论文标题
LAPGM：多季MR偏置校正和归一化模型
LapGM: A Multisequence MR Bias Correction and Normalization Model
论文作者
论文摘要
为偏置场校正和磁共振归一化问题提出了空间正则化的高斯混合模型LAPGM。提出的空间正常化程序为从业者提供了平衡偏置磁场去除与保存图像对比度之间的微调控制，以提供多序列的磁共振图像。 LAPGM的拟合高斯参数用作控制值，可用于将不同患者扫描的图像强度归一化。将LAPGM与单个和多序列设置中的众所周知的词汇算法N4ITK进行了比较。作为归一化过程，将LAPGM与已知技术（例如：最大归一化，Z得分归一化和水掩盖的利益区域归一化）进行了比较。最后，由作者提供了CUDA加速python软件包$ \ texttt {lapgm} $。
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 $\texttt{lapgm}$ is provided from the authors for use.
