论文标题
新型深度学习的MR图像重建管道的性能表征
Performance characterization of a novel deep learning-based MR image reconstruction pipeline
论文作者
论文摘要
一种新型的基于深度学习的磁共振成像重建管道旨在解决传统重建的基本图像质量限制，以提供高分辨率，低噪声MR图像。该管道的独特目的是将截断伪像的形象转换为改进的图像清晰度，同时共同确定图像以提高图像质量。这种新方法现已在Air Recon DL（Waukesha，WI）的Air Recon DL上获得，其中包括一个深度卷积神经网络（CNN），以帮助重建原始数据，最终产生干净，清晰的图像。在这里，我们描述了该管道及其CNN的关键特征，它表征了其在数字参考对象，幻影和体内的性能，以及示例示例图像和协议优化策略，这些策略利用了减少扫描时间的图像质量改进的图像质量。这种新的基于学习的重建管道代表了提高MRI扫描仪的诊断和操作性性能的强大新工具。
A novel deep learning-based magnetic resonance imaging reconstruction pipeline was designed to address fundamental image quality limitations of conventional reconstruction to provide high-resolution, low-noise MR images. This pipeline's unique aims were to convert truncation artifact into improved image sharpness while jointly denoising images to improve image quality. This new approach, now commercially available at AIR Recon DL (GE Healthcare, Waukesha, WI), includes a deep convolutional neural network (CNN) to aid in the reconstruction of raw data, ultimately producing clean, sharp images. Here we describe key features of this pipeline and its CNN, characterize its performance in digital reference objects, phantoms, and in-vivo, and present sample images and protocol optimization strategies that leverage image quality improvement for reduced scan time. This new deep learning-based reconstruction pipeline represents a powerful new tool to increase the diagnostic and operational performance of an MRI scanner.
