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.

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

Castiglioni I;Gilardi MC;
2006

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.
2006
Istituto di Bioimmagini e Fisiologia Molecolare - IBFM
978-1-58603-617-1
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/32593
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact