Institute of Biostructures and Bioimaging, Italian National Research Council, Naples, Italy; Department of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy.
Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
Comput Methods Programs Biomed. 2022 Aug;223:106957. doi: 10.1016/j.cmpb.2022.106957. Epub 2022 Jun 15.
Relaxation parameter maps (RPMs) calculated from spin-echo data have provided a basis for the segmentation of normal brain tissues and white matter lesions in multiple sclerosis (MS) MRI studies. However, Conventional Spin-Echo (CSE) sequences, once the core of clinical MRI studies, have been largely replaced by faster ones, which do not allow the calculation a-posteriori of RPMs from clinical studies. Aim of the study was to develop and validate a method to estimate RPMs (pseudo-RPMs) from routine clinical MRI protocols (including 3D-Gradient Echo T1w, FLAIR and fast-T2w sequences), suitable for fully automatic multiparametric segmentation of normal-appearing and pathological brain tissues in MS.
The proposed method processes spatially normalized clinical MRI studies through a multistep pipeline, to collect a set of data points of matched signal intensities (from MRI studies) and relaxation parameters (from a CSE-derived digital template and an MS lesion database), which are then fitted by a multiple and multivariate 4-th degree polynomial regression, providing pseudo-RPMs. The method was applied to a dataset of 59 clinical MRI studies providing pseudo-RPMs that were segmented through a method originally developed for the CSE-derived RPMs. Results of the segmentation in 12 studies were used to iteratively optimize method parameters. Accuracy of segmentation of normal-appearing brain tissues from the pseudo-RPMs was assessed by comparing their age-related changes, as measured in 47 clinical studies, against those measured acquired using CSE sequences in a comparable dataset of 47 patients. Lesion segmentation was validated against manual segmentation carried out by three neuroradiologists.
Age-related changes of normal-appearing brain tissue volumes measured using the pseudo-RPMs substantially overlapped those measured using the RPMs obtained from CSE sequences, and segmentation of MS lesions showed a moderate-high spatial overlap with manual segmentation, comparable to that achieved by the widely used Lesion Segmentation Tool on FLAIR images, with a greater volumetric agreement.
The proposed approach allows calculation from clinical studies of pseudo-RPMs, which are equivalent to those obtainable from CSE sequences, avoiding the need for the acquisition of additional, dedicated sequences for segmentation purposes.
从自旋回波数据计算出的弛豫参数图（RPM）为磁共振成像（MRI）研究中多发性硬化症（MS）正常脑组织和白质病变的分割提供了基础。然而，传统自旋回波（CSE）序列曾经是临床 MRI 研究的核心，现已在很大程度上被更快的序列所取代，这些序列不允许从临床研究中事后计算 RPM。本研究旨在开发和验证一种从常规临床 MRI 协议（包括 3D 梯度回波 T1w、FLAIR 和快速 T2w 序列）估算 RPM（伪 RPM）的方法，适用于 MS 中正常和病理性脑组织的全自动多参数分割。
该方法通过一个多步骤流水线处理空间归一化的临床 MRI 研究，以收集一组匹配的信号强度（来自 MRI 研究）和弛豫参数（来自 CSE 衍生的数字模板和 MS 病变数据库）的数据点，然后通过多元四次多项式回归进行拟合，提供伪 RPM。该方法应用于 59 项临床 MRI 研究数据集，提供了通过最初为 CSE 衍生 RPM 开发的方法进行分割的伪 RPM。对 12 项研究的分割结果进行迭代优化方法参数。通过比较 47 项临床研究中测量的年龄相关性变化，评估伪 RPM 分割正常脑组织的准确性，这些数据与在 47 名患者的可比数据集的 CSE 序列中获得的年龄相关性变化进行比较。通过三位神经放射学家进行的手动分割验证病变分割的准确性。
使用伪 RPM 测量的正常脑组织体积的年龄相关性变化与使用从 CSE 序列获得的 RPM 测量的变化基本重叠，并且 MS 病变的分割与手动分割具有中度高的空间重叠，与广泛使用的基于 FLAIR 图像的病变分割工具相当，具有更大的体积一致性。
该方法允许从临床研究中计算伪 RPM，这些 RPM 与从 CSE 序列获得的 RPM 等效，避免了为分割目的而采集额外专用序列的需要。
