2017
DOI: 10.18383/j.tom.2017.00019
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The Empirical Effect of Gaussian Noise in Undersampled MRI Reconstruction

Abstract: In Fourier-based medical imaging, sampling below the Nyquist rate results in an underdetermined system, in which a linear reconstruction will exhibit artifacts. Another consequence is lower signal-to-noise ratio (SNR) because of fewer acquired measurements. Even if one could obtain information to perfectly disambiguate the underdetermined system, the reconstructed image could still have lower image quality than a corresponding fully sampled acquisition because of reduced measurement time. The coupled effects o… Show more

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Cited by 16 publications
(14 citation statements)
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“…An undersampled acquisition results in a lower SNR ( Virtue and Lustig, 2017 ) owing to fewer acquired data in each frame. As a consequence, multiple frame accumulation/averaging is required to achieve a high-SNR image which indeed limits the practical imaging speed of the system.…”
Section: Resultsmentioning
confidence: 99%
“…An undersampled acquisition results in a lower SNR ( Virtue and Lustig, 2017 ) owing to fewer acquired data in each frame. As a consequence, multiple frame accumulation/averaging is required to achieve a high-SNR image which indeed limits the practical imaging speed of the system.…”
Section: Resultsmentioning
confidence: 99%
“…The effects on the SNR arising from accelerated imaging are well understood for parallel imaging [29], however, the noise penalty in CS is more complicated for several reasons. First, the spatial distribution of sampling points in a typical CS measurement gives rise to colored noise [30]. Secondly, l1 regularization inherently leads to denoising, the effect of which depends greatly on the chosen regularization parameter and which makes it difficult to quantify SNR in CS reconstructed images.…”
Section: Discussionmentioning
confidence: 99%
“…Then, the undersampled MRI is related to fully-sampled MRI via x LQ = F −1 (F(x HQ ) H). With a fixed sampling mask, accounting for noisy k-space acquisitions, corrupted by complex Gaussian noise (say η K ), that represent OOD-noisy data [41,46,20], the OOD-noisy input sample (say x LQ OOD ) can be modeled by,…”
Section: Methodsmentioning
confidence: 99%
“…In particular, the signalto-noise ratio (SNR) of the MRI k-space data (and consequently the image) depends on various factors such as field strength, pulse sequence, tissue characteristics, number of receiver coils and their sensitivities, scan/physiological parameters, etc. [41,46,30,22]. Hence, for both QE and MP, we generate OOD-noisy MRI data that captures variations in noise levels as described below.…”
Section: Methodsmentioning
confidence: 99%