2020年10月1日 · In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning.

2024年5月21日 · 扩散张量磁共振成像（Diffusion Tensor Imaging, DTI）在活体人脑组织微结构和结构连接性映射方面具有无可比拟的优势。 然而，传统的DTI技术因为角度采样的要求导致扫描时间过长，制约了其在常规临床实践和大规模研究中的应用。

2011年12月12日 · Diffusion tensor imaging (DTI) uses diffusion-encoding gradients applied in different directions to estimate the diffusion tensor and subsequently measure parameters that indirectly reflect brain microstructure .

2023年2月9日 · 弥散张量成像（diffusion tensor imaging，DTI）是一种基于常规磁共振影像技术发展出的新兴影像表达方法。 DTI是MRI的一种特殊形式，不仅可以显示每一个体素的信号强度，而且还能够计算3维空间中张量方向的具体数据。 然后将每一个体素展现出的单个箭头根据神经束方向的差异进行整理，生成一组方向箭头。 再经由图像数据的处理将每一组的方向箭头进行加工，形成神经束的线性图像。 大脑中的白质纤维束呈几何形式排列，由于弥散磁共振成像能够清晰 …

Diffusion tensor imaging (DTI) is widely used for clinical neuroimaging and neuroscientific research but has traditionally suffered from relatively length acquisition. Here, we propose a new approach to obtain both scalar and orientational DTI metrics from six diffusion-weighted images with optimal directional encoding.

In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning.

We propose an efficient DTI model-based 3D-Unet (DTI-Unet) to predict high-quality diffusion tensor field and non-diffusion-weighted image from the noisy input. In our model, the input contains only six diffusion-weighted volumes and one b0 volume.

2020年6月1日 · In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional...

Here, we propose a joint denoising and q-space angular super-resolution method called “DeepDTI” achieved using data-driven supervised deep learning that minimizes the data requirement for DTI to the theoretical minimum of one b=0 image and six DWIs.

Diffusion tensor imaging (DTI) is an emerging MRI-based technique that is often used in research and applied to the study of white matter fiber tracts. Quantitative DTI metrics like fractional anisotropy (FA) and apparent diffusion constant (ADC) have been shown to demonstrate progressive changes with age and cerebral white matter maturation.