Data_Sheet_1_Noise-Corrected, Exponentially Weighted, Diffusion-Weighted MRI (niceDWI) Improves Image Signal Uniformity in Whole-Body Imaging of Metastatic Prostate Cancer.PDF

Purpose: To characterize the voxel-wise uncertainties of Apparent Diffusion Coefficient (ADC) estimation from whole-body diffusion-weighted imaging (WBDWI). This enables the calculation of a new parametric map based on estimates of ADC and ADC uncertainty to improve WBDWI imaging standardization and interpretation: NoIse-Corrected Exponentially-weighted diffusion-weighted MRI (niceDWI).
Methods: Three approaches to the joint modeling of voxel-wise ADC and ADC uncertainty (σADC) are evaluated: (i) direct weighted least squares (DWLS), (ii) iterative linear-weighted least-squares (IWLS), and (iii) smoothed IWLS (SIWLS). The statistical properties of these approaches in terms of ADC/σADC accuracy and precision is compared using Monte Carlo simulations. Our proposed post-processing methodology (niceDWI) is evaluated using an ice-water phantom, by comparing the contrast-to-noise ratio (CNR) with conventional exponentially-weighted DWI. We present the clinical feasibility of niceDWI in a pilot cohort of 16 patients with metastatic prostate cancer.
Results: The statistical properties of ADC and σADC conformed closely to the theoretical predictions for DWLS, IWLS, and SIWLS fitting routines (a minor bias in parameter estimation is observed with DWLS). Ice-water phantom experiments demonstrated that a range of CNR could be generated using the niceDWI approach, and could improve CNR compared to conventional methods. We successfully implemented the niceDWI technique in our patient cohort, which visually improved the in-plane bias field compared with conventional WBDWI.
Conclusions: Measurement of the statistical uncertainty in ADC estimation provides a practical way to standardize WBDWI across different scanners, by providing quantitative image signals that improve its reliability. Our proposed method can overcome inter-scanner and intra-scanner WBDWI signal variations that can confound image interpretation.

目的：表征全身弥散加权成像（whole-body diffusion-weighted imaging，WBDWI）中表观扩散系数（Apparent Diffusion Coefficient，ADC）估计的逐体素不确定性。基于ADC及其不确定性的估计值，可构建全新参数图，以优化全身弥散加权成像的成像标准化与图像解读流程，该方法即为噪声校正指数加权弥散加权磁共振成像（Noise-Corrected Exponentially-weighted diffusion-weighted MRI，niceDWI）。
方法：本研究评估了三种用于逐体素ADC与ADC不确定性（σADC）联合建模的方法：(1) 直接加权最小二乘法（Direct Weighted Least Squares，DWLS）、(2) 迭代线性加权最小二乘法（Iterative Linear-weighted Least-squares，IWLS）以及(3) 平滑化IWLS（Smoothed IWLS，SIWLS）。通过蒙特卡洛模拟（Monte Carlo simulations），对比这三种方法在ADC/σADC的准确性与精准性方面的统计学特性。本研究采用冰水体模，通过对比噪声比（Contrast-to-Noise Ratio，CNR）与传统指数加权弥散加权成像的差异，对所提出的后处理方法niceDWI进行评估；同时纳入16例转移性前列腺癌患者的先导队列，验证niceDWI的临床可行性。
结果：ADC与σADC的统计学特性与DWLS、IWLS及SIWLS三种拟合流程的理论预测结果高度吻合，仅DWLS在参数估计中存在轻微偏倚。冰水体模实验表明，niceDWI方法可生成一系列不同水平的CNR，且相较于传统方法可提升CNR。本研究在患者队列中成功应用niceDWI技术，与传统全身弥散加权成像相比，其平面内偏倚场在视觉上得到了改善。
结论：对ADC估计中的统计不确定性进行量化，可通过提供可提升成像可靠性的定量图像信号，为不同扫描仪间的全身弥散加权成像标准化提供一种切实可行的途径。本研究提出的方法可克服扫描仪间与扫描仪内的全身弥散加权成像信号差异，避免这些差异干扰图像解读。