The September issue of Components in Electronics magazine features an article by Geoff North entitled “The electronic engineering driving the future of medical imaging”, in which Geoff explores how the technology has progressed from delivering static images to today’s advanced systems responding with high precision in real time to deliver life saving solutions.
Read it below or here in the online magazine.
Picture a surgeon performing delicate brain surgery, relying on real-time imaging that must never falter. Or an emergency responder using portable ultrasound in a remote village where the nearest hospital is hours away. These scenarios represent the new reality of medical imaging, where electronic engineers quietly work to solve problems that didn’t exist when imaging systems lived safely in hospital basements.
The engineering challenges are unlike anything in consumer electronics. How do you design systems that combine the precision of laboratory equipment with the portability of a smartphone, all while meeting medical safety standards that leave no room for error?
The answer lies in a convergence of breakthrough technologies that are reshaping the very foundations of medical imaging.
Seeing the invisible
New sensor designs are pushing imaging into previously inaccessible spectral ranges. For instance, shortwave infrared operates between 900 and 1,700 nanometres, where tissue absorbs less light and scatters more. As a result, biological structures once difficult to image become clearly visible.
The commercial landscape has also shifted dramatically in recent years. Three years ago, indium gallium arsenide (InGaAs) cameras were expensive and had limited performance. However, major manufacturers entering the market like Sony drove competition and improved quality. Consequently, high-performance infrared imaging is now viable for clinical integration.
Additionally, multispectral sensors capture RGB alongside near-infrared wavelengths on the same device. Near-infrared penetrates deeper into tissue, making it particularly effective for detecting cancerous cells. Modern hyperspectral systems can now analyse 20-30 different colour channels, allowing clinicians to examine tissue layers at varying depths by adjusting light frequencies.
Three-dimensional imaging offers significant diagnostic advantages over traditional methods. For example, surgeons can assess complete tumour spatial extension rather than relying on thin slice analysis. Nevertheless, implementation challenges remain, including precise camera synchronisation for simultaneous exposure and correction of manufacturing variations across lens mounting. These issues must be carefully resolved throughout the production process to ensure reliable performance.
Intelligence at the point of care
Processing medical imaging data at device level reduces latency and supports real-time clinical workflows. Edge AI enables automatic image analysis during procedures, with algorithms identifying anatomical features that human observers might miss. This capability allows for different aspects of anatomy to be highlighted during live procedures.
Moreover, edge processing positions computational power near medical equipment rather than within devices themselves. This approach makes automatic interpretations for rapid diagnoses possible whilst maintaining processing power for complex AI algorithms.
Additionally, data transmission requirements decrease whilst response times improve for time-sensitive medical applications.
Surgical precision demands higher frame rates to maintain optimal performance. Modern endoscopic systems can now capture 60 frames per second, effectively doubling the previous 15-30 fps standard. This improvement provides smoother imagery that maintains clarity as surgeons navigate through tissue. Importantly, system latency from event occurrence to visual display cannot exceed 150 milliseconds, as beyond that threshold, hand-eye coordination suffers significantly.
Digital staining represents a significant AI-driven advancement in the field. Computational processing generates digital staining effects without physical sample modification, delivering results faster than traditional staining methods. When properly trained, AI systems provide more consistent visualisation than manual processes as well as eliminating the time and chemical requirements of conventional techniques.
Sensor efficiency begins at the component level and significantly influences overall system performance. Photon-counting cameras achieve readout noise values below 0.3 electrons, which substantially reduces power consumption without compromising imaging performance. This efficiency becomes critical when addressing the thermal constraints of medical devices.
Keeping cool under pressure
Heat kills more medical imaging projects than any other single factor. When devices operate within millimetres of human tissue, surface temperatures exceeding 50°C can cause burns, making thermal management a life-safety issue rather than merely a performance consideration. Engineers must balance this constraint against the relentless demand for higher resolution sensors, faster frame rates and real-time AI processing.
The mathematics are unforgiving. A 4K sensor running at 60 fps generates exponentially more heat than its 1080p predecessor, while machine-learning algorithms demand continuous computation cycles that can overwhelm even sophisticated cooling systems.
Advanced processing tasks may run for hours during complex surgical procedures, creating sustained thermal loads that push conventional cooling approaches to their limits.
Modern solutions read like science fiction: image sensors enclosed in hermetically sealed chambers filled with dry nitrogen or argon gases that conduct heat more efficiently than air while preventing moisture damage. Adaptive power management systems monitor usage patterns in real-time, automatically scaling processor frequencies during standby periods and ramping up performance only when imaging demands require it.
The stakes continue to rise with the emergence of therapeutic imaging, where devices are required to capture high-resolution images and deliver focused treatment energy in tandem. These systems pose a significant thermal challenge, demanding that engineers dissipate heat generated by both the imaging sensors and the therapeutic energy sources within a single compact device.
The mobile revolution
The demands on modern medical imaging extend far beyond power constraints. Systems must now deliver hospital-grade performance in diverse environments, from rural clinics to emergency response vehicles, without sacrificing diagnostic accuracy or regulatory compliance.
Healthcare delivery transformed dramatically during the COVID-19 pandemic, when portable imaging devices proved essential for patient care in isolation wards, care homes and makeshift treatment facilities. This shift revealed the true potential of mobile diagnostic technology. Portable ultrasound systems, handheld X-ray devices and compact MRI units now bring sophisticated imaging capabilities directly to patients who might otherwise struggle to access centralised facilities.
The real game-changer lies in instantaneous data transmission. Modern imaging systems capture, process and transmit diagnostic images to Picture Archiving and Communication System (PACS) servers within seconds of acquisition. Radiologists can review emergency scans from remote locations, enabling critical diagnoses while ambulances are still en route to hospitals. This connectivity reshapes how quickly medical decisions can be made.
Behind this seamless experience, however, sits complex infrastructure architecture. Data flows from imaging sensors through edge processing units, across hospital networks and into cloud-based analytics platforms where AI algorithms enhance image quality and flag potential abnormalities. Each component must maintain millisecond-level synchronisation and handle the massive bandwidth requirements of high-resolution medical imagery.
Security challenges multiply exponentially in distributed systems like this. Unlike isolated hospital equipment, portable devices connect across public networks, creating multiple attack vectors. Centralised security management becomes crucial, providing real-time monitoring and automated threat response capabilities that local device configurations cannot match.
The numbers tell the story: healthcare data grows at around 36% annually, with medical imaging representing the largest component of this explosion. Balancing this growth with stringent security requirements and preserving seamless clinical workflows represents one of the most complex engineering challenges in modern medical technology.
The next engineering frontier
Medical imaging has progressed far beyond its original role of capturing static images of the human body. Today’s advanced systems can deliver laser therapy and monitor tissue response in real time or guide high-intensity ultrasound treatments with a level of precision that would have seemed unimaginable only a few years ago.
This marks a new pinnacle in engineering: developing devices that can diagnose and treat concurrently, all while adhering to the rigorous safety standards demanded by medical environments.
The transformation of medical imaging continues to accelerate, powered by engineering innovation. At its core are electronic engineers, driven by the understanding that each technical breakthrough brings life-saving solutions closer to the patients who need them most.