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Medical-image Analysis and Statistical Interpretation Lab

Noise Estimation Category

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Learning Implicit Brain MRI Manifolds with Deep Learning

Dec. 22, 2017—Bermudez, C., Plassard, A.J., Davis, T.L., Newton, A.T., Resnick, S.M., and Landman, B.A. (2017) “Learning implicit brain MRI manifolds with deep learning.” arXiv preprint arXiv:1801.01847 Full Text: https://arxiv.org/pdf/1801.01847.pdf Abstract An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease...

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Figure 3 Kurtosis of weighted residuals. The Kurtosis maps of the residuals from the weighted data are shown in the first column. The middle and the right column display one voxel residuals across the first 100 scans inside the cerebrospinal fluid region and the white matter region respectively.

A Comparison of Distributional Considerations with Statistical Analysis of Resting State fMRI at 3T and 7T

Feb. 1, 2012—Xue Yang, Martha J. Holmes, Allen T. Newton, Victoria L. Morgan, Bennett A. Landman. “A Comparison of Distributional Considerations with Statistical Analysis of Resting State fMRI at 3T and 7T.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) NIHMS341654† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=A+Comparison+of+Distributional+Considerations+with+Statistical+Analysis+of+Resting+State+fMRI+at+3T+and+7T Abstract Ultra-high field 7T magnetic resonance...

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Simulated impact of thresholds on bias in SVNF estimation. 5,000 Monte Carlo iterations were performed for each of 10 Rician distributed observations and for each of 40 linearly spaced SNRs between 1:2 and 20:1. For each dataset, the SVNF was estimated using Eq. (7) with a threshold of 0:1 (no threshold), 3:1, 5:1, and by Eq. (8) (data-adaptive threshold). Second, 5,000 Monte Carlo draws of 1 Rayleigh distributed observation (i.e., an “outlier” with a Rician distribution with SNR=0:1) and 9 Rician distributed observations for each of 40 linearly spaced SNRs between 1:2 and 20:1. For each dataset, the SVNF was estimated using Eq. (7) with a threshold of 0:1 (no threshold), 3:1, 5:1, and by Eq. (8) (data-adaptive threshold). The standard error on all reported estimates is less than 0.005 (for clarity, error bars are not shown).

Robust Estimation of Spatially Variable Noise Fields

Aug. 31, 2009—B. A. Landman, P-L Bazin, S. A. Smith, and J. L. Prince, “Robust Estimation of Spatially Variable Noise Fields”, Magnetic Resonance in Medicine, Aug;62(2):500-9. 2009 PMC2806192 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2806192/ Abstract Consideration of spatially variable noise fields is becoming increasing necessary in magnetic resonance imaging given recent innovations in artifact identification and statistically-driven image processing. Fast...

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Simulation results. In the idealized simulation (left), the background estimator (BE) method performed better than other methods both in terms of mean squared error (first row) and variability (second row). In the simulation incorporating spatial variability and artifacts (right), the proposed spatially regularized robust (SRR) method greatly exceeded the performance of the standard approaches.

Estimation and application of spatially variable noise fields in diffusion tensor imaging.

Feb. 28, 2009—B. A. Landman, P-L Bazin, and J. L. Prince, “Estimation and Application of Spatially Variable Noise Fields in Diffusion Tensor Imaging”, Magnetic Resonance Imaging, Volume 27, Issue 6, Pages 741-751. (2009) PMC2733233 Full text: https://www.ncbi.nlm.nih.gov/pubmed/19250784 Abstract Optimal interpretation of magnetic resonance image content often requires an estimate of the underlying image noise, which is typically...

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