论文标题
使用卷积神经网络进行螺旋实时MRI脱毛
Deblurring for Spiral Real-Time MRI Using Convolutional Neural Networks
论文作者
论文摘要
由于其时间效率，因此在实时MRI中首选螺旋出现。螺旋形的基本局限性是由于离子异常而导致的图像模糊，在空气组织边界处，图像质量显着降低。在这里，我们演示了一种简单的基于CNN的脱毛方法，用于人类语音生产的螺旋实时MRI。我们显示基于CNN的DeBlurring能够恢复模糊的声带组织边界，而无需特定于考试的场图。 Deblurring性能优于当前自动校准的方法，并且略微不如理想重建，并具有完美的田间图。
Spiral acquisitions are preferred in real-time MRI because of their time efficiency. A fundamental limitation of spirals is image blurring due to off-resonance, which degrades image quality significantly at air-tissue boundaries. Here, we demonstrate a simple CNN-based deblurring method for spiral real-time MRI of human speech production. We show the CNN-based deblurring is capable of restoring blurred vocal tract tissue boundaries, without a need for exam-specific field maps. Deblurring performance is superior to a current auto-calibrated method, and slightly inferior to ideal reconstruction with perfect knowledge of the field maps.
