Dr Emad Alsyed Alsyed

Research student

: AlsyedE@cardiff.ac.uk

_{I graduated with a bachelor's degree in Nuclear Engineering - Medical Physics from King Abdulaziz University, KSA. I received my MSc in Physics - Medical Physics from the University of South Florida, USA. Currently, I am a PhD Student in the School of Engineering at Cardiff University.}

_{My research interest focuses on: PET Imaging; Radiomic; Texture Analysis; Radiotherapy;}

Date
Type

2023

Alsyed, E., Smith, R., Bartley, L., Marshall, C. and Spezi, E. 2023. A heterogeneous phantom study for investigating the stability of PET images radiomic features with varying reconstruction settings. Frontiers in Nuclear Medicine 3 (10.3389/fnume.2023.1078536)
Alsyed, E. 2023. Positron emission tomography image texture analysis. PhD Thesis, Cardiff University.

2021

Alsyed, E., Smith, R., Paisey, S., Marshall, C. and Spezi, E. 2021. A self organizing map for exploratory analysis of PET radiomic features. Presented at: 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Boston, MA, USA, 31 October -7 November 20202020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, (10.1109/NSS/MIC42677.2020.9507846)
Smith, R. L., Chandler, H., Alsyed, E., Bartley, L., Fielding, P. and Marshall, C. 2021. Deep learning PET epilepsy detection with a novel symmetric loss convolutional autoencoder. Presented at: 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Boston, MA, USA, 31 October -7 November 20202020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE pp. 1-3., (10.1109/NSS/MIC42677.2020.9507980)

2019

Alsyed, E., Smith, R., Marshall, C., Paisey, S. and Spezi, E. 2019. The statistical influence of imaging time and segmentation volume on PET radiomic features: A preclinical study. Presented at: 2019 IEEE NSS-MIC, 26 October - 2 November 20192019 IEEE NSS-MIC. IEEE
Alsyed, E., Smith, R., Marshall, C., Paisey, S. and Spezi, E. 2019. Stability of PET radiomic features: A preclinical study [Abstract]. Presented at: Annual Congress of the European Association of Nuclear Medicine, Barcelona, Spain, 12-16 Oct 2019.

Project Title: "Positron Emission Tomography Image Texture Analysis In Radiotherapy"

Project Summary: The extraction of texture parameters from medical images increases the diagnostic information and diagnostic power. Texture analysis is an ongoing field of research, with applications ranging from the segmentation of specific anatomical structures and the detection of lesions, to differentiation between pathological and healthy tissue in different organs. Texture analysis can be applied to any medical imaging modality (e.g. CT, PET, MRI) and is at the core of the rapidly expanding field of radiomics (www.radiomics.org). Radiomics includes a wide range of novel image processing techniques and feature extraction algorithms that can rapidly extract data describing intra-tumoural characteristics. Radiomics data can be linked with other data sources (e.g. toxicity, outcome, genomic) to build predictive outcome models. These models could predict the optimal treatment for a specific patient, leading to personalised medicine. This work will focus on the evaluation of the impact that different image acquisition parameters in Positron Emission Tomography have on imaging texture and their effect in radiation therapy.

Emiliano Spezi

Professor of Healthcare Engineering
Director of Research