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[4] [5] [6] Other key features that may be seen include wall thickening and ring-down artifacts known as "comet tails" (produced by reverberations of sound between the sinuses). [4] [5] [6] Ultrasound can also distinguish between diffuse, segmental, and localized variants of adenomyomatosis based on morphology. [5] [6]
In electronic filters, the trade-off between frequency domain response and time domain ringing artifacts is well-illustrated by the Butterworth filter: the frequency response of a Butterworth filter slopes down linearly on the log scale, with a first-order filter having slope of −6 dB per octave, a second-order filter –12 dB per octave, and ...
Such an artifact may be called a statistical artifact. For instance, imagine a hypothetical finding that presidential approval rating is approximately equal to twice the percentage of citizens making more than $50,000 annually; if 60% of citizens make more than $50,000 annually, this would predict that the approval rating will be 120%.
Within surface science, a quartz crystal microbalance with dissipation monitoring (QCM-D) is a type of quartz crystal microbalance (QCM) based on the ring-down technique. It is used in interfacial acoustic sensing. Its most common application is the determination of a film thickness in a liquid environment (such as the thickness of an adsorbed ...
Time gain compensation (TGC) is a setting applied in diagnostic ultrasound imaging to account for tissue attenuation. [1] By increasing the received signal intensity with depth, the artifacts in the uniformity of a B-mode image intensity are reduced.
"It's sort of like an ultrasound for ice sheets, where we're mapping out the bottom of the ice sheet," Chad Greene, the cryospheric scientist who took the picture, told Business Insider.
Ghosting is a multidimensional artifact that occurs in the MRI in the phase-encoded direction (short axis of the image) after applying the Fourier transform. When the phase of the magnetic resonance signal is being encoded into the 2D or 3D Fourier image, a mild deviation from the actual phase and amplitude may occur.
Traditionally these artifacts were removed with slow iterative methods like total variation minimization, but the advent of deep learning approaches has opened a new avenue that utilizes a priori knowledge from network training to remove artifacts. In the deep learning methods that seek to remove these sparse sampling, limited-bandwidth, and ...