Total Body PET 2021 conference [rescheduled] Sep 22, 2021 - Sep 24, 2021 — Virtual Meeting (online)
PET is Wonderful Annual Meeting 2021 Oct 26, 2021 12:00 AM — Virtual Meeting (online)
NRS Mental Health Network Annual Scientific Meeting 2021 Nov 02, 2021 09:00 AM - 05:00 PM — Royal College of Physicians, Edinburgh (and online)

eLearning

SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.


Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

People

Your search for Keyword: 'Physics' returned 12 Result(s)

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ADHD Addictions Ageing Alzheimer's Anaesthesia Artificial Intelligence Attention Autism Bioorganic chemistry Bipolar disorder Brain temperature CT Cardiac imaging Cardiovascular Imaging Carotid Doppler ultrasound Cerebral atrophy Chemistry Clinical decision support Cognitive Control Computer vision DTI-tractography Data protection Database Deep Learning Dementia Depression Diffusion imaging Disorders of Consciousness Doppler ultrasound EEG ERP Elastography Episodic memory Evidence Based Radiology Executive Function Fetal and Child development Field-cycling Functional Connectivity Functional MRI (fMRI) Image analysis Image processing Image segmentation Image-guided intervention Imaging biomarkers Imitation Impaired Consciousness Kinetic Modelling Lacunar stroke Language Large animal imaging Leukoaraiosis Lung Imaging MRA MRI hardware MRI pulse sequences MRS Machine Learning Magnetization transfer Medical visualization Memory Meta-analysis Microvascular MRI Molecular Imaging Multicentre studies Muscle Imaging and Measurement NMR relaxometry Neurodevelopment Neuroinformatics Neurology Neuroradiology Novel Radiotracers Novel imaging methods Nuclear Medicine Oncologic Imaging Oncology Optical imaging PET Parallel computing Perfusion imaging (CT and MR) Permeability imaging (MR) Physics Preclinical Imaging Predictive Classification Psychiatry Radiochemistry Radiology Radiomics Radiotherapy Retinal imaging SPECT Schizophrenia Semantic Memory Simulation Small vessel disease Statistics Stroke Structural imaging TBI Texture analysis Thrombolysis Time series analyses Translational Imaging Ultra-high field MRI Ultrasound White matter disease

Mr Thomas Biggans


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Mrs Megan Couper


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Dr Calum Gray


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Dr Blair Johnston

Research for patient benefit: MRI physics, MRI safety, image analysis and clinical application of AI

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Mr Philipp Loske

Extraction of morphological networks based on structural MRI, analysing network measures that can help in providing neuroimaging makers for dementia.

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Professor David Lurie

My interests lie in the development of new technology and methods in MRI to solve problems in bio-medicine. Together with my group I have worked on methods of imaging free radicals by MRI, and on techniques for imaging solid materials with ultra-short T2 relaxation times. In recent years, my research has focussed on fast field-cycling (FFC) MRI , where the magnetic field is switched rapidly during the imaging procedure. In this way, FFC can access contrast mechanisms which are invisible to conventional MRI. My group has built a variety of MRI scanners over the years, including whole-body sized FFC-MRI scanners. We are currently investigating the use of FFC-MRI in a range of biological and medical research applications.

See our research group's web page at https://www.abdn.ac.uk/research/ffc-mri/ 

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Dr Nicolás Rubido

My research interests are focused on Complexity issues. In general, the complex systems that I research involve Coupled Dynamical Systems, namely, many non-trivially interacting sub-systems. I try to measure, explain, and/or predict their collective behaviour (for example, the emergence of synchronization or chaotic dynamics) in terms of how they are inter-connected, namely, in terms of the topological features of the underlying network topology (i.e., Graph Theory).

In particular, I am fascinated by Network Neuroscience research, where Complexity challenges abound -- neurons in the brain create a myriad of dynamical behaviours due to their intricate connectivity and complex substrate and our observations can only access these behaviours by indirect measurements. Hence, I am intereseted in questions such as, how do we manage to infer the brain's connectivity from indirect measurements (e.g., EEGs or MRIs)? how do particular diseases (e.g., Alzheimer's disease or chronic depression) affect the brain's connectivity? what data-driven conclusions can we draw from studying different states of consciousness (e.g., REM sleep)? and how can we develop/improve methods (both, in data acquisition and analysis) to increase our unserstanding of these issues?

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Emilie Sleight


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Miss Hannah Thomson


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Miss Hannah Thomson


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