The relentless pace of innovation in biomedical imaging has provided modern researchers with an unprecedented number of techniques and tools to choose from. While the development of new imaging techniques is vital for ongoing progress in the life sciences, it is challenging for researchers to keep pace. Imaging Modalities for Biological and Preclinical Research is designed to provide a comprehensive overview of currently available biological and preclinical imaging methods, including their benefits and limitations. Experts in the field guide the reader through both the physical principles and biomedical applications of each imaging modality, including description of typical setups and sample preparation.

PrefacePart II: In vivo imagingII.1 - Intravital microscopyII.2 - Optical coherence tomography (OCT)II.3 - Photoacoustic imagingII.4 - Optical imagingII.4.a FluorescenceII.4.b BioluminescenceII.4.c Cerenkov luminescence imagingII.5 - Confocal and multiphoton endomicroscopyII.6 - Preclinical in vivo ultrasound imaging II.7 - Magnetic resonance imaging II.7.a Nuclear magnetic resonance spectroscopy and imagingII.7.b Functional magnetic resonance imagingII.7.c Hyperpolarized 13C magnetic resonance spectroscopic imagingII.8 - Magnetic particle imagingII.9 - Computed tomography in biomedicineII.10 - Positron emission tomography (PET)II.11 - Single photon emission computed tomography (SPECT) Part III: Correlative multimodal imaging and image data fusion III.1 - Biological correlative imagingIII.1.a Correlative light electron microscopy (CLEM)III.1.b Correlative atomic force microscopyIII.2 - (Pre)clinical hybrid imagingIII.2.a PET/CT imagingIII.2.b PET/SPECT/CTIII.2.c Positron emission tomography/magnetic resonance imagingIII.2.d Combined fluorescence molecular tomography and computed tomography (FMT/CT) to track and quantify systemically applied NIR-dye labeled nucleic acidIII.2.e Hybrid PET-CT-ultrasound imagingIII.3 - Correlative multimodality imaging across scales III.4 - Multimodal image processing III.4.a Introduction-computer-assisted analysis of multimodality image dataIII.4.b Multimodality image registrationIII.4.c Learning-based approaches for multimodality imagingIII.4.d Multimodality image segmentationIII.4.e Visualization for correlative multimodality imagingIII.4.f Data compression algorithms for biomedical images III.4.g Perspectives and conclusion-computer-assisted analysis of multimodality image dataPart IV: Future of Imaging in Biological and Preclinical ResearchIV.1 Emerging technologies and outlook