Baselice Fabio, Ferraioli Giampaolo, Pascazio Vito
Dipartimento di Ingegneria, Università di Napoli Parthenope, Italy.
Dipartimento di Scienze e Tecnologie, Università di Napoli Parthenope, Italy.
Magn Reson Imaging. 2016 Apr;34(3):312-25. doi: 10.1016/j.mri.2015.10.020. Epub 2015 Nov 17.
Relaxation time estimation in MRI field can be helpful in clinical diagnosis. In particular, T1 and T2 changes can be related to tissues modification, being an effective tool for detecting the presence of several pathologies and measure their development, thus their estimation is a useful research field. Currently, most techniques work pixel-wise, and transfer the noise reduction task to post processing filters. A novel method for estimating spin-spin and spin-lattice relaxation times is proposed. The approach exploits Markov Random Field theory for modeling the unknown data and implements an a posteriori estimator in the Bayesian framework. The effect is the joint parameters estimation and noise reduction. Proposed methodology, with respect to already existing techniques, is able to provide effective results while preserving details also in case of few acquisitions or severe signal to noise ratio. The algorithm has been tested on both simulated and real datasets.
磁共振成像（MRI）领域中的弛豫时间估计对临床诊断可能会有所帮助。特别是，T1和T2的变化可能与组织改变有关，是检测多种病变的存在并测量其发展的有效工具，因此对它们的估计是一个有用的研究领域。目前，大多数技术都是逐像素工作的，并将降噪任务交给后处理滤波器。本文提出了一种估计自旋 - 自旋和自旋 - 晶格弛豫时间的新方法。该方法利用马尔可夫随机场理论对未知数据进行建模，并在贝叶斯框架中实现后验估计器。其效果是联合参数估计和降噪。相对于现有技术，所提出的方法能够在采集数据较少或信噪比很低的情况下，在保留细节的同时提供有效的结果。该算法已在模拟数据集和真实数据集上进行了测试。
