NDLI: Parallelization and performance of 3D ultrasound imaging beamforming algorithms on modern clusters

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Parallelization and performance of 3D ultrasound imaging beamforming algorithms on modern clusters

Content Provider	ACM Digital Library
Author	Plataniotis, K. N. Zhang, F. Stergiopoulos, S. Bilas, A. Abiprojo, R. Dhanantwari, A.
Abstract	Recently there has been a lot of interest in improving the infrastructure used in medical applications. In particular, there is renewed interest on non-invasive, high-resolution diagnostic methods. One such method is digital, 3D ultrasound medical imaging. Current state-of-the-art ultrasound systems use specialized hardware for performing advanced processing of input data to improve the quality of the generated images. Such systems are limited in their capabilities by the underlying computing architecture and they tend to be expensive due to the specialized nature of the solutions they employ.Our goal in this work is twofold: (i) To understand the behavior of this class of emerging medical applications in order to provide an efficient parallel implementation and (ii) to introduce a new benchmark for parallel computer architectures from a novel and important class of applications. We address the limitations faced by modern ultrasound systems by investigating how all processing required by advanced beamforming algorithms can be performed on modern clusters of high-end PCs connected with low-latency, high-bandwidth system area networks. We investigate the computational characteristics of a state-of-the-art algorithm and demonstrate that today's commodity architectures are capable of providing almost-real-time performance without compromising image quality significantly.
Starting Page	294
Ending Page	304
Page Count	11
File Format	PDF
ISBN	1581134835
DOI	10.1145/514191.514232
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2002-06-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Parallel processing Medical applications
Content Type	Text
Resource Type	Article

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