Koktzoglou Ioannis, Huang Rong, Ong Archie L, Aouad Pascale J, Aherne Emily A, Edelman Robert R
Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois.
Pritzker School of Medicine, University of Chicago, Chicago, Illinois.
Magn Reson Med. 2020 Aug;84(2):825-837. doi: 10.1002/mrm.28179. Epub 2020 Jan 23.
To develop and test the feasibility of a sub-3-minute imaging strategy for non-contrast evaluation of the extracranial carotid arteries using ungated quiescent interval slice-selective (QISS) MRA, combining single-shot radial sampling with deep neural network-based image processing to optimize image quality.
The extracranial carotid arteries of 12 human subjects were imaged at 3 T using ungated QISS MRA. In 7 healthy volunteers, the effects of radial and Cartesian k-space sampling, single-shot and multishot image acquisition (1.1-3.3 seconds/slice, 141-423 seconds/volume), and deep learning-based image processing were evaluated using segmental image quality scoring, arterial temporal SNR, arterial-to-background contrast and apparent contrast-to-noise ratio, and structural similarity index. Comparison of deep learning-based image processing was made with block matching and 3D filtering denoising.
Compared with Cartesian sampling, radial k-space sampling increased arterial temporal SNR 107% (P < .001) and improved image quality during 1-shot imaging (P < .05). The carotid arteries were depicted with similar image quality on the rapid 1-shot and much lengthier 3-shot radial QISS protocols (P = not significant), which was corroborated in patient studies. Deep learning-based image processing outperformed block matching and 3D filtering denoising in terms of structural similarity index (P < .001). Compared with original QISS source images, deep learning image processing provided 24% and 195% increases in arterial-to-background contrast (P < .001) and apparent contrast-to-noise ratio (P < .001), and provided source images that were preferred by radiologists (P < .001).
Rapid, sub-3-minute evaluation of the extracranial carotid arteries is feasible with ungated single-shot radial QISS, and benefits from the use of deep learning-based image processing to enhance source image quality.
开发并测试一种使用非门控静态间隔切片选择（QISS）磁共振血管造影（MRA）对颅外颈动脉进行非对比评估的3分钟内成像策略的可行性，该策略将单次径向采样与基于深度神经网络的图像处理相结合以优化图像质量。
使用非门控QISS MRA在3T磁场下对12名受试者的颅外颈动脉进行成像。在7名健康志愿者中，使用分段图像质量评分、动脉时间信噪比、动脉与背景对比度和表观对比噪声比以及结构相似性指数，评估径向和笛卡尔k空间采样、单次和多次图像采集（1.1 - 3.3秒/切片，141 - 423秒/容积）以及基于深度学习的图像处理的效果。将基于深度学习的图像处理与块匹配和3D滤波去噪进行比较。
与笛卡尔采样相比，径向k空间采样使动脉时间信噪比提高了107%（P <.001），并在单次成像期间改善了图像质量（P <.05）。在快速单次和更长时间的三次径向QISS协议上，颈动脉的图像质量相似（P = 无显著差异），这在患者研究中得到了证实。基于深度学习的图像处理在结构相似性指数方面优于块匹配和3D滤波去噪（P <.001）。与原始QISS源图像相比，深度学习图像处理使动脉与背景对比度（P <.001）和表观对比噪声比（P <.001）分别提高了24%和195%，并提供了放射科医生更喜欢的源图像（P <.001）。
使用非门控单次径向QISS对颅外颈动脉进行快速、3分钟内的评估是可行的，并且受益于基于深度学习的图像处理来提高源图像质量。
