Early diagnosis of alzheimer's disease using a grid implementation of statistical parametric mapping analysis

S. Bagnasco, F. Beltrame, B. Canesi, I. Castiglioni, P. Cerello, S. C. Cheran, M. C. Gilardi, E. Lopez Torres, E. Molinari, A. Schenone, L. Torterolo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A quantitative statistical analysis of perfusional medical images may provide powerful support to the early diagnosis for Alzheimer's Disease (AD). A Statistical Parametric Mapping algorithm (SPM), based on the comparison of the candidate with normal cases, has been validated by the neurological research community to quantify ipometabolic patterns in brain PET/SPECT studies. Since suitable 'normal patient' PET/SPECT images are rare and usually sparse and scattered across hospitals and research institutions, the Data Grid distributed analysis paradigm ('move code rather than input data') is well suited for implementing a remote statistical analysis use case, described in the present paper. Different Grid environments (LCG, AliEn) and their services have been used to implement the above-described use case and tackle the challenging problems related to the SPM-based early AD diagnosis.

Original languageEnglish
Title of host publicationStudies in Health Technology and Informatics
Pages69-81
Number of pages13
Volume120
Publication statusPublished - 2006

Keywords

  • Alzheimer's disease
  • distributed databases
  • grid computing
  • statistical analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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