Joanna Goodrich is the associate editor of The Institute, covering the work and accomplishments of IEEE members and IEEE and technology-related events. She has a master's degree in health communications from Rutgers University, in New Brunswick, N.J.
Research engineer Godfrey Hounsfield invented the CT scanner to create three-dimensional brain images.
When Hounsfield returned to work after that vacation, he proposed a project to his supervisor to develop a machine that could create three-dimensional brain images. The machine would project narrow beams of X-rays through a person’s head, and a computer would use the resulting data to construct a series of cross-sections that together would represent the brain in 3D.
Hounsfield worked with neuroradiologists to build the machine, and in 1971 they produced the first computed tomography scan of a human brain. CT scans are now used to pinpoint the location of blood clots, tumors, and bone fractures.
For his invention, Hounsfield was named corecipient of the 1979 Nobel Prize in Physiology or Medicine.
Hounsfield’s scanner was commemorated with an IEEE Milestone during a ceremony held on 26 October at the EMI Old Vinyl Factory, in Hayes, England, where the technology was developed. The IEEE United Kingdom and Ireland Section sponsored the nomination.
Selling Beatles albums and developing medical equipment
After the X-ray machine was invented in 1896, it quickly became standard equipment in hospitals. The machines produce great images of bones because their dense structures absorb X-ray beams well. The absorption pattern makes the bones look white on film. But soft-tissue organs such as the brain looked foggy because the radiation passed through them.
While Hounsfield served with the Royal Air Force, he learned the basics of electronics and radar. In 1951 he joined EMI, where he developed guided weapon systems and radar. His interest in computers grew, and in 1958 he helped design the Emidec 1100—the first commercially available all-transistor computer made in Britain.
After that project, Hounsfield’s supervisor warned him that his job would be in jeopardy if he didn’t come up with another good idea.
Hounsfield thought back to the conversation with the doctor about the limitations of X-ray images, then he proposed the project that would become the CT scanner.
EMI didn’t develop or manufacture medical equipment and wasn’t interested in getting into that line of business, but Hounsfield’s supervisor believed in his idea and approved it. The company couldn’t fully fund the project, so Hounsfield applied for and received a grant of around US $40,000—approximately $300,000 in 2022 figures—from the British Department of Health and Social Care.
A CT scanner for cow brains and human ones
Hounsfield worked with neuroradiologists James Ambrose and Louis Kreel to build the first prototype. It was small enough to sit atop a table. They tested the machine on small pigs, and after successfully producing images of their brains, the three men built a full-size scanner.
The CT scanner was first tested on human brains preserved in formalin. But the brains weren’t ideal because the chemical had hardened their tissues so severely that they no longer resembled normal brain matter, as described in an article about the scanner inThe Jewish News of Northern California. Because the scanner was intended for use on living patients, Hounsfield and his team looked for a brain similar to a human’s.
They procured fresh cow brains, but those couldn’t be used because an electric shock was used to stun the animals before they were slaughtered. The procedure caused the brain to fill with blood, and the fluid obstructed the radiologists’ view of the organ’s structure.
Ambrose, who was part Jewish, suggested using kosher cow brains because instead of being stunned, the animals had their jugular slit. The process drained blood away from the skull—which enabled clear CT scans of the brain.
After several successful tests, the machine was ready to be tried on a human. The scanner was installed in 1971 at Atkinson Morley Hospital, in London, where Ambrose worked. The first patient was a woman who showed signs of a brain tumor.
She lay on a table as X-rays were shot through her skull from a single site above her head. The beams passed through her and struck a crystal detector housed in the gantry below her head. Both the X-ray source and the detector moved around her in 1-degree increments until they had turned 180 degrees, with each device ending up at the other one’s starting point.
That allowed the scanner to depict the brain in individual layers. Hounsfield described it as putting the brain “through a bacon slicer,” according to an article about the scanner on the Siemens MedMuseum website.
The detector recorded the X-ray signals and sent the data to a computer. The computer constructed an image of the brain using physicist Allan MacLeod Cormack’s algebraic reconstruction technique. The technique built up an image by filling in a matrix, each square of which corresponded to a part of the examined organ, according to a Nobel news release about the scanner. Because the crystal detector was 100 times more sensitive than X-ray film, the density resolution was much higher, making the resulting image much clearer. Cormack shared the 1979 Nobel Prize with Hounsfield.
The scan took 30 minutes and the computerized construction of the image took another two hours. The image showed a cystic mass about the size of a plum on the patient’s left frontal lobe.
EMI began manufacturing CT scanners and sold them to hospitals with success. But within five years, General Electric, Siemens, and other companies began making more enhanced, full-body scanners. EMI eventually stopped producing its scanners because it couldn’t compete with the other manufacturers.
Administered by the IEEE History Center and supported by donors, the Milestone program recognizes outstanding technical developments around the world.
The CT scanner’s Milestone plaque, which is displayed on an exterior wall at the Old Vinyl Factory, reads:
On 1 October 1971, a team at the EMI Research Laboratories located on this site produced an image of a patient’s brain, using the world’s first clinical X-ray computerized tomography scanner, based on the patented inventions of Godfrey Hounsfield. The practical realization of high-resolution X-ray images of internal structures of the human body marked the beginning of a new era in clinical medicine.
In this article, SimpliSafe’s VP of Software Engineering discusses his team’s focus on creating a safer future through enhanced technology.
Using AI to enhance safety and customer experience
Since its founding in 2006, SimpliSafe has been reimagining antiquated security products and services and developing accessible technology that can be easily installed into homes across the country. It redefined the home security space by introducing wireless, DIY products and pioneered giving customers the ability to monitor their homes via a smartphone app. Today, SimpliSafe’s talented engineering team continues to innovate by investigating how AI can be used to reduce false alarms and enhance the customer experience.
False alarms are a constant obstacle in the home security and emergency response industries. Not only are false alarms annoying to customers, but they also make it difficult for emergency responders to differentiate between real emergencies and accidental alarm trips, which in turn, result in slower response times. One of our engineering team’s top priorities is understanding how they can reduce false alarms and send help fast to address verified emergencies. The potential solution? Integrating AI into security cameras.
Our engineering team is developing AI that will make our cameras “smarter” and give them the ability to identify specific objects to reduce false alarms and enhance the customer experience.
Imagine having an outdoor camera that could tell you if the vehicle in your driveway is a friend, a stranger, or a police car. Or what about having a video doorbell that could tell you if the backpacked teenager on your front stoop is your daughter returning home from school or a delivery person dropping off tonight’s takeout dinner? Customers are only interested in receiving alerts for movements that are out of the ordinary and security monitoring centers are only interested in addressing emergency events. Thanks to AI, we’re on our way to addressing all of these needs.
Currently, our engineering team is developing AI that will make our cameras “smarter” and give them the ability to identify specific objects with the ultimate goal of delivering a best-in-class customer experience. With these advanced cameras, customers will receive tailored app alerts detailing the specifics of the detected camera activity. In time, emergency operators will only receive calls for activity that appears out of the ordinary, thereby reducing false alarms and accelerating emergency response.
As AI technology develops and as our engineering team grows, we can explore more ways AI can make SimpliSafe smarter, faster, simpler and more accurate to help us advance the future of security. There is still plenty to be uncovered and our engineers have a unique opportunity to create life-saving products and services that have a truly positive impact on the customer.
Building infrastructure to meet the demand of a growing customer base
Today, SimpliSafe has over 1 million subscribing customers and nearly 5 million connected devices working hard to protect homes against intruders, fires, water damage, and more. Managing the server activity of those connected devices is a unique challenge to SimpliSafe. Few companies come close to SimpliSafe’s connected device volume.
Due to the large volume, we move beyond what any major cloud provider can handle. Even the biggest international SaaS providers running on common cloud solutions only support a few hundred thousand devices at a given time. Given the lack of scalable cloud providers, we must turn to our engineers to create a solution that can scale as our customer base grows and ensure customers’ servers are always operating smoothly, without interruption.
To add to the challenge, most internet of things (IoT) companies use servers for “request and response,” meaning that the connection used to first understand a customer’s request and then provide them with a response is short-lived. SimpliSafe works differently. Unlike other IoT companies, we must keep long-lived connections for our backend servers. Constant connection between the customer and the server is crucial because it allows us to trace an alert back to a relevant customer and notify them of an emergency quickly, a task that would take too long if we used “request and response” protocol.
As an award-winning home security company, we understand better than anyone how important it is for a customer in a potentially dangerous situation to receive help swiftly. Not only do our engineers need to create a solution to manage our servers, but they must also create a solution that can load-balance with ease and provide a highly responsive experience for our customers.
Thankfully, our talented team has created new and innovative technologies that are able to control all our servers as well as the data it processes. But load-balancing maintenance is an ongoing focus. We are always looking to hire more engineers who can help us scale to address our growing customer base.
SimpliSafe’s engineers are leading a growing number of innovative projects, more than can be outlined here. They span the gamut of advanced technology and engineering, including the development of new hardware devices, cameras, imaging technology, app development, cloud computing, cyber security protection, motion detection and user authentication technologies. No matter what our engineers are focused on, all are dedicated to enhancing safety. It’s our mission to make every home secure that pushes us forward and challenges us to reimagine a safer world, achieved with the help of exceptional home security products.
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