Image Integration
Image Integration - Related Videos
09:41
A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
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Cited by 19 •
2016
We describe the steps to use our custom designed software for image integration, visualization and planning in epilepsy surgery.
10:01
Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging
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Cited by 8 •
2017
The use of a hyperlens has been regarded as a novel super-resolution imaging technique due to its advantages in real-time imaging and its simple implementation with conventional optics. Here, we present a protocol describing the fabrication and imaging applications of a spherical hyperlens.
13:16
Computed Tomography and Optical Imaging of Osteogenesis-angiogenesis Coupling to Assess Integration of Cranial Bone Autografts and Allografts
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Cited by 7 •
2015
Implantation of autologous and allogeneic bone grafts constitute accepted approaches to treat major craniofacial bone loss. Yet the effect of graft composition on the interplay among neovascularization, cell differentiation and bone formation is unclear. We present a multimodal imaging protocol aimed to elucidate the angiogenesis-osteogenesis interdependence at the graft proximity.
11:38
In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice
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Cited by 7 •
2014
This video illustrates a method, using a clinical 3 T scanner, for contrast-enhanced MR imaging of the naïve mouse visual projection and for repetitive and longitudinal in vivo studies of optic nerve degeneration associated with acute optic nerve crush injury and chronic optic nerve degeneration in knock-out mice (p50KO).
01:26
Integration by Parts: Indefinite Integrals
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2026
Integration by parts is a fundamental technique in calculus for evaluating integrals involving the product of two functions. It is particularly useful when direct integration is not feasible. The method is based on the product rule for differentiation, which states that the derivative of a product equals the derivative of the first function times the second, plus the first function times the derivative of the second. By integrating this identity and rearranging terms, the integration by parts...
FAQs
Image registration establishes correspondence between datasets by aligning them according to shared anatomical features. This step is important when images come from different modalities or examinations, because anatomical structures may not appear in the same position or representation. Accurate alignment provides the spatial basis for later fusion or overlay, allowing clinicians and researchers to compare complementary findings in a coherent anatomical context.
Fusion and overlay are methods used after datasets have been aligned, but they present combined information in different ways. Both aim to preserve complementary structural and functional details from the source images. Their value lies in making relationships between findings easier to interpret, especially when one image contributes anatomical information and another contributes functional or molecular information.
Different imaging sources can contribute different types of evidence about the same anatomy. Structural information helps localize anatomical features, while functional or molecular information adds detail about activity or other biologically relevant findings. Integrating these views supports interpretation of complex medical information by linking complementary observations rather than requiring each dataset to be assessed in isolation.
Images acquired at different time points can be aligned and compared so that changes are interpreted in relation to shared anatomical features. This supports monitoring by helping distinguish findings that persist from those that change between examinations. The resulting comparisons can contribute to longitudinal assessment of disease, particularly when repeated imaging provides complementary information about anatomical or functional status.
A typical workflow begins by bringing together images from different sources, modalities, or time points. Registration then aligns the datasets using shared anatomical features. After alignment, fusion or overlay combines their complementary information into a coherent representation. The integrated result can subsequently support interpretation, quantitative analysis, diagnosis, treatment planning, image-guided procedures, or monitoring.
Medical teams may apply the approach when complex findings require information from more than one imaging source. Supported uses include diagnosis, treatment planning, image-guided procedures, and monitoring disease progression. Researchers can also use integrated datasets for quantitative analysis and multimodal studies, including combinations involving magnetic resonance imaging, computed tomography, ultrasound, and molecular imaging.