Core Concepts
Modeling noise in quantitative MRI using a noncentral chi distribution instead of a Gaussian approximation improves the accuracy of parameter map estimations, particularly for PD- and MT-weighted images.
Bás, K., Lambert, C., & Ashburner, J. (2024). Reconstructing MRI Parameters Using a Noncentral Chi Noise Model. In Medical Image Understanding and Analysis. MIUA 2024. Lecture Notes in Computer Science (Vol. 14860). Springer, Cham. https://doi.org/10.1007/978-3-031-66958-3_13
This research paper investigates the effectiveness of employing a noncentral chi (nc-χ) distribution to model noise in quantitative magnetic resonance imaging (qMRI) for improved parameter map estimation. The authors aim to demonstrate that this approach, based on a more physically plausible noise model, outperforms the conventional Gaussian approximation.