Professor Adam Waldman
Chair of Neuroradiology
- Centre for Clinical Brain Sciences
- Edinburgh Imaging
Contact details
- Tel: +44 (0)131 465 9599
- Email: Adam.Waldman@ed.ac.uk
Address
- Street
-
Edinburgh Medical School: Deanery of Clinical Sciences
Centre for Clinical Brain Sciences
The Chancellor's Building
49 Little France Crescent - City
- Edinburgh
- Post code
- EH16 4SB
Qualifications
Doctor of Philosophy (PhD), University of Bristol
Bachelor of Medicine, University of Cambridge, Cambridge, UK
Bachelor of Medicine and Bachelor of Surgery, University of Cambridge, Cambridge, UK
Master of Arts, University of Cambridge, Cambridge, UK
Bachelor of Science, University of Lacaster
Fellowship of the Royal College of Physicians of London, FRCP
Certificate of Completed Specialist Training: Diagnostic Radiology
Fellowship of the Royal College of Radiologists, UK, FRCR Part 2
Fellowship of the Royal College of Radiologists, UK, FRCR Part I
Membership of Royal College of Physicians, UK, MRCP Part II
Membership of Royal College of Physicians, UK, MRCP Part I
Research summary
My own research involves collaborative development and translation of quantitative and physiological neuroimaging techniques. Main areas of interest are neurooncology, neurodegenerative and neuroinflammatory disease.
-
Eano-Euracan-Sno Guidelines on Circumscribed Astrocytic GLIOMAS, Glioneuronal and Neuronaltumors
In:
Neuro-Oncology
DOI: https://doi.org/10.1093/neuonc/noac188
Research output: Contribution to Journal › Article (E-pub ahead of print) -
The Open-Access European Prevention of Alzheimer’s Dementia (EPAD) MRI dataset and processing workflow
In:
NeuroImage: Clinical, vol. 35, pp. 103106
DOI: https://doi.org/10.1016/j.nicl.2022.103106
Research output: Contribution to Journal › Article (E-pub ahead of print) -
FutureMS cohort profile: a Scottish multicentre inception cohort study of relapsing-remitting multiple sclerosis
In:
BMJ Open, vol. 12, pp. e058506
DOI: https://doi.org/10.1136/bmjopen-2021-058506
Research output: Contribution to Journal › Article (Published) -
Logistic Regression-Based Model Is More Efficient Than U-Net Model for Reliable Whole Brain Magnetic Resonance Imaging Segmentation
(9 pages)
In:
Topics in magnetic resonance imaging : TMRI, vol. 31, pp. 31-39
DOI: https://doi.org/10.1097/RMR.0000000000000296
Research output: Contribution to Journal › Article (Published) -
Quantitative magnetisation transfer imaging in relapsing-remitting multiple sclerosis: a systematic review and meta-analysis
In:
Brain Communications
DOI: https://doi.org/10.1093/braincomms/fcac088
Research output: Contribution to Journal › Article (Published)