The imaging facility:
X-ray microscopy, hyperspectral imaging and digital image/volume correlation

Our imaging facility at the Faculty of Engineering and Science (University of Greenwich) includes an InCiTe 3D X-ray microscope (KA Imaging) capable of sub-micron resolution and fast phase-contrast (first and only of its kind in Europe) equipped with a custom-designed in situ loading device. We also have digital image correlation (Imetrum) and hyperspectral imaging systems (Living Optics) to assess the structural and biochemical properties of materials under static and dynamic loading conditions. Digital volume correlation software is available and constantly under development by integrating different AI solutions.

Decoding biological tissues and bio-inspired materials with imaging-based measurement, AI and quantum insight

Gianluca Tozzi is the Professor of Industrial Engineering at the School of Engineering (University of Greenwich), where he leads multi-disciplinary research in Bio-Inspired Engineering with Imaging-based AI and quantum data (Bio-AImagiQ). Over the years, he has pioneered research that advanced the understanding of biological tissues and biomaterials through the integration of advanced imaging techniques (e.g. in situ X-ray tomography), full-field measurement techniques (e.g. digital volume correlation) and artificial intelligence.

Recently, groundbreaking models have been developed such as D2IM to predict bone deformation from X-ray tomography greyscale [https://doi.org/10.1016/j.eml.2024.102202], DiffSpectralNet to improve classification in hyperspectral imaging (HSI)[https://doi.org/10.1038/s41598-024-58125-4] and MedDiffHSI to improve tumour detection with HSI [https://doi.org/10.1111/jmi.13372].

New interdisciplinary research is underway and includes AI digital volume correlation of hard/soft tissue, data-driven modelling of hard/soft tissue with vision transformers and diffusion-based architectures, collaborative diffusion multi-modal image analysis of biological tissue, biomaterial formulation with diffusion-language models, composing bone biomimetic materials with AI sonification, quantum representation of images and measurement in the quantum space.

Active projects

Take a deeper look at our ongoing projects in Bio-AImagiQ.

Imaging-based AI models for tissue mechanics

The project, led by PhD student Jon Valijonov, aims at fully developing D2IM by integrating additional AI strategies, advanced XCT segmentation models and exploiting the power of large language models.

AI models for advanced biomaterial design

The project, led by PhD student Moeen Mohammady, aims at applying advanced AI models to analyze, predict, and optimize the hierarchical self-assembly of nanoparticles into macroscale soft biomaterials.

Hyperspectral imaging and AI for biological tissues

The project, led by Dr Neetu Sigger, aims at exploring the unique spectral signature of biological tissues and developing advanced AI models for healthcare.

Imaging-based AI models for bio-composites

The project (KTP-10108115), led by Ms Pinelopi Almpantaki, aims at developing D2IM-based tools, providing the industrial partner with a technology to identify the nature of defects in bio-composites and inform the manufacturing process.

Quantum-AI Synergy for Next-Generation Imaging of Biological Tissues

The project is a fully funded VC PhD Scholarship, open to applications, aiming at developing quantum-native representations of biological tissue images, enabling efficient AI operations such as classification, segmentation, measurement, and multimodal data fusion.

Multi-dimensional sonification of bone tissue XCT images

The project is a cross-disciplinary collaboration between academics and the Faculties of Engineering and Science and Liberal Arts, aiming at aims at investigating a novel multi-dimensional AI sonification approach to design and optimise bone biomimetic materials, blending high-resolution imaging, AI sonification, computational modelling and additive manufacturing.

Research opportunities and media coverage

The Journal of Microscopy is pleased to announce a new special issue featuring papers on “AI in Imaging”. The special issue will be guest edited by Dr Peter Soar (University of Greenwich), Dr Tuan Nguyen (University of Greenwich) and Prof Gianluca Tozzi (University of Greenwich). We welcome submissions for this issue and papers can be reviews, Methods and Protocols, or primary research articles. Deadline extended: 31 July 2025.