MEDICS:用于医学图像重建的超便携处理

Ganesh S. Dasika, Ankit Sethia, Vincentius Robby, T. Mudge, S. Mahlke
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引用次数: 11

摘要

医学成像为医生提供了生成人体3D图像的能力,以便检测和诊断各种疾病。使医学成像便于携带和更容易获得提供了一系列独特的挑战。为了提高可移植性,图像采集所消耗的功率——目前成像设备中最耗电的活动——必须大幅降低。然而,这只能通过使用复杂的图像重建算法来纠正低功耗采集带来的伪影,从而导致图像处理成为主要的功耗任务。目前的解决方案使用数字信号处理器、通用处理器以及最近用于医学图像处理的通用图形处理单元的组合。这些解决方案由于各种原因而不足,包括高功耗和无法执行下一代图像重建算法。本文介绍了MEDICS体系结构——一个专门为医学成像应用设计的领域特定的多核体系结构,但具有足够的通用性,使其可编程。目标是实现100 GFLOPs的性能,同时消耗比现有解决方案少几个数量级的功率。MEDICS的吞吐量为128 GFLOPs,而在高级CT重建应用中功耗仅为1.6W。这意味着与当前设计相比,计算效率提高了20倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MEDICS: Ultra-portable processing for medical image reconstruction
Medical imaging provides physicians with the ability to generate 3D images of the human body in order to detect and diagnose a wide variety of ailments. Making medical imaging portable and more accessible provides a unique set of challenges. In order to increase portability, the power consumed in image acquisition - currently the most power-consuming activity in an imaging device - must be dramatically reduced. This can only be done, however, by using complex image reconstruction algorithms to correct artifacts introduced by low-power acquisition, resulting in image processing becoming the dominant power-consuming task. Current solutions use combinations of digital signal processors, general-purpose processors and, more recently, general-purpose graphics processing units for medical image processing. These solutions fall short for various reasons including high power consumption and an inability to execute the next generation of image reconstruction algorithms. This paper presents the MEDICS architecture - a domain-specific multicore architecture designed specifically for medical imaging applications, but with sufficient generality tomake it programmable. The goal is to achieve 100 GFLOPs of performance while consuming orders of magnitude less power than the existing solutions. MEDICS has a throughput of 128 GFLOPs while consuming as little as 1.6W of power on advanced CT reconstruction applications. This represents up to a 20X increase in computation efficiency over current designs.
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