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Plos One|April 29, 2016
Inter- and Intra-Observer Repeatability of Quantitative Whole-Body, Diffusion-Weighted Imaging (WBDWI) in Metastatic Bone DiseaseMatthew D Blackledge, Nina Tunariu, Matthew R Orton, et al.
The Analyst|March 15, 2013
Spectroscopic imaging based approach for condom identification in condom contaminated fingermarksRobert Bradshaw, Rosalind Wolstenholme, Leesa Susanne Ferguson, et al.
Lung Cancer (Amsterdam, Netherlands)|October 8, 2015
Response evaluation in mesothelioma: Beyond RECISTLin Cheng, Nina Tunariu, David J Collins, et al.
European Radiology|September 28, 2022
Optimisation of b-values for the accurate estimation of the apparent diffusion coefficient (ADC) in whole-body diffusion-weighted MRI in patients with metastatic melanomaAnnemarie K Knill, Matthew D Blackledge, Andra Curcean, et al.
European Radiology|August 24, 2023
The value of baseline 18F-sodium fluoride and 18F-choline PET activity for identifying responders to radium-223 treatment in castration-resistant prostate cancer bone metastasesRicardo Donners, Nina Tunariu, Holly Tovey, et al.
Frontiers in Oncology|August 16, 2021
CT-Based Pelvic T1-Weighted MR Image Synthesis Using UNet, UNet++ and Cycle-Consistent Generative Adversarial Network (Cycle-GAN)Reza Kalantar, Christina Messiou, Jessica M Winfield, et al.
Scientific Reports|June 29, 2023
Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19Reza Kalantar, Sumeet Hindocha, Benjamin Hunter, et al.
Radiology. Artificial Intelligence|October 7, 2021
Accelerating Whole-Body Diffusion-weighted MRI with Deep Learning-based Denoising Image FiltersKonstantinos Zormpas-Petridis, Nina Tunariu, Andra Curcean, et al.
Cancer Imaging : the Official Publication of the International Cancer Imaging Society|July 1, 2026
Predictive imaging biomarkers on whole-body diffusion-weighted MRI (WB-DWMRI) and [68Ga]GaPSMA-PET/CT for [177Lu]LuPSMA therapy in metastatic prostate cancer (mCRPC)Minal Padden-Modi, Jan Taprogge, Peter Dutey-Magni, et al.
Frontiers in Oncology|October 26, 2019
Supervised Machine-Learning Enables Segmentation and Evaluation of Heterogeneous Post-treatment Changes in Multi-Parametric MRI of Soft-Tissue SarcomaMatthew D Blackledge, Jessica M Winfield, Aisha Miah, et al.
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