Ultrasound Data Fusion
Ultrasound Data Fusion - Related Videos
09:11
Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
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Cited by 4 •
2019
Presented herein is a protocol to perform targeted biopsy of the prostate using an MRI-ultrasound fusion system.
02:45
Nuclear Fusion
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2020
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons. A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
10:45
A Faster, High Resolution, mtPA-GFP-based Mitochondrial Fusion Assay Acquiring Kinetic Data of Multiple Cells in Parallel Using Confocal Microscopy
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Cited by 20 •
2012
Mitochondrial fusion was measured by tracking the equilibration of photoconverted matrix-targeted GFP across the mitochondrial network over time. Thus far, only one cell could be subjected to an hour long kinetic analysis at a time. We present a method that simultaneously measures multiple cells, thereby speeding up the data collection process.
09:19
Analysis of SNARE-mediated Membrane Fusion Using an Enzymatic Cell Fusion Assay
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2012
We have developed a cell fusion assay that quantifies SNARE-mediated membrane fusion events by activated expression of β-galactosidase.
10:53
A Novel Application of Musculoskeletal Ultrasound Imaging
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Cited by 14 •
2013
We describe a new ultrasound-based vector tissue Doppler imaging technique to measure muscle contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities. This approach provides complementary measurements of dynamic muscle function and could lead to a better understanding of mechanisms underlying musculoskeletal disorders.
FAQs
Registration aligns corresponding anatomical structures across separate ultrasound acquisitions or imaging modes before information is combined. This alignment allows features from B-mode, Doppler, contrast-enhanced, or elastography data to refer to comparable locations rather than unrelated regions. Accurate registration therefore supports more meaningful feature integration and helps produce a representation that is more reliable than poorly aligned measurements.
Each mode captures different information and has its own limitations. B-mode can contribute anatomical appearance, Doppler can add blood-flow information, contrast-enhanced imaging can provide complementary enhancement patterns, and elastography can contribute tissue-mechanical information. Combining these features may support lesion characterization or physiological assessment when a single measurement does not fully describe the relevant tissue.
After alignment and feature extraction, fusion can use image-processing algorithms or statistical algorithms to integrate the available measurements. These approaches may combine complementary signals, reduce noise, or compensate for weaknesses in an individual acquisition. The resulting output is intended to preserve useful anatomical or physiological information while making interpretation more robust than an isolated dataset.
A typical workflow begins by collecting multiple ultrasound images, acquisitions, or imaging modes. The datasets are then registered so corresponding structures align, followed by extraction of relevant complementary features. Image-processing or statistical algorithms combine those features into a unified representation. Researchers or clinicians can then examine the fused result for anatomical, physiological, or motion-related information.
Medical applications include lesion characterization, tissue-motion assessment, and image-guided procedures. Fusion can place complementary anatomical and physiological information into a shared representation, helping users interpret findings that may be incomplete in one acquisition. Its relevance is especially clear when procedural guidance or tissue assessment benefits from combining structural appearance with flow, enhancement, or mechanical information.
By integrating multiple measurements, the approach may reduce the influence of noise and compensate for limitations in any single ultrasound dataset. It can support more complete visualization and more robust interpretation while retaining ultrasound’s real-time and nonionizing advantages. These properties make fusion relevant to clinical decision-making, although the value of the output depends on the quality and alignment of the contributing data.