论文标题
中枢神经系统的2D-MRI：基于深度学习的重建管道对整体图像质量的影响
2D-MRI of the Central Nervous System: The effect of a deep learning-based reconstruction pipeline on the overall image quality
论文作者
论文摘要
这项研究的目的是评估配备深卷积神经网络对整体图像质量的强大磁共振重建管道的影响，从吉布斯人伪影降低和SNR改进方面。这项研究的16（16）名健康志愿者参加了这项研究，并以3T成像。通过管道重建了每个图像系列的代表性图像，这些图像序列是通过传统管道通过常规管道重建的相应图像进行了回顾性的。与相应的常规重建图像相比，DL重建图像显示出显着的SNR改进。除此之外，当通过DL管道重建原始数据时，有效消除了吉布斯伪像。吉布斯人伪影的减少是由两名经验丰富的医学物理学家和两名经验丰富的放射科医生定性评估的。基于DL的重建可以导致SNR盈余，该盈余可以进一步投入到更高的空间分辨率和更薄的切片中，或者较短的扫描时间。
Purpose of this study was to evaluate the effect of a robust magnetic resonance reconstruction pipeline equipped with a deep convolutional neural network on the overall image quality, in terms of Gibbs artifact reduction, and SNR improvement. Sixteen (16) healthy volunteers enrolled in this study and were imaged at 3T. Representative images of each image series that were reconstructed through the pipeline that leverages a deep learning (DL) algorithm were retrospectively benchmarked against corresponding images reconstructed through a conventional pipeline. DL-reconstructed images showed significant SNR improvements compared to the corresponding conventionally reconstructed images. In addition to that, Gibbs artifacts were effectively eliminated, when the raw data were reconstructed through the DL pipeline. Gibbs artifact reduction was qualitatively assessed by two experienced medical physicists and two experienced radiologists. DL-based reconstruction can lead to an SNR surplus which can be further invested into either higher spatial resolution and thinner slices, or into shorter scan times.
