Dr
Sarah
Needleman
PhD
School of Medicine and Population Health
Post-doctoral Research Associate in Pulmonary MRI
s.h.needleman@sheffield.ac.uk
Polaris
Full contact details
Dr Sarah Needleman
Polaris
School of Medicine and Population Health
Polaris
18 Claremont Crescent
Sheffield
S10 2TA
- Profile
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My research advances the use of MRI to assess lung function.
I have previously focused on using inhaled pure oxygen as an MRI contrast agent, a technique known as oxygen-enhanced MRI (OE-MRI).
- Qualifications
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- Doctor of Philosophy (PhD) in Medical Imaging, University College London, 2020 - 2024
- Master of Research in Medical Imaging (MRes), University College London, 2019 - 2020
- Master of Natural Sciences (MSci), University of Cambridge, 2014 - 2018
- Research interests
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- Lung MRI
- Tissue oxygenation
- Dynamic imaging
- Publications
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Journal articles
- Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance. Journal of Big Data, 11(1). View this article in WRRO
- Feasibility of dynamic T2*-based oxygen-enhanced lung MRI at 3T. Magnetic Resonance in Medicine, 91(3), 972-986. View this article in WRRO
- Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T. Magnetic Resonance in Medicine, 91(3), 955-971. View this article in WRRO
- Automated airway quantification associates with mortality in idiopathic pulmonary fibrosis. European Radiology, 33(11), 8228-8238. View this article in WRRO
- A novel methodology using direct patient contact and UK national registries to collect long-term data from randomised trials: TARGIT-X – an extended follow-up study of the TARGIT-A trial of targeted intraoperative radiotherapy for breast cancer. Health Technology Assessment, 1-32.
Conference proceedings
- Functional Lung MRI at 3.0 T using Oxygen-Enhanced MRI (OE-MRI) and Independent Component Analysis (ICA). ISMRM Annual Meeting, 7 May 2022 - 12 May 2022.
- Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance. Journal of Big Data, 11(1). View this article in WRRO