Preclinical research utilizing animal models is a crucial part of biomedical science, serving to investigate and validate new biomarkers, imaging agents, and therapeutic interventions prior to clinical trial initiation. An extensive variety of animal models is available, effectively replicating various human diseases, including cancer, cardiovascular, neurological, and inflammatory conditions. Nuclear medicine molecular imaging offers three-dimensional, functional visualization of biological activity within living organisms through the use of radioactive tracers, revealing how these processes are spatially distributed in both animal models and human bodies. The present chapter deals with the potentials offered by the wide range of available scanners for pre-clinical molecular imaging discussing PET and SPECT/CT imaging, PET/MRI as well as optical imaging. Although these systems share some features with their clinical counterparts, significant adjustments are necessary to appropriately scale down clinical imaging technologies for preclinical applications. Preclinical PET scanners must achieve a much higher spatial resolution than clinical systems, given the considerably smaller dimensions of anatomical structures in small animals. Additionally, preclinical systems necessitate superior temporal resolution in order to register physiological processes with greater precision. Specific chapters are dedicated to preclinical imaging acquisitions for evaluation of drug delivery systems, targeting and biodistribution assessment, monitoring drug activity and interaction, evaluation of drug delivery systems, targeting and biodistribution, evaluation of therapeutic efficacy, theranostics. A large part of the chapter will be thus dedicated to preclinical imaging acquisition in oncology but non-oncological applications will be also addressed. Data regarding the application of Artificial Intelligence to preclinical imaging analysis are also summarized.

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State of the Art in Pre-clinical Imaging

  • Guido Rovera,
  • Gianluca Destro,
  • Edoardo Dighero,
  • Martina Cioffi,
  • Maria Luce Mangia,
  • Alessandra Agosti,
  • Gloria Garelli,
  • Matteo Bauckneht,
  • Gianmario Sambuceti,
  • Enzo Terreno,
  • Silvia Morbelli

摘要

Preclinical research utilizing animal models is a crucial part of biomedical science, serving to investigate and validate new biomarkers, imaging agents, and therapeutic interventions prior to clinical trial initiation. An extensive variety of animal models is available, effectively replicating various human diseases, including cancer, cardiovascular, neurological, and inflammatory conditions. Nuclear medicine molecular imaging offers three-dimensional, functional visualization of biological activity within living organisms through the use of radioactive tracers, revealing how these processes are spatially distributed in both animal models and human bodies. The present chapter deals with the potentials offered by the wide range of available scanners for pre-clinical molecular imaging discussing PET and SPECT/CT imaging, PET/MRI as well as optical imaging. Although these systems share some features with their clinical counterparts, significant adjustments are necessary to appropriately scale down clinical imaging technologies for preclinical applications. Preclinical PET scanners must achieve a much higher spatial resolution than clinical systems, given the considerably smaller dimensions of anatomical structures in small animals. Additionally, preclinical systems necessitate superior temporal resolution in order to register physiological processes with greater precision. Specific chapters are dedicated to preclinical imaging acquisitions for evaluation of drug delivery systems, targeting and biodistribution assessment, monitoring drug activity and interaction, evaluation of drug delivery systems, targeting and biodistribution, evaluation of therapeutic efficacy, theranostics. A large part of the chapter will be thus dedicated to preclinical imaging acquisition in oncology but non-oncological applications will be also addressed. Data regarding the application of Artificial Intelligence to preclinical imaging analysis are also summarized.