IEEE J Biomed Health Inform. 2022 Jul;26(7):3151-3162. doi: 10.1109/JBHI.2022.3156009. Epub 2022 Jul 1.
The Apparent Diffusion Coefficient (ADC) is considered an importantimaging biomarker contributing to the assessment of tissue microstructure and pathophy- siology. It is calculated from Diffusion-Weighted Magnetic Resonance Imaging (DWI) by means of a diffusion model, usually without considering any motion during image acquisition. We propose a method to improve the computation of the ADC by coping jointly with both motion artifacts in whole-body DWI (through group-wise registration) and possible instrumental noise in the diffusion model. The proposed deformable registration method yielded on average the lowest ADC reconstruction error on data with simulated motion and diffusion. Moreover, our approach was applied on whole-body diffusion weighted images obtained with five different b-values from a cohort of 38 patients with histologically confirmed lymphomas of three different types (Hodgkin, diffuse large B-cell lymphoma and follicular lymphoma). Evaluation on the real data showed that ADC-based features, extracted using our joint optimization approach classified lymphomas with an accuracy of approximately 78.6% (yielding a 11% increase in respect to the standard features extracted from unregistered diffusion-weighted images). Furthermore, the correlation between diffusion characteristics and histopathological findings was higher than any other previous approach of ADC computation.
表观扩散系数（ADC）被认为是一种重要的成像生物标志物，有助于评估组织的微观结构和病理生理学。它是通过扩散模型从磁共振弥散加权成像（DWI）计算得出的，通常不考虑在图像采集过程中的任何运动。我们提出了一种方法，通过对全身 DWI 中的运动伪影（通过分组配准）和扩散模型中的可能仪器噪声进行联合处理，来提高 ADC 的计算精度。在模拟运动和扩散数据上，所提出的可变形配准方法平均产生了最低的 ADC 重建误差。此外，我们的方法还应用于从 38 名经组织学证实的三种不同类型（霍奇金淋巴瘤、弥漫性大 B 细胞淋巴瘤和滤泡性淋巴瘤）淋巴瘤患者的一组全身扩散加权图像中。对真实数据的评估表明，使用我们的联合优化方法提取的基于 ADC 的特征可以对淋巴瘤进行分类，准确率约为 78.6%（与从未配准的扩散加权图像中提取的标准特征相比，准确率提高了 11%）。此外，与之前任何一种 ADC 计算方法相比，扩散特征与组织病理学发现之间的相关性更高。
