Abstract
Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.
Highlights
Medical imaging is a set of techniques and methods that noninvasively acquire a variety of images of the body’s internal aspects by means of several effects and interactions with different tissues, revolutionizing modern diagnostic imaging, radiology, and nuclear medicine [1,2]
The authors discussed how modern hardware architectures have advanced to deliver competitive clock rates allowing them to be used in high-capacity imaging equipment
All five types of hardware architectures described have benefited from increased logic density in recent years, along with other features such as integration of dedicated memory and high-speed serial input/output
Summary
Medical imaging is a set of techniques and methods that noninvasively acquire a variety of images of the body’s internal aspects by means of several effects and interactions with different tissues, revolutionizing modern diagnostic imaging, radiology, and nuclear medicine [1,2]. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated software/hardware systems for medical image registration, storage, analysis, and processing [4,5]. They demand complex computations and real-time processing of the images, whose number, size, resolution, and bit depth tend to increase with the evolution of the technology scenario, and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data
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