Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

Abstract Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.

摘要引言：扩散张量成像（Diffusion Tensor Imaging, DTI）是一种重要的医学成像模态，已被广泛应用于神经系统疾病微观结构变化的研究。然而，图像噪声水平是其主要的临床应用局限，常规解决方案之一是对多次连续采集的信号进行平均，但该方法耗时较长，难以在临床常规工作中推广应用。本研究旨在评估各向异性反常扩散（Anisotropic anomalous diffusion, AAD）滤波器，以改善扩散张量成像的整体图像质量。

方法：本研究纳入20名健康受试者，使用3T磁共振扫描仪采集不同平均次数（N=1、2、4、6、8及16）的DTI数据，并分别对其应用二维AAD滤波器与传统各向异性扩散滤波器。本研究从各向异性分数（Fractional Anisotropy, FA）及表观扩散系数（Apparent Diffusion Coefficient, ADC）图谱出发，评估了相对平均误差（Relative Mean Error, RME）、结构相似性指数（Structural Similarity Index, SSIM）、变异系数（Coefficient of Variation, CV）以及纤维束追踪重建效果。

结果：结果显示，AAD滤波器可使CV、RME及SSIM指标最高提升30%，而传统各向异性扩散滤波器的提升幅度最高为14%（p<0.05）。纤维束追踪结果表明，AAD滤波器在纤维计数估算上表现更优，且其FA变异性更低。此外，AAD滤波器的图像质量提升效果与更高次数的平均采集方案相当，即可达到额外两次采集后的图像质量水平。

结论：总体而言，即使在高噪声水平的DTI图像中，AAD滤波器仍展现出良好的噪声抑制能力与整体图像质量提升效果。