In this paper, a new, fast compressively sensed diffusion magnetic resonance image enhancement technique is presented. This algorithm aims to overcome two major obstacles—image resolution limitation and algorithm reconstruction time efficiency-by combining a highly sparse k–q-space sampling pattern with super-resolution (SR) image enhancement. Similar to the RoSA (rotating single-shot acquisition) acceleration scheme, the presented algorithm takes advantage of simultaneous k–q-space sampling procedures being able to implement directly with no hardware modifications. The method sequentially processes compressively sensed k-space’s semi-PROPELLER blades with respect to appropriately synchronized diffusion directions. The dMR image structure is expressed as a kind of minimum-spanning tree. It fades out distortions of the image’s features. Moreover, as contrasted with numerous other super-resolution algorithms, the presented method overcomes the simplifying motion model as well as blur kernel and noise estimation issues. The simulation and experimental studies have been conducted using a dMRI scanner as well as a phantom input. Combining super-resolution with time-efficient data sets resulted in a reduction of motion artifacts, improving edge delineation as well as spatial resolution.

中文翻译：

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具有增强分辨率的快速扩散加权成像

在本文中，提出了一种新的、快速的压缩传感扩散磁共振图像增强技术。该算法旨在通过将高度稀疏的 k-q 空间采样模式与超分辨率 (SR) 图像增强相结合来克服两个主要障碍——图像分辨率限制和算法重建时间效率。与 RoSA（旋转单次采集）加速方案类似，所提出的算法利用同步 k-q 空间采样程序，无需硬件修改即可直接实现。该方法相对于适当同步的扩散方向顺序处理压缩感测到的 k 空间的半螺旋桨叶片。dMR 图像结构表示为一种最小生成树。它淡出图像特征的失真。而且，与许多其他超分辨率算法相比，所提出的方法克服了简化的运动模型以及模糊核和噪声估计问题。模拟和实验研究是使用 dMRI 扫描仪和幻像输入进行的。将超分辨率与省时的数据集相结合，减少了运动伪影，改善了边缘描绘和空间分辨率。