Self-calibration, histological validation, and an improved signal model for χ-separation using single-subject (N=1) physics-constrained deep learning.


Conference paper


Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F
Joint Workshop on MR phase, magnetic susceptibility and electrical properties mapping, Lucca, Italy, 2022

Cite

Cite

APA
I, B., G, G., S, H., T, J., R, Z., & F, S. (2022). Self-calibration, histological validation, and an improved signal model for χ-separation using single-subject (N=1) physics-constrained deep learning. . Lucca, Italy.

Chicago/Turabian
I, Benslimane, Grabner G, Hametner S, Jochmann T, Zivadinov R, and Schweser F. “Self-Calibration, Histological Validation, and an Improved Signal Model for χ-Separation Using Single-Subject (N=1) Physics-Constrained Deep Learning. .” Joint Workshop on MR phase, magnetic susceptibility and electrical properties mapping. Lucca, Italy, 2022.

MLA
I, Benslimane, et al. Self-Calibration, Histological Validation, and an Improved Signal Model for χ-Separation Using Single-Subject (N=1) Physics-Constrained Deep Learning. . 2022.