π Lab

Predictive Imaging Lab, School of Engineering, University of Greenwich

We see what happens inside.

Decoding biological tissues and bio‑inspired materials with in‑situ X-ray microscopy, full-field measurement, artificial intelligence and quantum insight.

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XCT greyscale D2IM predicted strain ε z 0428 / 1024
The work

Bone, soft tissue, bio-composites and self-assembled biomaterials: imaged under load, then modelled.

A tomogram is a measurement, not a picture. We build the models that read it.

The Predictive Imaging Lab is led by Gianluca Tozzi, Professor of Industrial Engineering at the University of Greenwich. The group advances the understanding of biological tissues and biomaterials by integrating advanced imaging (in‑situ X-ray tomography, hyperspectral imaging) with full-field measurement such as digital volume correlation, and with artificial intelligence.

Models

Published work

Named models from the lab, with the papers behind them.

All publications
Active projects

What is running now

Six programmes, each led by a member or affiliate of the lab.

Knee radiograph beside a femur rendered with a strain colour map and trabecular bone detail

Imaging-based AI models for tissue mechanics

Fully developing D2IM by integrating additional AI strategies, advanced XCT segmentation models and the power of large language models.

LeadJon Valijonov
Molecular model, joint anatomy, self-assembled nanoparticles and a printed lattice scaffold

AI models for advanced biomaterial design

Applying advanced AI models to analyse, predict and optimise the hierarchical self-assembly of nanoparticles into macroscale soft biomaterials.

LeadMoeen Mohammady
Hyperspectral light dispersed across a tissue sample beside a brain illustration

Hyperspectral imaging and AI for biological tissues

Exploring the unique spectral signature of biological tissues and developing advanced AI models for healthcare.

LeadNeetu Sigger
Scanning electron micrograph of composite fibres mapped to a modelled defect volume

Imaging-based AI models for bio-composites

Developing D2IM-based tools that give the industrial partner a technology to identify the nature of defects in bio-composites and inform manufacturing.

KTP-10108115Pinelopi Almpantaki
Quantum processor and AI circuitry beside a magnified tissue cross-section

Quantum-AI synergy for next-generation imaging

Developing quantum-native representations of biological tissue images, enabling efficient classification, segmentation, measurement and multimodal fusion.

LeadIsabella Florez
Bone lattice cube surrounded by acoustic waveforms and frequency spectra

Multi-dimensional sonification of bone XCT images

A cross-faculty collaboration investigating AI sonification to design and optimise bone biomimetic materials, blending imaging, computational modelling and additive manufacturing.

CollaborationEngineering & Science
Project details
Facility

The imaging bay

Faculty of Engineering and Science, University of Greenwich.

InCiTe 3D X-ray Microscope

KA Imaging

Sub-micron resolution with fast phase-contrast acquisition, paired with a custom-designed in‑situ loading device so specimens can be imaged while they deform.

Resolution< 1 µm
ContrastPhase + absorption
In situCustom loading rig

Hyperspectral Imaging

Living Optics

Spectral signatures that reveal the biochemical state of tissue alongside its structure, and feed our HSI classification and tumour-detection models.

SignalSpectral cube
ReadsBiochemical state
All instruments

Curious about what is inside?

We take on PhD researchers, postdocs and industrial partners working at the intersection of imaging, mechanics and machine learning. Bring a specimen, a dataset or a question.

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