Young Ian R, Szeverenyi Nikolaus M, Du Jiang, Bydder Graeme M
Formerly Department of Electrical Engineering, Imperial College of Science, Technology, and Medicine, London, UK.
Department of Radiology, University of California San Diego, San Diego, USA.
Quant Imaging Med Surg. 2020 May;10(5):1080-1120. doi: 10.21037/qims.2020.04.07.
This paper describes a quantitative approach to understanding the signal, contrast and weighting of magnetic resonance (MR) images. It uses the concept of pulse sequences as tissue property (TP) filters and models the signal, contrast and weighting of sequences using either a single TP-filter (univariate model) or several TP-filters (the multivariate model). For the spin echo (SE) sequence using the Bloch equations, voxel signal intensity is plotted against the logarithm of the value of the TPs contributing to the sequence signal to produce three TP-filters, an exponential ρ-filter, a low pass T-filter and a high pass T-filter. Using the univariate model which considers signal changes in only one of ρ, T, or T at a time, the first partial derivative of signal with respect to the natural logarithm of ρ, T or T is the sequence weighting for each filter (for small changes in each TP). Absolute contrast is then the sequence weighting multiplied by the fractional change in TP for each filter. For large changes in TPs, the same approach is followed, but using the mean slope of the filter as the sequence weighting. These approaches can also be used for fractional contrast. The univariate TP-filter model provides a mathematical framework for converting conventional qualitative univariate weighting as used in everyday clinical practice into quantitative univariate weighting. Using the multivariate model which considers several TP-filters together, the relative contributions of each TP to overall sequence and image weighting are expressed as sequence and imaging weighting ratios respectively. This is not possible with conventional qualitative weighting which is univariate. The same approaches are used for inversion recovery (IR), pulsed gradient SE, spoiled gradient echo (SGE), balanced steady state free precession, ultrashort echo time and other pulse sequences. Other TPs such as susceptibility, chemical shift and flow can be included with phase along the Y axis of the TP-filter. Contrast agent effects are also included. In the text TP-filters are distinguished from k-space filters, signal filters (S-filters) which are used in imaging processing as well as to describe windowing the signal width and level of images, and spatial filters. The TP-filters approach resolves many of the ambiguities and inconsistencies associated with conventional qualitative weighting and provides a variety of new insights into the signal, contrast and weighting of MR images which are not apparent using qualitative weighting. The TP-filter approach relates the preparation component of pulse sequences to voxel signal, and contrast between two voxels. This is complementary to k-space which relates the acquisition component of pulse sequences to the spatial properties of MR images and their global contrast.
本文描述了一种用于理解磁共振（MR）图像信号、对比度和权重的定量方法。它将脉冲序列的概念用作组织特性（TP）滤波器，并使用单个TP滤波器（单变量模型）或多个TP滤波器（多变量模型）对序列的信号、对比度和权重进行建模。对于使用布洛赫方程的自旋回波（SE）序列，将体素信号强度相对于对序列信号有贡献的TP值的对数进行绘制，以生成三个TP滤波器，即指数ρ滤波器、低通T滤波器和高通T滤波器。使用一次仅考虑ρ、T或T其中之一信号变化的单变量模型，信号相对于ρ、T或T的自然对数的一阶偏导数就是每个滤波器的序列权重（对于每个TP的小变化）。然后，绝对对比度是序列权重乘以每个滤波器中TP的分数变化。对于TP的大变化，采用相同的方法，但使用滤波器的平均斜率作为序列权重。这些方法也可用于分数对比度。单变量TP滤波器模型提供了一个数学框架，用于将日常临床实践中使用的传统定性单变量权重转换为定量单变量权重。使用一起考虑多个TP滤波器的多变量模型，每个TP对整体序列和图像权重的相对贡献分别表示为序列权重比和成像权重比。这对于传统的单变量定性权重是不可能的。相同的方法用于反转恢复（IR）、脉冲梯度SE、扰相梯度回波（SGE）、稳态自由进动平衡、超短回波时间和其他脉冲序列。其他TP，如磁化率、化学位移和流动，可以与沿TP滤波器Y轴的相位一起包含在内。造影剂效应也包括在内。在本文中，TP滤波器与k空间滤波器、用于图像处理以及描述图像信号宽度和电平的窗口化的信号滤波器（S滤波器）以及空间滤波器区分开来。TP滤波器方法解决了许多与传统定性权重相关的模糊性和不一致性问题，并为MR图像的信号、对比度和权重提供了许多使用定性权重时不明显的新见解。TP滤波器方法将脉冲序列的准备部分与体素信号以及两个体素之间的对比度联系起来。这与k空间互补，k空间将脉冲序列的采集部分与MR图像的空间特性及其全局对比度联系起来。
