 Signal-To-Noise Ratio
"Signal-To-Noise Ratio" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
The comparison of the quantity of meaningful data to the irrelevant or incorrect data.
| Descriptor ID |
D059629
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| MeSH Number(s) |
E05.318.740.872.875 E05.318.780.800.875 G17.800.500 N05.715.360.750.725.750 N05.715.360.780.700.840 N06.850.520.445.800.875 N06.850.520.830.872.750
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| Concept/Terms |
Signal-To-Noise Ratio- Signal-To-Noise Ratio
- Ratio, Signal-To-Noise
- Ratios, Signal-To-Noise
- Signal To Noise Ratio
- Signal-To-Noise Ratios
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Below are MeSH descriptors whose meaning is more general than "Signal-To-Noise Ratio".
Below are MeSH descriptors whose meaning is more specific than "Signal-To-Noise Ratio".
This graph shows the total number of publications written about "Signal-To-Noise Ratio" by people in this website by year, and whether "Signal-To-Noise Ratio" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 2012 | 0 | 3 | 3 | | 2013 | 1 | 3 | 4 | | 2014 | 0 | 13 | 13 | | 2015 | 0 | 6 | 6 | | 2016 | 0 | 2 | 2 | | 2017 | 0 | 9 | 9 | | 2018 | 1 | 6 | 7 | | 2019 | 0 | 2 | 2 |
To return to the timeline, click here.
Below are the most recent publications written about "Signal-To-Noise Ratio" by people in Profiles.
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Pineda F, Sheth D, Abe H, Medved M, Karczmar GS. Low-dose imaging technique (LITE) MRI: initial experience in breast imaging. Br J Radiol. 2019 Nov; 92(1103):20190302.
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Rajiah P, Ciancibello L, Novak R, Sposato J, Landeras L, Gilkeson R. Ultra-low dose contrast CT pulmonary angiography in oncology patients using a high-pitch helical dual-source technology. Diagn Interv Radiol. 2019 May; 25(3):195-203.
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Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, Summers RM, Giger ML. Deep learning in medical imaging and radiation therapy. Med Phys. 2019 Jan; 46(1):e1-e36.
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Fukui R, Shiraishi J. Application of a pixel-shifted linear interpolation technique for reducing the projection number in tomosynthesis imaging. Radiol Phys Technol. 2019 Mar; 12(1):30-39.
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Deh K, Kawaji K, Bulk M, Van Der Weerd L, Lind E, Spincemaille P, McCabe Gillen K, Van Auderkerke J, Wang Y, Nguyen TD. Multicenter reproducibility of quantitative susceptibility mapping in a gadolinium phantom using MEDI+0 automatic zero referencing. Magn Reson Med. 2019 02; 81(2):1229-1236.
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Chen B, Zhang Z, Xia D, Sidky EY, Pan X. Algorithm-enabled partial-angular-scan configurations for dual-energy CT. Med Phys. 2018 May; 45(5):1857-1870.
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Yang X, De Andrade V, Scullin W, Dyer EL, Kasthuri N, De Carlo F, Gürsoy D. Low-dose x-ray tomography through a deep convolutional neural network. Sci Rep. 2018 02 07; 8(1):2575.
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Pineda FD, Easley TO, Karczmar GS. Dynamic field-of-view imaging to increase temporal resolution in the early phase of contrast media uptake in breast DCE-MRI: A feasibility study. Med Phys. 2018 Mar; 45(3):1050-1058.
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Qiao Z, Redler G, Gui Z, Qian Y, Epel B, Halpern H. Three novel accurate pixel-driven projection methods for 2D CT and 3D EPR imaging. J Xray Sci Technol. 2018; 26(1):83-102.
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Ginat DT, Anthony GJ, Christoforidis G, Oto A, Dalag L, Sammet S. Comparison between whole-body and head and neck neurovascular coils for 3-T magnetic resonance proton resonance frequency shift thermography guidance in the head and neck region. Lasers Med Sci. 2018 Feb; 33(2):369-373.
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