Department of Physics, University of Windsor, 401 Sunset Avenue, Windsor, Canada.
Department of Physics, University of Windsor, 401 Sunset Avenue, Windsor, Canada.
J Magn Reson. 2021 Apr;325:106930. doi: 10.1016/j.jmr.2021.106930. Epub 2021 Feb 9.
Quantitative analysis of magnetic resonance signal lifetimes could reveal molecular scale information. However, it is non-trivial to recover the relaxation times from MR experiments in the multi-component exponential decay analysis. Constraints are required for the ill-posed problem in conventional inversion methods, which could lead to biased solutions. Artificial neural networks (ANNs) are a series of densely connected information processing nodes which cumulatively map a set of inputs to a set of outputs. They have proven to be universal approximators and powerful tools for solving complex nonlinear problems. In this work, ANNs were trained to recover T relaxation times. Both the discrete T spectrum and continuous T distribution were considered. Increased accuracy was achieved compared to the traditional methods. The continuous spectrum peak widths, generally not reliable in the traditional approach, could be determined accurately with ANN when the signal-to-noise ratio permitted.
磁共振信号寿命的定量分析可以揭示分子尺度的信息。然而，在多分量指数衰减分析中，从磁共振实验中恢复弛豫时间并非易事。传统反演方法中的不适定问题需要约束条件，这可能导致有偏解。人工神经网络（ANNs）是一系列密集连接的信息处理节点，它们将一组输入累积映射到一组输出。它们已被证明是通用逼近器和解决复杂非线性问题的有力工具。在这项工作中，训练 ANN 以恢复 T 弛豫时间。离散 T 谱和连续 T 分布都被考虑在内。与传统方法相比，精度得到了提高。当信噪比允许时，ANN 可以准确确定连续谱峰宽，而传统方法通常不可靠。
