用深度学习自动估计高b值螺旋扩散MRI的磁场缺陷，提升图像质量。
ACE-Net: AutofoCus-Enhanced Convolutional Network for Field Imperfection Estimation with application to high b-value spiral Diffusion MRI
- 融合聚焦度指标与深度学习，结合紧凑基表示估计磁场误差。
- 在高b值单次采集螺旋扩散MRI中实现了精准的B0和涡流估计。
- 无需外部校准即可重建高质量图像，适合快速扩散MRI应用。
B0不均匀性和扩散编码引起的涡流导致时空磁场变化，对快速成像序列（如螺旋、EPI和3D锥形）产生不利影响，引发图像伪影。本文提出一种数据驱动方法，将聚焦度度量与深度学习结合，并利用预期磁场缺陷的紧凑基表示，实现场畸变的自动估计。该方法应用于高b值单次采集螺旋扩散MRI，成功获得精确的B0和涡流估计结果，实现了无需额外外部校准的高质量图像重建。
原文摘要 · Abstract (English)
Spatiotemporal magnetic field variations from B0-inhomogeneity and diffusion-encoding-induced eddy-currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts. In this work, a data driven approach for automatic estimation of these field imperfections is developed by combining autofocus metrics with deep learning, and by leveraging a compact basis representation of the expected field imperfections. The method was applied to single-shot spiral diffusion MRI at high b-values where accurate estimation of B0 and eddy were obtained, resulting in high quality image reconstruction without need for additional external calibrations.
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