Computer-aided diagnostic reporting of FDG PET for the diagnosis of Alzheimer's disease

被引:3
|
作者
Prestia A. [1 ]
Muscio C. [1 ]
Caroli A. [1 ,2 ]
Frisoni G.B. [1 ]
机构
[1] LENITEM-Laboratory of Epidemiology Neuroimaging and Telemedicine, Unit for the Clinical Translation of Research, IRCCS Centro San Giovanni di Dio FBF, Brescia
[2] Medical Imaging Unit, Biomedical Engineering Department, Mario Negri Institute for Pharmacological Research, Bergamo
关键词
Dementia diagnosis; FDG PET; Global indices; Visual rating;
D O I
10.1007/s40336-013-0031-1
中图分类号
学科分类号
摘要
Temporoparietal hypometabolism on 18F-FDG PET is one of the core biomarkers for the biomarker-based diagnosis of Alzheimer's disease. The traditional readout of 18F-FDG PET for diagnostic reports is a subjective visual rating. However, this is loaded with substantial inter-rater variability. Standardization of readouts is a key factor for the use of markers in the clinic. Automated tools have been developed aimed at aiding diagnostic reporting and making it more reliable. Some involve the statistical voxel-by-voxel comparison of 18F-FDG uptake compared to a normative dataset, which provides a statistical map of difference of metabolism and requires expert judgement. Others provide a summary metric of temporoparietal hypometabolism, where a cutoff defines normality/abnormality. Strengths, weaknesses, and cautionary warnings of visual rating and both classes of tools are reviewed and discussed. © 2013 Italian Association of Nuclear Medicine and Molecular Imaging.
引用
收藏
页码:279 / 288
页数:9
相关论文
共 50 条
  • [1] Brain region ranking for 18FDG-PET computer-aided diagnosis of Alzheimer's disease
    Garali, I.
    Adel, M.
    Bourennane, S.
    Guedj, E.
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2016, 27 : 15 - 23
  • [2] Computer-aided system for the diagnosis of Alzheimer's disease
    Kobashi, S
    Morinaga, N
    Hirano, S
    Kamiura, N
    Hata, Y
    Yamato, K
    ELECTRICAL ENGINEERING IN JAPAN, 1997, 119 (04) : 32 - 41
  • [3] Computer-aided system for the diagnosis of Alzheimer's disease
    Kobashi, Syoji
    Morinaga, Norio
    Hirano, Shoji
    Kamiura, Naotake
    Hata, Yutaka
    Yamato, Kazuharu
    Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), 1997, 119 (04): : 32 - 41
  • [4] Neuroimaging computer-aided diagnosis systems for Alzheimer's disease
    Karami, Vania
    Nittari, Giulio
    Amenta, Francesco
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2019, 29 (01) : 83 - 94
  • [5] Fuzzy Computer-aided Diagnosis of Alzheimer's Disease Using MRI and PET Statistical Features
    Krashenyi, Igor
    Popov, Anton
    Ramirez, Javier
    Manuel Gorriz, Juan
    2016 IEEE 36TH INTERNATIONAL CONFERENCE ON ELECTRONICS AND NANOTECHNOLOGY (ELNANO), 2016, : 187 - 191
  • [6] COMPUTER-AIDED DIAGNOSIS AND REPORTING
    REICHERT.PL
    BIOMETRICS, 1971, 27 (01) : 259 - &
  • [7] Complex wavelet algorithm for computer-aided diagnosis of Alzheimer's disease
    Torrents-Barrena, J.
    Lazar, P.
    Jayapathy, R.
    Rathnam, M. R.
    Mohandhas, B.
    Puig, D.
    ELECTRONICS LETTERS, 2015, 51 (20) : 1566 - 1567
  • [8] Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion
    Pena, Danilo
    Suescun, Jessika
    Schiess, Mya
    Ellmore, Timothy M.
    Giancardo, Luca
    FRONTIERS IN NEUROSCIENCE, 2022, 15
  • [9] Computer-aided diagnosis of Alzheimer’s disease by MRI analysis and evolutionary computing
    de Souza R.G.
    dos Santos Lucas e Silva G.
    dos Santos W.P.
    de Lima M.E.
    Research on Biomedical Engineering, 2021, 37 (3) : 455 - 483
  • [10] Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer's Disease
    Alharbi, Meshal
    Ziyad, Shabana R.
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 74 (03): : 5483 - 5505