Magnetic resonance imaging (MRI) machines are among the most complex devices available in modern medicine. Their engineering merges quantum mechanics, superconducting magnetics, Fourier transform mathematics and advanced pattern detection and imaging software.
Isidor Rabi, a Galician-born US physicist, discovered nuclear magnetic resonance (NMR) in 1944, going on to receive the Nobel Prize in Physics for his work.
Rabi found that the atomic nuclei of certain elements – for example, particular variants of hydrogen, carbon and phosphorus – are like bar magnets. Normally the magnetic directions are randomly aligned: not just north-south, but every direction, effectively cancelling each other out. However, when placed in a strong magnetic field, the magnetic bars of the nuclei line up all pointing the same way.
If the nuclei are then given a radio pulse at a carefully chosen frequency, they rotate their orientation away from the magnetic field. Each nuclei type responds to a different radio frequency: in a particular magnetic field, magnetic hydrogen nuclei might twist at 300MHz, and carbon at 75MHz. As each radio pulse ends, the nuclei emit – as it were – a “sigh” as they relax back again to realign with the magnetic field.
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In practice their sigh is a decaying spiral motion, which induces a detectable electrical signal.
NMR technology has become a common technique for analysing the constituents of a chemical compound.
In 1952, Robert Gabillard published his PhD thesis at ENS Paris showing how the spatial positions of magnetic nuclei can be detected across a magnetic field gradient. Twenty years later Paul Lauterbur, an associate professor at Stony Brook in New York, realised that Gabillard’s insight could be used to build maps of biological tissues.
Different biological tissues “sigh” differently: magnetic hydrogen in fatty tissue may relax faster than magnetic hydrogen in a blood vessel.
Working overnights with the chemistry department’s NMR machine, he built the world’s first MRI machine. His work was submitted to the Nature journal, whose editors promptly rejected his paper asserting that his images were too indistinct. Prof Peter Mansfield, a physicist at the University of Nottingham, further developed Lauterbur’s work by using Fourier analysis on the output signals, greatly accelerating the production of images.
Lauterbur attempted to patent his results, but Stony Brook declined to fund the patent costs, believing that the work was insufficiently valuable. Mansfield did, however, persuade the University of Nottingham to file patents and earned considerable royalties as a result of the commercialisation of MRI machines.
Lauterbur and Mansfield were jointly awarded the Nobel Prize for Medicine in 2003.
The magnetic field in an MRI machine is typically as strong as those used by electromagnetic cranes in metal junkyards. The magnets within an MRI machine are usually made of niobium-titanium, and some 80 per cent of the chemical worldwide is used for MRI machines.
The metals form a superconductor when cooled to -273 degrees. At that temperature, an electric current can endlessly circulate to produce magnetism without consuming power.
Similar technology is used in particle accelerators, such as the large hadron collider at CERN, and can generate extremely powerful magnetic fields without melting the electric wiring needed. However, cooling the magnet to a superconductor temperature can take several days, and thus MRI magnets are kept permanently on.
The field strength of an MRI magnet is usually one to two tesla. Tesla measurements are nothing to do with Elon Musk but named after Nikola Tesla, a pioneer in electricity and magnetism.
After 25 years of research and development, last month the French Alternative Energies and Atomic Energy Commission (CEA) announced the world’s first 11.7 tesla MRI machine, purchased for €70 million of which the magnet assembly alone cost €50 million.
The machine is producing images of the human brain to a resolution of 0.2 of a millimetre, compared to about 1mm in other high-end commercial machines. Significantly, it is able to detect atomic elements such as sodium, phosphorus and fluorine beyond the capabilities of other machines, thus helping further understand the brain’s biochemistry.
The extremely detailed anatomical information should, for example, improve the diagnosis and management of neurodegenerative diseases such as Alzheimer’s or Parkinson’s disease.
Innovation in MRI technology is far from over. Industrial researchers in Germany started working on a 14-tesla machine in 2013, and other initiatives are under way in Korea and the United States.
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