High-Level Story: Data Analysis in Multimodal Preclinical Imaging—Methods and Tools

  • Gabriel Tobon
  • Jacob Hesterman
  • Shil Patel
  • Christian LackasEmail author


Preclinical research has long been at the forefront of software and methodological innovation for multimodal imaging. In vivo imaging, ex vivo imaging, and the combination of the two offer a wide variety of complementary image modalities for deriving biological insight. It is increasingly common for research applications to use images from hybrid modality scanners, images from multiple scanners, and derived images in tandem for advanced analysis and quantitation. A long-standing example of the value of multimodal imaging comes from positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging, where it is a common practice to acquire a computed tomography (CT) image for attenuation correction (AC) of the reconstructed signal [1]. It demonstrates how combining image modalities can not only provide complementary information but also enhance the quality and reliability of one or more of the modalities. In the subsequent analysis of PET and SPECT images, it is common for a researcher to quantify the sum or concentration of signal within a region of interest. As “functional” modalities, they may only exhibit contrast to background in regions where the targeted function takes place; however, more regions than those visible may be desired a priori for quantification. The quantitation of such regions greatly benefits from fusing an anatomical modality with a functional one for use in region segmentation.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gabriel Tobon
    • 1
  • Jacob Hesterman
    • 1
  • Shil Patel
    • 2
  • Christian Lackas
    • 1
    Email author
  1. 1.Invicro—A Konica Minolta CompanyBostonUSA
  2. 2.Eisai Inc.AndoverUSA

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