a Short INTRODUCTION Neuro-e module
Every year over 60 million MRI examinations take place worldwide in both research and clinical locations. Thanks to a group of scientists who have improved and developed the technique, MRI has become as sophisticated as it nowadays is. Advances in chemistry, physics, maths and computer science have also played a part, especially in brain research. Specialists and researchers use MRI to study the healthy and unhealthy brain, as a result of the discovery, more than 60 years ago (Bloch and Purcell 1940s) that a chemist’s tool could show detailed images of the internal body. Will techniques evolve even more in the near future, to produce even more detailed and dynamic images of the brain? Surely it will.
Nowadays the scientists have developed techniques to study brain activities and connections, showing many brain structures and functions, but this has also raised new questions.
With traditional MRI, scientists could see nerve cell bodies but not the nerve fibre connections (axons) akin to the roads linking different regions. DTI (Diffusion Tensor Imaging) visualises these connections: it tracks the favoured movement of water molecules along the axons connecting cells, showing the pathways of the brain. Thanks to DTI studies, there is more knowledge about how the brain continues to change throughout life.
Courtesy: Sara Parker
A neuron will form multiple axons, as seen here, when the short version of a signalling molecule is blocked from functioning.
It has also proven useful for tracking short- and long-term structural damage caused by injuries such as concussion, permitting physicians to assess brain damage long after symptoms are resolved.
Just as MRI transformed the diagnosis of cancer, scientists hope that the next generation of imaging techniques may help in the early diagnosis of neurodegenerative diseases such as Alzheimer’s and Parkinson’s and psychiatric disorders, including depression and schizophrenia.
The creation of new tools to study the brain has become one of the most demanding needs and exciting opportunities for today’s neuroscience field. Large-scale projects funded by public and private entities in the United States and Europe aim to further advance imaging technology and technologies that collect, store, distribute, share, and analyse data on brain anatomy and activity. AI (Artificial Intelligence), deep learning and big data mining are vital to progress future developments.
Courtesy: Jesus Rodriguez
The brain has always been considered the main inspiration for the field of AI.
Besides T1 T2 rho T2*, new contrasts are used nowadays such as APT (Amide Proton Transfer) and Sodium imaging. More image contrasts have been developed by using different assets: physical or structural properties (e.g. DWI, MR elastography); functional properties like Perfusion, BOLD, resting state fMRI; and chemical compositions like MRS and CEST (Chemical Exchange Saturation Transfer).
MRE (MR Elastography): HR mean MRE stiffness maps of young and older adults transformed into standard MNI (Montreal Neurological Institute) space, show the prevalence of softer brains with ageing (P < 0.001)
MRE offers a potential biomarker to characterise the viscoelastic properties of the brain in dementia patients, may have a role in the diagnosis and differentiation between common subtypes of dementia (Alzheimer’s disease, frontotemporal dementia, and normal pressure hydrocephalus) and can be of use to show tumour stiffness to predict tumour grading (e.g. in gliomas).
It is important to diagnose neurological disorders in our society: MR is one of the modalities which can supply a good diagnosis and therapy, in combination with other modalities (hybrid imaging).
Courtesy: Richard Ernst
The Tree of Knowledge of Science: NMR researchers invent many methods (e.g. many ladders) to climb the tree
This module covers a very wide range of Neuro MR but there will always be gaps. During one module I went deeper than another one: this is partly because the items are well discussed elsewhere and partly my own interests, of course. It is impossible for me to stay 100% neutral. I am open for additional information and discussions: feel free!
These are the topics I have focused on in the different chapters:
- RF sequences in neuro imaging;
- Acceleration methods such as Multi Band, Simultaneous Multi Slice and Sensing;
- Artificial Intelligence, deep learning and big data handling/mining;
- Diffusion, IVIM and fMRI;
- DSC, DCE T1, Vascular Permeability Imaging, ASL and IVIM Imaging;
- MR Neurography;
- MRA MRV;
- MRS, Sodium and CEST Imaging;
- Hybrid Imaging and MR Elastography.
Questions (false-true and multiple choice) and statements are built in at the end of each chapter. Some examples are included below.
This document should not be construed to represent a definitive interpretation of the regulatory statutes regarding MRI and MRS, and the reader should be aware that regulations might change and render possible out-of-date information specified herein.
Although every attempt has been made to verify the information contained in this e-module, the author cannot guarantee its 100% accuracy. Each effort is made by the editorial board to see that no inaccurate or misleading data, opinion or statements occur. EMRIC sarl cannot accept responsibility for the completeness. This document may not be distributed or re-posted without the express written permission of EMRIC sarl.
I know not everything can be covered, but at least I would like to give an overview of MR knowledge, trying to stay updated, and also putting together what I picked up in the last 36 years. I still like to encourage everybody to do the same!
If you find an aspect of MRI and MRS that I have not covered, do let me know and I will resolve or add it.