What machine detects radio waves?

Magnetic resonance imaging (MRI) uses the body's natural magnetic properties to produce detailed images from any part of the body. For imaging purposes the hydrogen nucleus (a single proton) is used because of its abundance in water and fat.

The hydrogen proton can be likened to the planet earth, spinning on its axis, with a north-south pole. In this respect it behaves like a small bar magnet. Under normal circumstances, these hydrogen proton “bar magnets” spin in the body with their axes randomly aligned.

When the body is placed in a strong magnetic field, such as an MRI scanner, the protons' axes all line up. This uniform alignment creates a magnetic vector oriented along the axis of the MRI scanner. MRI scanners come in different field strengths, usually between 0.5 and 1.5 tesla.

When additional energy (in the form of a radio wave) is added to the magnetic field, the magnetic vector is deflected. The radio wave frequency (RF) that causes the hydrogen nuclei to resonate is dependent on the element sought (hydrogen in this case) and the strength of the magnetic field.

The strength of the magnetic field can be altered electronically from head to toe using a series of gradient electric coils, and, by altering the local magnetic field by these small increments, different slices of the body will resonate as different frequencies are applied.

When the radiofrequency source is switched off the magnetic vector returns to its resting state, and this causes a signal (also a radio wave) to be emitted. It is this signal which is used to create the MR images. Receiver coils are used around the body part in question to act as aerials to improve the detection of the emitted signal. The intensity of the received signal is then plotted on a grey scale and cross sectional images are built up.

Multiple transmitted radiofrequency pulses can be used in sequence to emphasise particular tissues or abnormalities. A different emphasis occurs because different tissues relax at different rates when the transmitted radiofrequency pulse is switched off. The time taken for the protons to fully relax is measured in two ways. The first is the time taken for the magnetic vector to return to its resting state and the second is the time needed for the axial spin to return to its resting state. The first is called T1 relaxation, the second is called T2 relaxation.

An MR examination is thus made up of a series of pulse sequences. Different tissues (such as fat and water) have different relaxation times and can be identified separately. By using a “fat suppression” pulse sequence, for example, the signal from fat will be removed, leaving only the signal from any abnormalities lying within it.

Most diseases manifest themselves by an increase in water content, so MRI is a sensitive test for the detection of disease. The exact nature of the pathology can be more difficult to ascertain: for example, infection and tumour can in some cases look similar. A careful analysis of the images by a radiologist will often yield the correct answer.

There are no known biological hazards of MRI because, unlike x ray and computed tomography, MRI uses radiation in the radiofrequency range which is found all around us and does not damage tissue as it passes through.

Pacemakers, metal clips, and metal valves can be dangerous in MRI scanners because of potential movement within a magnetic field. Metal joint prostheses are less of a problem, although there may be some distortion of the image close to the metal. MRI departments always check for implanted metal and can advise on their safety. Safety information is also available on the internet on

http://kanal.arad.upmc.edu/MR_Safety/

"X-ray vision" that can track people's movements through walls using radio signals could be the future of smart homes, gaming and health care, researchers say.

A new system built by computer scientists at MIT can beam out radio waves that bounce off the human body. Receivers then pick up the reflections, which are processed by computer algorithms to map people’s movements in real time, they added.

Unlike other motion-tracking devices, however, the new system takes advantage of the fact that radio signals with short wavelengths can travel through walls. This allowed the system, dubbed RF-Capture, to identify 15 different people through a wall with nearly 90 percent accuracy, the researchers said. The RF-Capture system could even track their movements to within 0.8 inches (2 centimeters). [10 Technologies That Will Transform Your Life]

Researchers say this technology could have applications as varied as gesture-controlled gaming devices that rival Microsoft's Kinect system, motion capture for special effects in movies, or even the monitoring of hospital patients' vital signs.

"It basically lets you see through walls," said Fadel Adib, a Ph.D. student at MIT's Computer Science and Artificial Intelligence Lab and lead author of a new paper describing the system. "Our revolution is still nowhere near what optical systems can give you, but over the last three years, we have moved from being able to detect someone behind a wall and sense coarse movement, to today, where you can see roughly what a person looks like and even get a person’s breathing and heart rate."

The team, led by Dina Katabi, a professor of electrical engineering and computer science at MIT, has been developing wireless tracking technologies for a number of years. In 2013, the researchers used Wi-Fi signals to detect humans through walls and track the direction of their movement.

The new system, unveiled at the SIGGRAPH Asia conference held from Nov. 2 to Nov. 5 in Japan, uses radio waves that are 1,000 times less powerful than Wi-Fi signals. Adib said improved hardware and software make RF-Capture a far more powerful tool overall.

"These [radio waves used by RF-Capture] produce a much weaker signal, but we can extract far more information from them because they are structured specifically to make this possible," Adib told Live Science.

The system uses a T-shaped antenna array the size of a laptop that features four transmitters along the vertical section and 16 receivers along the horizontal section. The array is controlled from a standard computer with a powerful graphics card, which is used to analyze data, the researchers said.

Because inanimate objects also reflect signals, the system starts by scanning for static features and removes them from its analysis. Then, it takes a series of snapshots, looking for reflections that vary over time, which represent moving human body parts.

However, unless a person’s body parts are at just the right angle in relation to the antenna array they will not redirect the transmitted beams back to the sensors. This means each snapshot captures only some of their body parts, and which ones are captured varies from frame to frame. "In comparison with light, every part of the body reflects the signal back, and that's why you can recover exactly what the person looks like using a camera," Adib said. "But with [radio waves], only a subset of body parts reflect the signal back, and you don't even know which ones."

The solution is an intelligent algorithm that can identify body parts across snapshots and use a simple model of the human skeleton to stich them together to create a silhouette, the researchers said. But scanning the entire 3D space around the antenna array uses a lot of computer power, so to simplify things, the researchers borrowed concepts from military radar systems that can lock onto and track targets. [6 Incredible Spy Technologies That Are Real]

Using a so-called "coarse-to-fine" algorithm, the system starts by using a small number of antennas to scan broad areas and then gradually increases the number of antennas in order to zero in on areas of strong reflection that represent body parts, while ignoring the rest of the room.

This approach allows the system to identify which body part a person moved, with 99 percent accuracy, from about 10 feet (3 meters) away and through a wall. It could also trace letters that individuals wrote in the air by tracking the movement of their palms to within fractions of an inch (just a couple of centimeters).

Currently, RF-Capture can only track people who are directly facing the sensors, and it can't perform full skeletal tracking as traditional motion-capture solutions can. But Adib said that introducing a more complex model of the human body, or increasing the number of arrays, could help overcome these limitations.

The system costs just $200 to $300 to build, and the MIT team is already in the process of applying the technology to its first commercial application — a product called Emerald that is designed to detect, predict and prevent falls among the elderly.

"This is the first application that's going to hit the market," Adib said. "But once you have a device and lots of people are using it, the cost of producing such a device immediately gets reduced, and once it's reduced, you can use it for even more applications."

The initial applications of the technology are likely to be in health care, and the team will soon be deploying the technology in a hospital ward to monitor the breathing patterns of patients suffering from sleep apnea. But as the resolution of the technology increases, Adib said, it could open up a host of applications in gesture control and motion capture.

"We still have a long path to go before we can get to that kind of level of fidelity," he added. "There are a lot of technical challenges that still need to be overcome. But I think over the next few years, these systems are going to significantly evolve to do that."

Follow Live Science @livescience, Facebook & Google+. Original article on Live Science.