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How FPGAs boost medical imaging

Posted: 29 Nov 2011 ?? ?Print Version ?Bookmark and Share

Keywords:Medical imaging? Optical coherence tomography? OCT?

The advancements in medical research rely heavily on the technology that supports it. Medical imaging in particular plays a huge role in the entire clinical processfrom diagnostics and treatment to surgery and research. In addition to the hurdles medical professionals encounter, "seeing" (in the medical sense) is one of the biggest challenges. Diseases are difficult to spot because they tend to hide deep inside the body. Techniques that enable clinicians to noninvasively see these areas are critical to ensuring progress in this field.

Optical coherence tomography (OCT) is a promising diagnostic tool that could have applications in many different medical fields. The technology takes advantage of the latest computing hardware architectures and is used to create medical instruments that can detect cancer and other conditions in a safe, simple, and effective manner.

How it works?
This noninvasive imaging technique provides subsurface, cross-sectional images of materials. Interest in OCT technology has grown significantly because it provides much greater resolution than other imaging techniques such as magnetic resonance imaging (MRI) or positron emission tomography (PET). In addition, OCT requires relatively little preparation by medical staff and is safe for patients because it uses low laser outputs without the need for ionizing radiation.

To create images, OCT uses a low-power light source and the corresponding light reflections. It measures light in a way that is similar to how ultrasound machines measure sound. When the light beam is projected into a sample, much of the light is scattered. A small amount reflects as a collimated beam, which can be detected and used to create a very detailed image.

Computing elements
Field-programmable gate arrays (FPGAs) enable design flexibility, helping designers explore new ideas and reduce risk in the system development process. This capability is important in the medical space because it is critical to get to market quickly. Traditionally, demonstrating hardware-based processing required a custom application-specific integrated circuit (ASIC), but ASIC development is expensive and functionality is fixed.

FPGAs are reconfigurable through software. This advantage enables a designer to save development time by demonstrating hardware-based processing while preserving the option of reprogramming the FPGA to accommodate modifications that are required after initial specification. Although FPGA board designs can be complex and modular, off-the-shelf FPGA boards provide hardware to build around with infrastructure components for I/O connectivity, bus interfacing, and DRAM communication. Developing these components in-house can be time consuming and distracting.

FPGAs have rapidly grown in popularity for medical applications. With regard to medical imaging, FPGAs are primarily used in detection and image construction. The detection application involves embedded systems, with real-time performance requirements and significant hardware interface challenges. Image reconstruction, on the other hand, is similar to a high-performance computing problem.

The use of GPUs has also ramped up significantly for scientific research. One popular parallel computing architecture, called compute unified device architecture (CUDA), is used to accelerate a simulation program called Amber. The molecular dynamics simulation program is used by more than 60,000 researchers in academia and pharmaceutical companies worldwide to accelerate new drug discovery.

Neuroimaging is another one of the many medical fields directly benefitting from the computational power that GPUs provide. Using state-of-the-art medical imaging acquisition devices with advanced brain imaging techniques requires the ability to process extensive analysis and simulations with high-resolution images. By using powerful hardware devices such as GPUs, researchers can reduce the time for these large simulations, enabling faster deductions and even larger simulations to be performed.

To understand why GPUs are being adopted for these types of data-intensive applications, it's important to consider the history of GPU hardware and the architectural design of modern GPUs. As the name implies, GPU hardware was originally designed to provide enhanced PC graphics capabilities. Early graphics hardware featured fixed-functionality logic for vertex processing and pixel operations.

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