1. Sheffield Multimodal Imaging Centre /
  2. Research and publications

Research and publications

From mapping molecules within a fingermark to revealing the internal structure of biological tissues, plants and engineered materials, research at the Sheffield Multimodal Imaging Centre turns advanced imaging into real-world insight. Our multidisciplinary work brings together expertise in biosciences, forensic science, materials engineering, healthcare and computational analysis to address complex research and industrial challenges.

Explore selected publications and research activities across our key areas below:


Forensic Mass Spectrometry Imaging

BMRC - Mass Spectrometry Imaging

• Dissecting the Suitability of DESI-MSI for Molecular Fingerprinting. Krishna R, Millard G, Bradshaw R, Cole LM, Francese S. Anal Chem. 2026 doi: 10.1021/acs.analchem.5c08143.

• A multimodal mass spectrometry imaging workflow for ballpoint pen ink analysis and "forgery" detection. Tibljas V, Francese S, Da Costa Abreu M, Bradshaw R. Analyst. 2025;  doi: 10.1039/d5an00217f.

• A multi-modal mass spectrometry approach for the detection and mapping of date rape drugs in fingermarks. R. Krishna, K. Hamer, S. Bleay, Bradshaw, L. Cole, J. Langridge, E. Claude, J. Bucek, S. Francese, 2025. doi: https://doi.org/10.1039/D5AN00328H

• Multimodal analytical approach applied to whisky labels for authenticity determination. Tibljas V, Francese S, Clark J, Goodall I, Birch F, Bradshaw R. 2025; doi: https://doi.org/10.1016/j.forc.2025.100709. 

• Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination. Kjeldbjerg Lassen J, Bradshaw R, Villesen P, Francese S. Molecules. 2023; doi: 10.3390/molecules28135207.

• Francese S. and Heaton C. Emerging Technologies: Use of Matrix Assisted Laser Desorption Ionisation Mass Spectrometry for the analysis of fingermark and blood evidence IN: Applications of mass spectrometry for the provision of forensic intelligence - state of the art and perspectives Francese S and Bleay SM (eds) 2023

• Detection and mapping of haemoglobin variants in blood fingermarks by MALDI MS for suspect "profiling". Heaton C, Witt M, Cole L, Eyre J, Tazzyman S, McColm R, Francese S.Analyst. 2021; doi: 10.1039/d1an00578b.

• "MALDI-CSI": A proposed method for the tandem detection of human blood and DNA typing from enhanced fingermarks.  Kennedy K, Bengiat R, Heaton C, Herman Y, Oz C, Levin Elad M., Cole L, Francese S, 2021 https://doi.org/10.1016/j.forsciint.2021.11

• R. Bradshaw and S. Francese - MALDI-MSI in the Forensic arena, IN: MALDI Mass Spectrometry Imaging: From Fundamentals to Spatial Omics 978-1-83916-520-7 T Porta (Ed) 2021

• Application of MALDI MS imaging after sequential processing of latent fingermarks Bradshaw R, Wilson G, Denison N, Francese S, 2021. doi: https://doi.org/10.1016/j.forsciint.2020.110643

• Implementation of MALDI MS profiling and imaging methods for the analysis of real crime scene fingermarks. Bradshaw R, Denison N, Francese S, 2017; doi: https://doi.org/10.1039/C7AN00218A

• Optimization of sample preparation and instrumental parameters for the rapid analysis of drugs of abuse in hair samples by MALDI-MS/MS imaging. Flinders B., Beasley E., Verlaan R.M., Cuypers E., Francese S, Bassindale T, Clench MR, Heeren RMA, 2017. doi: https://doi.org/10.1007/s13361-017-1766-0

 

Medical Imaging and Digital Pathology

Research Activities - AI and Image Analysis

• Shiner, A., Kiss, A., Saednia, K., Jerzak, K.J., Gandhi, S., Lu, F.I., et al. (2023). Predicting patterns of distant metastasis in breast cancer patients following local regional therapy using machine learning. Genes, 14(9), 1768.

• Kasturi, S., Tran, W.T. and Shenfield, A. (2022). Accurate nuclei segmentation in breast cancer tumour biopsies. In 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, pp. 1–8.

• Lagree, A., Shiner, A., Alera, M.A., Fleshner, L., Law, E., Law, B., et al. (2021). Assessment of digital pathology imaging biomarkers associated with breast cancer histologic grade. Current Oncology, 28(6), 4298–4316.

• Lagree, A., Mohebpour, M., Meti, N., Saednia, K., Lu, F.I., Slodkowska, E., et al. (2021). A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Scientific Reports, 11(1), 8025.
​

Clinical Imaging and Infrared Thermography

Clinical and Applied Health Imaging 

• Childs, C., Nwaizu, H., Bullivant, E., Willmott, J., Davies, M., Ousey, K., Soltani, H. and Jacques, R. (2023). ‘Cutaneous perfusion dynamics of the lower abdomen in healthy normal weight, overweight and obese women: Methods development using infrared thermography with applications for future wound management after caesarean section’. International Journal of Environmental Research and Public Health, 20, 5100. DOI: 10.3390/ijerph20065100. 

• Childs, C., Elliott, J., Khatab, K., Hampshaw, S., Fowler-Davis, S., Willmott, J.R. and Ali, A. (2020). ‘Thermal sensation in older people with and without dementia living in residential care: New assessment approaches to thermal comfort using infrared thermography’. International Journal of Environmental Research and Public Health, 17, 6932. DOI: 10.3390/ijerph17186932.

• Childs, C., Nwaizu, H., Voloaca, O. and Shenfield, A. (2023). ‘Segmentation agreement and AI-based feature extraction of cutaneous infrared images of the obese abdomen after caesarean section: Results from a single training session’. Applied Sciences, 13, 3992. DOI: 10.3390/app13063992. 

• Childs, C., Wright, N., Willmott, J., Davies, M., Kilner, K., Ousey, K., Soltani, H., Madhuvrata, P. and Stephenson, J. (2019). ‘The surgical wound in infrared: Thermographic profiles and early-stage test accuracy to predict surgical site infection in obese women during the first 30 days after caesarean section’. Antimicrobial Resistance and Infection Control, 8, 7. DOI: 10.1186/s13756-018-0461-7.

Other Computational Imaging Applications

Research Activities - Cross Disciplinary Imaging

• Haverson, K., Smith, R., Shenfield, A., Fortulan, R., Ćwiok, M., Dominik, W., et al. (2024). A ResNeXt50-based convolution neural network for nuclear reaction classification in an active target TPC detector. Il Nuovo Cimento, 48(1).

• Kasturi, S., Shenfield, A., Roast, C., Page, D.L. and Broome, A. (2023). Object Detection in Heritage Archives Using a Human-in-Loop Concept. In UK Workshop on Computational Intelligence, pp. 170–181.


PhD Projects

• Advancing the Molecular Fingerprint Capabilities within Drug Facilitated Sexual Assaults - PhD student Rohith Krishna 2024-2027 - Co-funded by Sheffield Hallam University Transforming Lives Collaborative studentship scheme and Waters Corporation.