Multimodal Image Fusion
Multimodal Image Fusion - Related Videos
12:38
4D Multimodality Imaging of Citrobacter rodentium Infections in Mice
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Cited by 19 •
2013
Multi-modality imaging is a valuable approach for studying bacterial colonization in small animal models. This protocol outlines infection of mice with bioluminescent Citrobacter rodentium and the longitudinal monitoring of bacterial colonization using composite 3D diffuse light imaging tomography with μCT imaging to create a 4D movie of C. rodentium infection.
06:10
Live Imaging of Mouse Secondary Palate Fusion
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Cited by 11 •
2017
Here, we present a protocol for live imaging of mouse secondary palate fusion using confocal microscopy. This protocol can be used in combination with a variety of fluorescent reporter mouse lines, and with pathway inhibitors for mechanistic insight. This protocol can be adapted for live imaging in other developmental systems.
11:28
Biofunctionalized Prussian Blue Nanoparticles for Multimodal Molecular Imaging Applications
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Cited by 17 •
2015
This protocol describes the synthesis of biofunctionalized Prussian blue nanoparticles and their use as multimodal, molecular imaging agents. The nanoparticles have a core-shell design where gadolinium or manganese ions within the nanoparticle core generate MRI contrast. The biofunctional shell contains fluorophores for fluorescence imaging and targeting ligands for molecular targeting.
09:43
In vivo Near Infrared Fluorescence (NIRF) Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis
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Cited by 32 •
2011
We detail a new near-infrared fluorescence (NIRF) catheter for 2-dimensional intravascular molecular imaging of plaque biology in vivo. The NIRF catheter can visualize key biological processes such as inflammation by reporting on the presence of plaque-avid activatable and targeted NIR fluorochromes. The catheter utilizes clinical engineering and power requirements and is targeted for application in human coronary arteries. The following research study describes a multimodal imaging strategy...
10:25
Multimodal Imaging of Stem Cell Implantation in the Central Nervous System of Mice
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Cited by 11 •
2012
This article describes an optimized sequence of events for multimodal imaging of cellular grafts in rodent brain using: (i) in vivo bioluminescence and magnetic resonance imaging, and (ii) post mortem histological analysis. Combining these imaging modalities on a single animal allows cellular graft evaluation with high resolution, sensitivity and specificity.
FAQs
Weighted intensity methods combine image values directly, whereas multiscale transforms merge information across image scales. Deep-learning models learn integration patterns computationally. The appropriate choice depends on the information that must be preserved and how the fused representation will support medical interpretation, rather than on the mere availability of multiple image sources.
Accurate spatial registration places corresponding anatomy in the same locations before information is integrated. If alignment is poor, structural and functional signals may be associated with the wrong tissue, reducing the reliability of the fused image. This makes registration a foundational quality condition for lesion localization, tissue characterization, and image-guided intervention.
Pixel-level integration combines image information close to the original intensity data. Feature-level integration first uses selected image characteristics, while decision-level integration combines outputs from separate analyses. These levels represent different points in the processing pathway, so the choice affects what information is retained and how the final representation supports clinical or research interpretation.
Acquisition quality, spatial registration, fusion strategy, and validation against clinical needs all influence reliability. Poor source images can limit the information available for integration, while misalignment can distort anatomical relationships. Even a technically successful result requires validation to determine whether the fused representation actually supports the intended medical task.
A typical workflow begins with images acquired using different modalities or settings, followed by spatial registration to align corresponding anatomy. The aligned data are then integrated at the pixel, feature, or decision level using a selected fusion method. Finally, the result is assessed against the clinical or research purpose for which it was created.
Combining CT, MRI, or PET can connect structural detail with biochemical or functional signals. This combined view may support lesion localization and tissue characterization, while also contributing to treatment planning and image-guided intervention. Its value depends on whether the integrated information answers a relevant clinical question and whether the result has been appropriately validated.