Multimodal Image Fusion
Multimodal Image Fusion - Related Videos
07:13
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Cited by 3 •
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.
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.
04:10
Robotic-assisted Bronchoscopy Combined with Multimodal Imaging for Targeted Lung Cryobiopsies
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Cited by 1 •
2024
This article aims to describe a stepwise approach to performing robotic-assisted bronchoscopy combined with fluoroscopy, radial endobronchial ultrasound, and cone beam computed tomography to obtain targeted transbronchial lung...
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.