scholarly journals Signal-to-noise ratio assessment of muscle diffusion tensor imaging using single image set and validation by the difference image method

2019 ◽  
Vol 92 (1102) ◽  
pp. 20190133 ◽  
Author(s):  
Zhiyue J. Wang ◽  
Jin Yamamura ◽  
Sarah Keller
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Signal To Noise Ratio ◽  
Image Method ◽  
Single Image ◽  
Signal To Noise ◽  
Difference Image ◽  
Thigh Muscles ◽  
Image Set ◽  
The Difference

Objective: Signal-to-noise ratio (SNR) assessment is essential for accurate quantification of diffusion tensor imaging (DTI) metrics and usually requires the use of a difference image method using duplicate images. We aimed to estimate the SNR of DTI of thigh muscles using a single image set without duplicate images. Methods: DTI of one thigh were acquired on a 3 T scanner from 15 healthy adults, and scans with number of signal averages (NSA) = 4 and 8 were repeatedly acquired. SNR were evaluated for six thigh muscles. For SNR calculation from a single image set, diffusion-weighted images with similar diffusion encoding directions were grouped into pairs. The difference image of each pair was high-pass filtered in k-space to yield noise images. Noise images were also calculated with a difference method using two image sets as a reference. Subjects were divided into two groups for filter optimization and validation, respectively. The coefficient of repeatability (CR) of the SNR obtained from the two methods was also evaluated separately. Results: Bland–Altman analysis comparing the single image set method and the reference showed 95% limits of agreement of −9.2 to 9.2% for the optimization group and −12.5 to 12.6% for the validation group. The SNR measurement had a CR of 21.1% using the reference method, and 13.8% using the single image set method. Conclusion: The single image method can be used for DTI SNR assessment and offers better repeatability. Advances in knowledge: SNR of skeletal muscle DTI can be assessed for any data set without duplicate images.

scholarly journals Signal to noise ratio and uncertainty in diffusion tensor imaging at 1.5, 3.0, and 7.0 Tesla

2011 ◽  
Vol 33 (6) ◽  
pp. 1456-1463 ◽  
Author(s):  
Daniel L. Polders ◽  
Alexander Leemans ◽  
Jeroen Hendrikse ◽  
Manus J. Donahue ◽  
Peter R. Luijten ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

Bias in estimates of mb at small magnitudes

1976 ◽  
Vol 66 (6) ◽  
pp. 1887-1904
Author(s):  
J. F. Evernden ◽  
W. M. Kohler
Keyword(s):  
Signal To Noise Ratio ◽  
High Signal ◽  
Systematic Bias ◽  
Station Correction ◽  
Signal To Noise ◽  
Identification Criterion ◽  
The Difference ◽  
Operational Networks ◽  
Worldwide Network ◽  
Simple Parameter

abstract A possibly significant factor in application of an identification criterion such as MS:mb is systematic bias in mb magnitude estimates at small magnitudes due to a variety of factors. Magnitude bias is the difference in magnitude value, positive or negative, between an observed network-based magnitude value and the expected magnitude value if all stations of the network had detected the event at high signal-to-noise ratio. This paper constitutes a partial study of the general problem; it evaluates the bias effects expected from both conceptual and operational networks when using parameters for noise and signal levels and standard deviations derived from observations, and when correcting observed station mb values solely via a simple parameter station correction factor. The analysis shows that any bias effects on mb inherent in any operational or potential worldwide network are so small as to have negligible effect on use of an MS:mb discriminant.

Compression of Secondary Ion Microscopy Image Sets Using a Three-dimensional Wavelet Transformation

2000 ◽  
Vol 6 (1) ◽  
pp. 68-75 ◽  
Author(s):  
Martin G. Wolkenstein ◽  
Herbert Hutter
Keyword(s):  
Image Compression ◽  
Wavelet Transformation ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Compression Method ◽  
Signal To Noise ◽  
Microscopy Image ◽  
Image Set ◽  
Ion Microscopy ◽  
Secondary Ion

This article proposes a lossy three-dimensional (3-D) image compression method for 3-D secondary ion microscopy (SIMS) image sets that uses a separable nonuniform 3-D wavelet transform. A typical 3-D SIMS measurement produces relatively large amounts of data which has to be reduced for archivation purposes. Although it is possible to compress an image set slice by slice, more efficient compression can be achieved by exploring the correlation between slices. Compared to different two-dimensional (2-D) image compression methods, compression ratios of the 3-D wavelet method are about four times higher at a comparable peak signal-to-noise ratio (PSNR).

In Vivo Diffusion Tensor Imaging and Tractography of Human Thigh Muscles in Healthy Subjects

2010 ◽  
Vol 195 (5) ◽  
pp. W352-W356 ◽  
Author(s):  
Erwan Kermarrec ◽  
Jean-François Budzik ◽  
Chadi Khalil ◽  
Vianney Le Thuc ◽  
Caroline Hancart-Destee ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Healthy Subjects ◽  
Diffusion Tensor ◽  
Thigh Muscles

Compression of Secondary Ion Microscopy Image Sets Using a Three-dimensional Wavelet Transformation

2000 ◽  
Vol 6 (1) ◽  
pp. 68-75
Author(s):  
Martin G. Wolkenstein ◽  
Herbert Hutter
Keyword(s):  
Image Compression ◽  
Wavelet Transformation ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Compression Method ◽  
Signal To Noise ◽  
Microscopy Image ◽  
Image Set ◽  
Ion Microscopy ◽  
Secondary Ion

Abstract This article proposes a lossy three-dimensional (3-D) image compression method for 3-D secondary ion microscopy (SIMS) image sets that uses a separable nonuniform 3-D wavelet transform. A typical 3-D SIMS measurement produces relatively large amounts of data which has to be reduced for archivation purposes. Although it is possible to compress an image set slice by slice, more efficient compression can be achieved by exploring the correlation between slices. Compared to different two-dimensional (2-D) image compression methods, compression ratios of the 3-D wavelet method are about four times higher at a comparable peak signal-to-noise ratio (PSNR).

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