Noise Estimation Category
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...
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...
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...
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...