Alzheimer’s disease markers from structural MRI and FDG-PET brain images

被引:0
|
作者
Andrea Chincarini
Paolo Bosco
Gianluca Gemme
Silvia Morbelli
Dario Arnaldi
Francesco Sensi
Ilaria Solano
Nicola Amoroso
Sabina Tangaro
Renata Longo
Sandro Squarcia
Flavio Nobili
机构
[1] Sezione di Genova,INFN
[2] Dip. di Medicina Interna e Specialità Mediche Università degli Studi di Genova,Medicina Nucleare
[3] Università degli Studi di Genova,Dipartimento di Fisica
[4] Università degli Studi di Bari,Dipartimento Interateneo di Fisica “M. Merlin” and TIRES
[5] Sezione di Trieste,INFN
[6] Università degli Studi di Trieste,Dipartimento di Fisica
[7] Azienda Ospedale-Università S. Martino,Neurofisiologia Clinica, Dipartimento di Neuroscienze, Oftalmologia e Genetica
[8] Genova,undefined
关键词
Mild Cognitive Impairment; Default Mode Network; Mild Cognitive Impairment Patient; Structural Magnetic Resonance Imaging; Mild Cognitive Impairment Subject;
D O I
暂无
中图分类号
学科分类号
摘要
Despite the widespread use of neuroimaging tools (morphological and functional) in the routine diagnostic of cerebral diseases, the information available by the end user -the clinician- remains largely limited to qualitative visual analysis. This restriction greatly reduces the diagnostic impact of neuroimaging in routine clinical practice and increases the risk of misdiagnosis. In this context, researches are focussing on the development of sophisticated automatic analyses able to extract clinically relevant information from the captured data. The identification of biological markers at early stages of Alzheimer’s disease (AD) contributes to diagnostic accuracy and adds prognostic value. However, in spite of recent developments, results of structural and functional imaging studies on predicting conversion to AD are not uniform. We provide here an overview of analysis methods and approaches, discussing their contribution to clinical assessment.
引用
收藏
相关论文
共 50 条
  • [1] Alzheimer's disease markers from structural MRI and FDG-PET brain images
    Chincarini, Andrea
    Bosco, Paolo
    Gemme, Gianluca
    Morbelli, Silvia
    Arnaldi, Dario
    Sensi, Francesco
    Solano, Ilaria
    Amoroso, Nicola
    Tangaro, Sabina
    Longo, Renata
    Squarcia, Sandro
    Nobili, Flavio
    EUROPEAN PHYSICAL JOURNAL PLUS, 2012, 127 (11):
  • [2] Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease
    Pan, Xiaoxi
    Adel, Mouloud
    Fossati, Caroline
    Gaidon, Thierry
    Guedj, Eric
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2019, 23 (04) : 1499 - 1506
  • [3] Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer's dementia score
    Yee, Evangeline
    Popuri, Karteek
    Beg, Mirza Faisal
    HUMAN BRAIN MAPPING, 2020, 41 (01) : 5 - 16
  • [4] ALZHEIMER'S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES
    Pan, Xiaoxi
    Adel, Mouloud
    Fossati, Caroline
    Gaidon, Thierry
    Guedj, Eric
    2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2018, : 366 - 370
  • [5] Generative FDG-PET and MRI Model of Aging and Disease Progression in Alzheimer's Disease
    Dukart, Juergen
    Kherif, Ferath
    Mueller, Karsten
    Adaszewski, Stanislaw
    Schroeter, Matthias L.
    Frackowiak, Richard S. J.
    Draganski, Bogdan
    PLOS COMPUTATIONAL BIOLOGY, 2013, 9 (04)
  • [6] Classification of Alzheimer's Disease from FDG-PET images using Favourite Class Ensembles
    Cabral, Carlos
    Silveira, Margarida
    2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2013, : 2477 - 2480
  • [7] The concept of FDG-PET endophenotype in Alzheimer's disease
    During, Emmanuel H.
    Osorio, R. S.
    Elahi, F. M.
    Mosconi, L.
    de Leon, M. J.
    NEUROLOGICAL SCIENCES, 2011, 32 (04) : 559 - 569
  • [8] The concept of FDG-PET endophenotype in Alzheimer’s disease
    Emmanuel H. During
    R. S. Osorio
    F. M. Elahi
    L. Mosconi
    M. J. de Leon
    Neurological Sciences, 2011, 32 : 559 - 569
  • [9] Deep learning based diagnosis of Alzheimer's disease using FDG-PET images
    Kishore, Nand
    Goel, Neelam
    NEUROSCIENCE LETTERS, 2023, 817
  • [10] Brain FDG-PET in the diagnosis of Alzheimer's disease and frontotemporal dementia in the clinical setting
    Rubi, S.
    Tarongi, S.
    Vasquez, D.
    Garcia, A.
    Vico, H.
    Noguera, A.
    Sampol, C.
    Gimenez, M.
    Mas, A.
    Picado, M. J.
    Pena, C.
    Amer, G.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2013, 40 : S340 - S340