Extraction of Input Function from Rat [18F]FDG PET Images

被引:8
|
作者
Kudomi, Nobuyuki [1 ,2 ]
Bucci, Marco [1 ]
Oikonen, Vesa [1 ]
Silvennoinen, Mika [3 ]
Kainulainen, Heikki [3 ]
Nuutila, Pirjo [1 ,4 ]
Iozzo, Patricia [1 ,5 ]
Roivainen, Anne [1 ,6 ]
机构
[1] Univ Turku, Turku PET Ctr, Turku, Finland
[2] Kagawa Univ, Fac Med, Dept Med Phys, Kagawa, Japan
[3] Univ Jyvaskyla, Dept Biol Phys Activ, SF-40100 Jyvaskyla, Finland
[4] Univ Turku, Dept Med, Turku, Finland
[5] CNR, Inst Clin Physiol, Pisa, Italy
[6] Univ Turku, Turku Ctr Dis Modeling, Turku, Finland
基金
芬兰科学院;
关键词
Small animal; F-18]FDG; PET; Input function; BRAIN TRANSFER CONSTANTS; SMALL-ANIMAL PET; GRAPHICAL EVALUATION; METABOLIC-RATE; IN-VIVO; GLUCOSE; MODEL; FDG;
D O I
10.1007/s11307-010-0449-z
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose: Small animal positron emission tomography (PET) with 2-deoxy-2-[F-18]fluoro-d-glucose ([F-18]FDG) facilitates the visualization and quantification of glucose uptake in rats and mice. The quantification of glucose uptake requires an input function, which is generally obtained by measuring radioactivity in arterial plasma withdrawn during PET imaging; however, this approach is not always feasible because abundant blood sampling may affect the physiological process being measured. The purpose of the present study was to develop a new model-based technique (K-Model) and compare it to the previous F-Model. Materials and Methods: The study material consisted of two separate groups of rats having different physiological conditions. Each group was scanned by different PET cameras, i.e., HRRT and Inveon-PET/CT, and blood samples were drawn during imaging. Two kinds of model functions, i.e., F-Model and K-Model, were used for estimating input functions by an optimization procedure, applying restrictions on boundary conditions. To validate the method, glucose influx rate, K (i), was computed from the estimated and measured input functions for comparison. Results: The input functions were well reproduced when single-point blood count data were used for both models. The difference in K (i) values between the model-based and blood sampling methods was 1.1 +/- 15.1% by K-Model which showed the most feasible in the study. The regression analysis showed a tight correlation between the image-based and blood sampling methods, and the slope was close to unity and the intercept close to zero. Conclusion: It is possible to estimate the input function from rat [F-18]FDG PET images, thus facilitating the assessment of glucose metabolism without affecting the physiological conditions of the animal as a result of abundant blood sampling.
引用
收藏
页码:1241 / 1249
页数:9
相关论文
共 50 条
  • [41] Non-invasive estimation of hepatic glucose uptake from [18F]FDG PET images using tissue-derived input functions
    N. Kudomi
    M. J. Järvisalo
    J. Kiss
    R. Borra
    A. Viljanen
    T. Viljanen
    T. Savunen
    J. Knuuti
    H. Iida
    P. Nuutila
    P. Iozzo
    European Journal of Nuclear Medicine and Molecular Imaging, 2009, 36 : 2014 - 2026
  • [42] Non-invasive estimation of hepatic glucose uptake from [18F]FDG PET images using tissue-derived input functions
    Kudomi, N.
    Jarvisalo, M. J.
    Kiss, J.
    Borra, R.
    Viljanen, A.
    Viljanen, T.
    Savunen, T.
    Knuuti, J.
    Iida, H.
    Nuutila, P.
    Iozzo, P.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2009, 36 (12) : 2014 - 2026
  • [43] Correlation between Kidney Uptake at [18F]FDG PET/CT and Renal Function
    Dondi, Francesco
    Pisani, Antonio Rosario
    Lucarelli, Nicola Maria
    Gazzilli, Maria
    Talin, Anna
    Albano, Domenico
    Rubini, Dino
    Maggialetti, Nicola
    Rubini, Giuseppe
    Bertagna, Francesco
    JOURNAL OF PERSONALIZED MEDICINE, 2024, 14 (01):
  • [44] Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network
    John Ly
    David Minarik
    Jonas Jögi
    Per Wollmer
    Elin Trägårdh
    EJNMMI Research, 11
  • [45] Development of a database of realistic simulated whole body [18F]FDG PET images for lymphoma
    Tomei, Sandrine
    Reilhac, Anthonin
    Visvikis, Dimitris
    Odet, Christophe
    Giammarile, Francesco
    Mognetti, Thomas
    Lartizien, Carole
    2008 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (2008 NSS/MIC), VOLS 1-9, 2009, : 4224 - +
  • [46] Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network
    Ly, John
    Minarik, David
    Jogi, Jonas
    Wollmer, Per
    Tragardh, Elin
    EJNMMI RESEARCH, 2021, 11 (01)
  • [47] Quantification of patient's tumor burden for the differentiation of lymphomas with [18F]FDG PET images
    Constantino, C. S.
    Leocadio, S.
    Oliveira, F. P. M.
    Silva, M.
    Oliveira, C.
    Castanheira, J. C.
    Silva, A.
    Vaz, S.
    Teixeira, R.
    Neves, M.
    Lucio, P.
    Joao, C.
    Costa, D. C.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2022, 49 (SUPPL 1) : S410 - S411
  • [48] The Incremental Value of 18F FDG Labelled Leukocytes PET/CT Over 18F FDG PET/CT Scan in the Detection of Occult Infection
    Manda, Divya
    Thakral, Parul
    Sen, Ishita
    Das, Subha
    Virupakshappa, C. B.
    Pant, Vineet
    JOURNAL OF NUCLEAR MEDICINE, 2021, 62
  • [49] The Incremental Value of 18F FDG Labelled Leukocytes PET CT Over 18F FDG PET CT Scan in the Detection of Occult Infection
    Manda, D.
    Sen, I.
    Thakral, P.
    Malik, D.
    Das, S. S.
    Cb, V.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2022, 49 (SUPPL 1) : S88 - S88
  • [50] Whole-Body [18F]FDG-PET/MRI vs. [18F]FDG-PET/CT in Malignant Melanoma
    Berzaczy, Dominik
    Fueger, Barbara
    Hoeller, Christoph
    Haug, Alexander R.
    Staudenherz, Anton
    Berzaczy, Gundula
    Weber, Michael
    Mayerhoefer, Marius E.
    MOLECULAR IMAGING AND BIOLOGY, 2020, 22 (03) : 739 - 744