Medical imaging trends and key components

Healthcare would look very different today if it wasn’t for medical imaging. X-rays, ultrasounds, endoscopy, optical coherence tomography (OCT), computerized tomography (CT), molecular imaging (MI), and magnetic resonance imaging (MRI), among others, are essential for patient diagnosis and therapy.

As one of the most important advances in medical history, imaging has evolved to provide higher-resolution images with the convergence of varied modalities having proved instrumental to early diagnosis and treatment.

The advancements in medical imaging hinge on a foundation of sophisticated hardware and software systems that register, store, analyze, and provide highly accurate real-time processing of images and data in large volumes. The cost, performance, development, and implementation of hardware and software technology play a critical role in maximizing the overall investment of equipment and its life span, which greatly contribute to making healthcare more accessible.

In today’s imaging technology market, solution providers range from large companies innovating in mature markets to disruptive start-ups innovating in targeted areas. Each player is contributing to further enhancements in technology and ultimately to the way medicine is practiced globally.


Figure: Medical imaging equipment landscape – 2019 global spending (Source: Yole)

Traditionally imaging systems were bulky and cost hundreds of thousands of dollars, but developments in technology mean that newer solutions are now less expensive and more compact. Newer designs allow compact equipment to be easily moved around consulting areas and to patient bedsides. Portable handheld devices and laptop-computer-based imaging systems are also growing in popularity. These are bringing imaging scanning closer to patients, meaning people in remote areas or with limited mobility also have access to diagnosis and care.

This evolution and enhanced performance in medical imaging technology is largely being driven by rapid advances in semiconductors – sensors, processors, digital signal processors (DSPs), field programmable gate arrays (FPGAs), analog front ends (AFEs), and other analog and mixed-signal semiconductors. Cloud, artificial intelligence, software-based techniques, and complex 3D and 4D imaging formats are also contributing to unprecedented speed and precision which has greatly improved patient diagnosis and care.

Higher resolution images allow more accurate diagnosis, accelerated data throughputs result in faster diagnostics and minimize patient discomfort, and the use of innovative techniques to produce smaller scanners that dissipate less power for more cost-effective solutions are all advantages made possible by semiconductor technology.

Key elements of a medical imaging systems

Image Acquisition

Image acquisition refers to the capturing of the internal physical profile of the body/area of interest. Varying types of image scanning use different methods to capture these physical profiles and hence can be focused on detecting different physical traits. For example, in digital radiography (DR) or computed tomography (CT), it is the energy of incident photons; in positron emission tomography (PET), it is the photon’s energy and their detection time; in magnetic resonance imaging (MRI), it is the parameters of a radio-frequency signal emitted by the excited atoms; and in ultrasonography, it is the parameters of the acoustic echoes. Despite their differences, most canning methods follow the same process to acquire images. First, the identification of physical quality occurs and is transformed into an electric signal, next the acquired signal is preconditioned, and last, it is digitized – refer to the figure below for further details of the process.


Figure: Image Acquisition Components (Source: Analog Devices)

Image processing

Medical image applications are highly sophisticated, demanding systems. They rely on complex computations and real-time image processing. These systems are continually required to manage higher resolutions, file sizes, and bit depth the more the technology progresses.

To reduce computation time, many often think choosing hardware with high computational power is the best path. Although the processing power of PC central processing units (CPUs) continues to increase, it may not be the optimal choice for the specific application. This is particularly relevant when designing a system that requires low power consumption and high performance.

There are a host of other hardware components that also have a role to play in equipment performance. Hardware accelerators for computer vision and image processing algorithms needed for medical imaging include graphics processing units (GPUs), digital signal processors (DSP), field programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs). The strengths and weaknesses of each are shown in the table below.


Table: Comparing Image Processing Options for Medical Systems

Hardware architectures are continuing to evolve to manage increasing performance demands. Heterogeneous architectures that use various combinations of multicore designs including CPUs, GPUs, DSPs, FPGAs, or small ASICs are growing in popularity. With this comes challenges for both manufacturers and designers in integrating the varying architectures and alleviating the programming difficulties associated with the heterogeneous solutions.

Artificial Intelligence

Artificial Intelligence (AI) is already having a positive and lasting impact on healthcare and medical imaging is no exception. AI algorithms can be applied to decipher and simplify complex data, accelerating and improving the diagnostic process. The value of AI combined with the expertise of radiologists and clinicians is set to revolutionize the field.

Machine learning is used in AI to develop mathematical models that can recognize patterns and make predictions based on statistical models. Deep learning is an area of machine learning based on neural networks with various layers of processing units and is commonly used in computer vision or in audio recognition. Deep learning is literally learning complex patterns in large amounts of data, to provide the best recognition accuracy. This explains why it is used in all algorithms applied to medical imaging – making it easier to diagnose and track treatment progress.


Figure: Alzheimer’s disease detection using artificial intelligence techniques (Source: Subasi 2020)


Medical imaging systems rely heavily on scalable storage and data protection to drive operational and diagnostic imaging workflow performance improvements. Additionally, the high resolution of today’s medical images means that file sizes and volumes have increased exponentially, making storage one of the most significant challenges for medical facilities. According to Dell Technologies, an estimated 600 million medical images were acquired in the US alone in the last year. Such volumes create a heavy load for technology infrastructure and require proactive strategies for effective management. Edge and cloud storage architectures are commonly the best suited for these applications.


The healthcare ecosystem is heavily reliant on digital technology and is often referred to as Digital Health or Health Tech. Valuable patient records, IoT devices, and robotic equipment are all digitally driven with data often stored in the cloud. This is one of the reasons it is now an unprecedented necessity for healthcare facilities to safeguard against potential cyber security breaches.

Attacks could be made on IoT-based equipment such as MRI scanners, insulin pumps, or on the various automatons used in healthcare facilities, with potentially fatal consequences. Baseline security measures should include:

  •  Secure Boot: Guarantee the authenticity of the firmware, protect the Intellectual Property
  •  Authenticity of the data: Guarantee that the data received by the monitor are not modified between the probe and the monitor
  •  Secure Communications Protocol (TLS, IPSec): Offer the capability to securely (encrypted and authenticated) send the data to other devices and systems.


Advancements in technology continue to be a driving force in taking medical imaging to new levels. Image resolution, high precision accuracy, newer compact designs, and portable devices with the use of machine learning, artificial intelligence, and IoT are changing the way healthcare is delivered globally. Through these enhancements, healthcare is being made more accessible and flexible to patient needs, locations, and treatment.

Arrow has a dedicated team with deep knowledge and technical expertise in the healthcare industry. Our technology and advanced portfolio of solutions mean we can help you design, validate, and deliver the next generation of medical imaging devices and healthcare technology. Everything from concept and build to fulfillment, supply chain, and services are all areas we can support you in. Whatever path we take together the privacy and security of patient information is a top priority for us as we deliver solutions for the healthcare industry.


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