基于边缘损失的低信噪比心脏MRI分组图像配准
Groupwise Image Registration with Edge-Based Loss for Low-SNR Cardiac MRI
AI总结：
针对低信噪比单次采集心脏MRI，提出基于边缘检测损失的深度学习配准方法AiM-ED，联合配准多幅图像，提升图像质量，优于现有方法。
摘要：
目的：对多幅自由呼吸单次采集心脏图像进行图像配准和平均，其中单幅图像可能具有低信噪比（SNR）。方法：为解决单次采集成像中遇到的低信噪比问题，特别是在低场强下，我们提出一种基于深度学习的快速图像配准方法，称为带边缘检测的平均变形（AiM-ED）。AiM-ED将多幅噪声源图像联合配准到一幅噪声目标图像，并利用噪声鲁棒的预训练边缘检测器来定义训练损失。我们使用来自MR扩展心脏-躯干（MRXCAT）体模的合成晚期钆增强（LGE）图像以及来自健康受试者（24层）和患者（5层）的自由呼吸单次采集LGE图像，在添加不同噪声水平下验证AiM-ED。此外，我们通过将AiM-ED应用于在0.55T扫描仪上扫描的患者（6层）数据，展示了其临床可行性。结果：与传统的基于能量最小化的图像配准方法和基于深度学习的VoxelMorph相比，使用AiM-ED配准的图像在恢复信噪比和三个感知图像质量指标上表现出更高的值。消融研究显示了联合处理多幅源图像以及在AiM-ED中使用边缘图的益处。结论：对于单次采集LGE成像，AiM-ED在图像质量方面优于现有的图像配准方法。凭借快速推理、极少的训练数据需求以及在各种噪声水平下的稳健性能，AiM-ED有潜力惠及单次采集CMR应用。
英文摘要：
Purpose: To perform image registration and averaging of multiple free-breathing single-shot cardiac images, where the individual images may have a low signal-to-noise ratio (SNR). Methods: To address low SNR encountered in single-shot imaging, especially at low field strengths, we propose a fast deep learning (DL)-based image registration method, called Averaging Morph with Edge Detection (AiM-ED). AiM-ED jointly registers multiple noisy source images to a noisy target image and utilizes a noise-robust pre-trained edge detector to define the training loss. We validate AiM-ED using synthetic late gadolinium enhanced (LGE) images from the MR extended cardiac-torso (MRXCAT) phantom and free-breathing single-shot LGE images from healthy subjects (24 slices) and patients (5 slices) under various levels of added noise. Additionally, we demonstrate the clinical feasibility of AiM-ED by applying it to data from patients (6 slices) scanned on a 0.55T scanner. Results: Compared to a traditional energy-minimization-based image registration method and DL-based VoxelMorph, images registered using AiM-ED exhibit higher values of recovery SNR and three perceptual image quality metrics. An ablation study shows the benefit of both jointly processing multiple source images and using an edge map in AiM-ED. Conclusion: For single-shot LGE imaging, AiM-ED outperforms existing image registration methods in terms of image quality. With fast inference, minimal training data requirements, and robust performance at various noise levels, AiM-ED has the potential to benefit single-shot CMR applications.
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