Institute of Information Technology, Warsaw University of Life Sciences, 159 Nowoursynowska, 02776 Warsaw, Poland.
Sensors (Basel). 2023 Jun 19;23(12):5698. doi: 10.3390/s23125698.
The utilization of quick compression-sensed magnetic resonance imaging results in an enhancement of diffusion imaging. Wasserstein Generative Adversarial Networks (WGANs) leverage image-based information. The article presents a novel G-guided generative multilevel network, which leverages diffusion weighted imaging (DWI) input data with constrained sampling. The present study aims to investigate two primary concerns pertaining to MRI image reconstruction, namely, image resolution and reconstruction duration. The implementation of simultaneous k-q space sampling has been found to enhance the performance of Rotating Single-Shot Acquisition (RoSA) without necessitating any hardware modifications. Diffusion weighted imaging (DWI) is capable of decreasing the duration of testing by minimizing the amount of input data required. The synchronization of diffusion directions within PROPELLER blades is achieved through the utilization of compressed k-space synchronization. The grids utilized in DW-MRI are represented by minimal-spanning trees. The utilization of conjugate symmetry in sensing and the Partial Fourier approach has been observed to enhance the efficacy of data acquisition as compared to unaltered k-space sampling systems. The image's sharpness, edge readings, and contrast have been enhanced. These achievements have been certified by numerous metrics including PSNR and TRE. It is desirable to enhance image quality without necessitating any modifications to the hardware.
快速压缩感知磁共振成像的应用导致扩散成像得到增强。 Wasserstein 生成对抗网络 (WGAN) 利用基于图像的信息。本文提出了一种新颖的 G 引导生成多级网络，该网络利用扩散加权成像 (DWI) 输入数据进行受限采样。本研究旨在探讨与 MRI 图像重建相关的两个主要问题，即图像分辨率和重建时间。同时进行 k-q 空间采样的实现被发现可以增强 Rotating Single-Shot Acquisition (RoSA) 的性能，而无需进行任何硬件修改。扩散加权成像 (DWI) 通过最小化所需输入数据量来缩短测试时间。通过利用压缩 k 空间同步来实现 PROPELLER 叶片内扩散方向的同步。DW-MRI 中使用的网格由最小生成树表示。与未经修改的 k 空间采样系统相比，共轭对称性在感应和部分傅里叶方法中的应用已被观察到可提高数据采集的效果。图像的锐度、边缘读数和对比度都得到了增强。这些成就已通过包括 PSNR 和 TRE 在内的多种指标得到了验证。在不修改硬件的情况下提高图像质量是可取的。
