A spatially regularized Gaussian mixture model, LapGM, is proposed for the bias field correction and magnetic resonance normalization problem. The proposed spatial regularizer gives practitioners fine-tuned control between balancing bias field removal and preserving image contrast preservation for multi-sequence, magnetic resonance images. The fitted Gaussian parameters of LapGM serve as control values which can be used to normalize image intensities across different patient scans. LapGM is compared to well-known debiasing algorithm N4ITK in both the single and multi-sequence setting. As a normalization procedure, LapGM is compared to known techniques such as: max normalization, Z-score normalization, and a water-masked region-of-interest normalization. Lastly a CUDA-accelerated Python package lapgm is provided from the authors for use.
@article{arxiv.2209.13619,
title = {LapGM: A Multisequence MR Bias Correction and Normalization Model},
author = {Luciano Vinas and Arash A. Amini and Jade Fischer and Atchar Sudhyadhom},
journal= {arXiv preprint arXiv:2209.13619},
year = {2022}
}