Gibbs Ringing Correction
Gibbs Ringing Correction - Related Videos
02:39
Gibbs Free Energy
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2020
One of the challenges of using the second law of thermodynamics to determine if a process is spontaneous is that it requires measurements of the entropy change for the system and the entropy change for the surroundings. An alternative approach involving a new thermodynamic property defined in terms of system properties only was introduced in the late nineteenth century by American mathematician Josiah Willard Gibbs. This new property is called the Gibbs free energy (G) (or simply the free...
01:29
The Reaction Gibbs Energy
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2026
The reaction Gibbs energy (ΔrG) is a crucial parameter that determines whether a reaction will occur spontaneously or not. It can be used to categorize reactions into two types: exergonic and endergonic.Exergonic reactions are those in which ΔrG is less than zero. This implies that these reactions can occur spontaneously without an external input of energy. In biological systems, a typical example of an exergonic reaction is the oxidation of carbohydrates. This reaction produces simple...
02:23
Gibbs Free Energy and Thermodynamic Favorability
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2023
The spontaneity of a process depends upon the temperature of the system. Phase transitions, for example, will proceed spontaneously in one direction or the other depending upon the temperature of the substance in question. Likewise, some chemical reactions can also exhibit temperature-dependent spontaneities. To illustrate this concept, the equation relating free energy change to the enthalpy and entropy changes for the process is considered: The spontaneity of a process, as reflected in the...
01:15
Distance Corrections
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2025
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
08:00
Directed Cellular Self-Assembly to Fabricate Cell-Derived Tissue Rings for Biomechanical Analysis and Tissue Engineering
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Cited by 39 •
2011
This article outlines a versatile method to create cell-derived tissue rings by cellular self-assembly. Smooth muscle cells seeded into ring-shaped agarose wells aggregate and contract to form robust three-dimensional (3D) tissues within 7 days. Millimeter-scale tissue rings are conducive to mechanical testing and serve as building blocks for tissue assembly.
FAQs
Sharp boundaries contain high spatial-frequency information, and finite k-space sampling truncates part of that information. During Fourier reconstruction, the incomplete frequency representation produces alternating overshoot and undershoot around the edge. The visible ripple is therefore a sampling and reconstruction effect concentrated near intensity transitions, rather than necessarily a genuine anatomical signal. Recognizing this helps distinguish artifact from structure.
A useful correction must reduce oscillatory intensity patterns while retaining genuine anatomical detail. Its central challenge is separating the edge-related ripple from the underlying signal across neighboring voxels or within an estimated edge profile. If the method models that local transition accurately, suppression can target the artifact rather than flattening the tissue boundary, which is important for downstream measurements.
Neighboring-voxel approaches estimate the underlying signal by examining local intensity information around the affected boundary. Edge-profile approaches instead model the shape of the transition itself before suppressing the oscillation. Both strategies address the same reconstruction artifact, but they use different representations of the local signal. Their shared objective is to reduce ripples without removing genuine anatomical boundaries.
A general workflow first estimates the underlying signal at an affected edge, using either neighboring voxels or a modeled edge profile. It then suppresses the oscillatory pattern while aiming to retain the genuine boundary. The resulting image can be inspected for improved edge visualization before being used for segmentation, cortical measurements, or other quantitative analysis.
Applications span structural, functional, and diffusion MRI, so the correction is not limited to one neuroscience imaging contrast. In structural scans, it can clarify tissue boundaries; across these MRI applications, reducing misleading edge intensities supports visualization and analysis. The overview specifically connects this improvement with segmentation, cortical measurements, and quantitative assessment.
Near tissue interfaces, oscillatory intensities can mimic or obscure local anatomical patterns. Correcting them helps reduce misleading evidence in images used to examine brain structure and function. This matters when boundaries influence segmentation or cortical measurements, because the goal is to base those outputs on the underlying signal rather than reconstruction-induced ripples.