Noise Estimation Category
Distortion correction of functional MRI without reverse phase encoding scans or field maps
Aug. 31, 2023—Tian Yu, Leon Y. Cai, Salvatore Torrisi, An Thanh Vu, Victoria L. Morgan, Sarah E. Goodale, Karthik Ramadass, Steven L. Meisler, Jinglei Lv, Aaron E.L. Warren, Dario J. Englot, Laurie Cutting, Catie Chang, John C. Gore, Bennett A. Landman, Kurt G. Schilling Paper: https://www.sciencedirect.com/science/article/pii/S0730725X23001121 Code: https://github.com/MASILab/SynBOLD-DisCo Abstract Functional magnetic resonance images (fMRI) acquired using echo planar sequences...
Denoising of diffusion MRI in the cervical spinal cord – effects of denoising strategy and acquisition on intra-cord contrast, signal modeling, and feature conspicuity
Aug. 31, 2023—Kurt Schilling, Shreyas Fadnavis, Joshua Batson, Mereze Visagie, Anna JE Combes, Colin D Mcknight, Francesca Bagnato, Eleftherios Garyfallidis, Bennett A Landman, Seth A Smith, Kristin P O’Grady Paper: https://pubmed.ncbi.nlm.nih.gov/36543265/ Abstract Quantitative diffusion MRI (dMRI) is a promising technique for evaluating the spinal cord in health and disease. However, low signal-to-noise ratio (SNR) can impede interpretation and...
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...