Hybrid Imaging Modality
Hybrid Imaging Modality - Related Videos
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 34 •
2015
We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.
13:51
Cross-Modal Multivariate Pattern Analysis
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Cited by 5 •
2011
Classical multivariate pattern analysis predicts sensory stimuli a subject perceives from neural activity in the corresponding cortices (e.g. visual stimuli from activity in visual cortex). Here, we apply pattern analysis cross-modally and show that sound- and touch-implying visual stimuli can be predicted from activity in auditory and somatosensory cortices, respectively.
07:13
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Cited by 1 •
2023
The combination of multiple imaging modalities is often necessary to gain a comprehensive understanding of pathophysiology. This approach utilizes phantoms to generate a differential transformation between the coordinate systems of two modalities, which is then applied for co-registration. This method eliminates the need for fiducials in production scans.
06:51
Dual-modality Molecular Cartography: Integrating Multiplex mRNA Detection with Protein Imaging Mass Cytometry
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2025
This protocol details a methodology for multiplex mRNA detection with protein imaging mass cytometry in formalin fixed paraffin embedded tissue sections.
12:23
Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound
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Cited by 16 •
2012
In the pathogenesis of bone metastasis, angiogenesis is a crucial process and therefore represents a target for imaging and therapy. Here, we present a rat model of site-specific breast cancer bone metastasis and describe strategies to non-invasively image angiogenesis in vivo using magnetic resonance imaging, volumetric computed tomography and ultrasound.
FAQs
Spatial registration aligns datasets acquired through different imaging techniques so that corresponding anatomical or functional features occupy consistent locations. Calibration supports this alignment by helping account for differences between acquisition systems. Without accurate coordination, the fused result may place complementary information incorrectly, reducing visualization quality and weakening quantitative interpretation or engineering validation.
Image reconstruction converts acquired signals into usable image data, while computational fusion combines the separately reconstructed datasets into a unified representation. These stages preserve information generated by different physical principles and make it possible to interpret structural, anatomical, or functional features together. Their performance directly affects visualization and quantitative analysis.
Performance depends on coordinated hardware, precise spatial alignment, calibration, image reconstruction, and computational fusion. Acquisition speed also matters because faster systems can support more efficient examinations and data handling. Reliable integration across these components determines whether the combined output provides consistent information for biomedical research, diagnosis, treatment planning, or technology development.
A single technique may emphasize only one type of information, whereas combining techniques allows complementary anatomical, structural, or functional features to be examined together. This broader representation can improve visualization and support quantitative analysis. The resulting information may help users make more informed decisions in research, diagnosis, treatment planning, and system development.
A typical workflow begins by coordinating hardware and acquiring signals with the selected imaging techniques. The datasets then undergo calibration and spatial registration, followed by image reconstruction and computational fusion. The integrated output can subsequently be examined for visualization or quantitative analysis. Each stage must preserve correspondence between the separately acquired information sources.
Applications include biomedical research, clinical diagnosis, treatment planning, and imaging technology development. In these settings, the combined datasets can support improved visualization, quantitative analysis, and more informed decision-making. Engineering contributions focus on building coordinated systems, maintaining reliable data integration, and developing processing methods that make the combined information usable.
Future progress depends on faster acquisition, reliable integration of multiple datasets, and increasingly intelligent image-processing methods. These developments can improve how quickly information is collected, aligned, reconstructed, and fused. For engineering, the challenge is to advance the full imaging pipeline while preserving the accuracy and interpretability needed for research and biomedical decision-making.