IEEE Trans Med Imaging. 2019 Nov;38(11):2676-2686. doi: 10.1109/TMI.2019.2910386. Epub 2019 Apr 11.
A new parameter estimation algorithm, MERLIN, is presented for accurate and robust multi-exponential relaxometry using magnetic resonance imaging, a tool that can provide valuable insight into the tissue microstructure of the brain. Multi-exponential relaxometry is used to analyze the myelin water fraction and can help to detect related diseases. However, the underlying problem is ill-conditioned, and as such, is extremely sensitive to noise and measurement imperfections, which can lead to less precise and more biased parameter estimates. MERLIN is a fully automated, multi-voxel approach that incorporates state-of-the-art l -regularization to enforce sparsity and spatial consistency of the estimated distributions. The proposed method is validated in simulations and in vivo experiments, using a multi-echo gradient-echo (MEGE) sequence at 3 T. MERLIN is compared to the conventional single-voxel l -regularized NNLS (rNNLS) and a multi-voxel extension with spatial priors (rNNLS + SP), where it consistently showed lower root mean squared errors of up to 70 percent for all parameters of interest in these simulations.
提出了一种新的参数估计算法 MERLIN，用于磁共振成像的精确和鲁棒多指数弛豫定量分析，这一工具可以为大脑组织微观结构提供有价值的见解。多指数弛豫定量分析用于分析髓鞘水分数，可以帮助检测相关疾病。然而，基础问题是病态的，因此，对噪声和测量不完美极其敏感，这可能导致参数估计不够精确和更有偏差。MERLIN 是一种全自动的多体素方法，它采用了最先进的 l 正则化方法来强制估计分布的稀疏性和空间一致性。该方法在模拟和体内实验中得到了验证，使用了 3 T 多回波梯度回波（MEGE）序列。MERLIN 与传统的单体素 l 正则化 NNLS（rNNLS）和具有空间先验的多体素扩展（rNNLS + SP）进行了比较，在这些模拟中，它始终表现出更低的均方根误差，所有感兴趣的参数的误差低至 70%。
