvakeros, A and Papoutsellis, E and Dong, H and Docherty, R and Beale, AM and Cooper, SJ and Jacques, SDM},
doi = {10.1039/D5DD00252D},
journal = {Digital Discovery},
title = {nDTomo: a modular python toolkit for X-ray chemical imaging and tomography},
url = {http://dx.doi.org/10.1039/D5DD00252D},
year = {2025}
}
TY - JOUR
AB - nDTomo is a Python-based software suite for the simulation, reconstruction and analysis of X-ray chemical imaging and computed tomography data. It provides a collection of Python function-based tools designed for accessibility and education as well as a graphical user interface. Prioritising transparency and ease of learning, nDTomo adopts a function-centric design that facilitates straightforward understanding and extension of core workflows, from phantom generation and pencil-beam tomography simulation to sinogram correction, tomographic reconstruction and peak fitting. While many scientific toolkits embrace object-oriented design for modularity and scalability, nDTomo instead emphasises pedagogical clarity, making it especially suitable for students and researchers entering the chemical imaging and tomography field. The suite also includes modern deep learning tools, such as a self-supervised neural network for peak analysis (PeakFitCNN) and a GPU-based direct least squares reconstruction (DLSR) approach for simultaneous tomographic reconstruction and parameter estimation. Rather than aiming to replace established tomography frameworks, nDTomo serves as an open, function-oriented environment for training, prototyping, and research in chemical imaging and tomography.
AU - Vamvakeros,A
AU - Vamvakeros,A
AU - Papoutsellis,E
AU - Dong,H
AU - Docherty,R
AU - Beale,AM
AU - Cooper,SJ
AU - Jacques,SDM
DO - 10.1039/D5DD00252D
PY - 2025///
SN - 2635-098X
TI - nDTomo: a modular python toolkit for X-ray chemical imaging and tomography
T2 - Digital Discovery
UR - http://dx.doi.org/10.1039/D5DD00252D
UR - https://pubs.rsc.org/en/content/articlelanding/2025/dd/d5dd00252d
ER -