Kinetic analysis of functional images: The case for a practical approach to performance prediction

F. Munz, T. Ludwig, S. Ziegler, P. Bartenstein, M. Schwaiger, A. Bode

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present the first parallel medical application for the analysis of dynamic positron emission tomography (PET) images together with a practical performance model. The parallel application may improve the diagnosis for a patient (e. g. in epilepsy surgery) because it enables the fast computation of parametric images on a pixel level as opposed to the traditionally used region of interest (ROI) approach which is applied to determine an average parametric value for a particular anatomic region of the brain. We derive the performance model from the application context and show its relation to abstract machine models. We demonstrate the accuracy of the model to predict the runtime of the application on a network of workstations and use it to determine an optimal value in the message frequency-size relationship.

Original languageEnglish
Title of host publicationHigh Performance Computing - 2nd International Symposium, ISHPC 1999, Proceedings
EditorsKazuki Joe, Akira Fukuda, Constantine Polychronopoulos, Shinji Tomita
PublisherSpringer Verlag
Pages169-180
Number of pages12
ISBN (Print)3540659692, 9783540659693
DOIs
StatePublished - 1999
Event2nd International Symposium on High Performance Computing, ISHPC 1999 - Kyoto, Japan
Duration: 26 May 199928 May 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1615
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Symposium on High Performance Computing, ISHPC 1999
Country/TerritoryJapan
CityKyoto
Period26/05/9928/05/99

Keywords

  • BSP
  • Functional imaging
  • Kinetic modeling
  • LogP
  • Network of workstations
  • PPM
  • Practical performance prediction

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