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Stefano Zappala
Teams and roles for Stefano Zappala
Publication
2024
- Zappalá, S., Keenan, B. E., Marshall, D., Wu, J., Evans, S. L. and Al-Dirini, R. M. 2024. In vivo strain measurements in the human buttock during sitting using MR-based digital volume correlation. Journal of Biomechanics 163, article number: 111913. (10.1016/j.jbiomech.2023.111913)
2023
- Potts, M. R., Bennion, N. J., Zappala, S., Marshall, D., Harrison, R. and Evans, S. L. 2023. Fabrication of a positional brain shift phantom through the utilization of the frozen intermediate hydrogel state. Journal of the Mechanical Behavior of Biomedical Materials 140, article number: 105704. (10.1016/j.jmbbm.2023.105704)
2022
- Zappala, S. 2022. In-vivo digital volume correlation via magnetic resonance imaging: Application to positional brain shift and deep tissue injury. PhD Thesis, Cardiff University.
2021
- Zappala, S. et al. 2021. Full-field MRI measurements of in-vivo positional brain shift reveal the significance of intra-cranial geometry and head orientation for stereotactic surgery. Scientific Reports 11(1), article number: 17684. (10.1038/s41598-021-97150-5)
2020
- Dyke, R. M. et al. 2020. SHREC'20: Shape correspondence with non-isometric deformations. Computers and Graphics 92, pp. 28-43. (10.1016/j.cag.2020.08.008)
Articles
- Zappalá, S., Keenan, B. E., Marshall, D., Wu, J., Evans, S. L. and Al-Dirini, R. M. 2024. In vivo strain measurements in the human buttock during sitting using MR-based digital volume correlation. Journal of Biomechanics 163, article number: 111913. (10.1016/j.jbiomech.2023.111913)
- Potts, M. R., Bennion, N. J., Zappala, S., Marshall, D., Harrison, R. and Evans, S. L. 2023. Fabrication of a positional brain shift phantom through the utilization of the frozen intermediate hydrogel state. Journal of the Mechanical Behavior of Biomedical Materials 140, article number: 105704. (10.1016/j.jmbbm.2023.105704)
- Zappala, S. et al. 2021. Full-field MRI measurements of in-vivo positional brain shift reveal the significance of intra-cranial geometry and head orientation for stereotactic surgery. Scientific Reports 11(1), article number: 17684. (10.1038/s41598-021-97150-5)
- Dyke, R. M. et al. 2020. SHREC'20: Shape correspondence with non-isometric deformations. Computers and Graphics 92, pp. 28-43. (10.1016/j.cag.2020.08.008)
Thesis
- Zappala, S. 2022. In-vivo digital volume correlation via magnetic resonance imaging: Application to positional brain shift and deep tissue injury. PhD Thesis, Cardiff University.
Research
My research focuses on developing computational methods to capture and quantify non-invasively biological processes via MR imaging, with the goal of improving clinical diagnosis and patient management. I make use of computer vision tools to extract information from multi-dimensional images and quantify biological processes.
My research portfolio includes:
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Non-destructive and in-vivo 3D measurements of soft tissues deformation from anatomical MR images via deformable image registration. We first investigated the deformation of the brain tissue due to gravity (1) that arises following patient repositioning during stereotactic neurosurgery. Then we captured the full-field and three-dimentional deformation of the buttock during sitting in-vivo (2), casting a light on the slide of the gluteus maximum away from the ischial tuberosity, a known risk factor for pressure ulcers.
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Aplication of an oxygen diffusion model to capture brain tissue metabolism via MR relaxometry, with the aim to develop a non-invasive alternative to PET imaging with radiotracers. The methodology was successfully tested on 23 healthy subjects in different physiological conditions as result of the administration of gasses via facemask. The same technique was then applied to a clinical trial aimed to capture the altered metabolism of tumorous brain tissue.
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Experimentation with a deep learning model for the super-resolution reconstruction of quantitative susceptibility mapping, to improve the identification of small peripheral brain vessels and reduce the bias affecting estimations of oxygen metabolism. A previously developed 3D densely connected convolutional neural network was trained and tested on 101 multi-dimensional MR images, showing a reduction of the effect of partial voluming.