UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
Neuroimage. 2019 Feb 1;186:464-475. doi: 10.1016/j.neuroimage.2018.11.023. Epub 2018 Nov 19.
Quantitative proton density (PD) maps measure the amount of free water, which is important for non-invasive tissue characterization in pathology and across lifespan. PD mapping requires the estimation and subsequent removal of factors influencing the signal intensity other than PD. These factors include the T1, T2* relaxation effects, transmit field inhomogeneities, receiver coil sensitivity profile (RP) and the spatially invariant factor that is required to scale the data. While the transmit field can be reliably measured, the RP estimation is usually based on image post-processing techniques due to limitations of its measurement at magnetic fields higher than 1.5 T. The post-processing methods are based on unified bias-field/tissue segmentation, fitting the sensitivity profile from images obtained with different coils, or on the linear relationship between T1 and PD. The scaling factor is derived from the signal within a specific tissue compartment or reference object. However, these approaches for calculating the RP and scaling factor have limitations particularly in severe pathology or over a wide age range, restricting their application. We propose a new approach for PD mapping based on a multi-contrast variable flip angle acquisition protocol and a data-driven estimation method for the RP correction and map scaling. By combining all the multi-contrast data acquired at different echo times, we are able to fully correct the MRI signal for T2* relaxation effects and to decrease the variance and the entropy of PD values within tissue class of the final map. The RP is determined from the corrected data applying a non-parametric bias estimation, and the scaling factor is based on the median intensity of an external calibration object. Finally, we compare the signal intensity and homogeneity of the multi-contrast PD map with the well-established effective PD (PD*) mapping, for which the RP is based on concurrent bias field estimation and tissue classification, and the scaling factor is estimated from the mean white matter signal. The multi-contrast PD values homogeneity and accuracy within the cerebrospinal fluid (CSF) and deep brain structures are increased beyond that obtained using PD* maps. We demonstrate that the multi-contrast RP approach is insensitive to anatomical or a priori tissue information by applying it in a patient with extensive brain abnormalities and for whole body PD mapping in post-mortem foetal imaging.
定量质子密度（PD）图测量自由水的含量，这对于病理学和整个生命周期中的非侵入性组织特征具有重要意义。PD 映射需要估计和随后去除除 PD 以外影响信号强度的因素。这些因素包括 T1、T2弛豫效应、发射场不均匀性、接收线圈灵敏度分布（RP）以及需要缩放数据的空间不变因子。虽然可以可靠地测量发射场，但由于在高于 1.5 T 的磁场中测量的限制，RP 估计通常基于图像后处理技术。后处理方法基于统一的偏置场/组织分割，从不同线圈获得的图像拟合灵敏度分布，或 T1 和 PD 之间的线性关系。缩放因子来自特定组织隔室或参考对象内的信号。然而，这些用于计算 RP 和缩放因子的方法存在局限性，特别是在严重病理或广泛年龄范围内，限制了它们的应用。我们提出了一种基于多对比度可变翻转角采集协议和用于 RP 校正和映射缩放的数据驱动估计方法的 PD 映射新方法。通过组合在不同回波时间获得的所有多对比度数据，我们能够完全校正 MRI 信号的 T2弛豫效应，并降低最终图谱中组织类内 PD 值的方差和熵。从校正数据中应用非参数偏差估计确定 RP，并且缩放因子基于外部校准对象的中位数强度。最后，我们将多对比度 PD 图的信号强度和同质性与基于有效 PD（PD*）映射的信号强度和同质性进行比较，其中 RP 基于并发偏置场估计和组织分类，并且缩放因子从平均白质信号中估计。与使用 PD*图谱获得的图谱相比，CSF 和深部脑结构内的多对比度 PD 值均匀性和准确性得到了提高。我们通过在具有广泛脑异常的患者中应用它并在死后胎儿成像中进行全身 PD 映射来证明多对比度 RP 方法对解剖学或先验组织信息不敏感。
