通过光时空变换器实现标记-正片磁共振成像序列合成

Xiaofeng Liu, Fangxu Xing, Zhangxing Bian, Tomas Arias-Vergara, Paula Andrea Pérez-Toro, Andreas Maier, Maureen Stone, Jiachen Zhuo, Jerry L Prince, Jonghye Woo
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摘要

标记磁共振成像(MRI)已成功用于跟踪移动器官内部组织点的运动。通常情况下,要使用标记磁共振成像分析运动,需要获取同一坐标系的 cine MRI 数据,这就需要额外的时间和成本。因此,从标记到线性磁共振合成有望减少与线性磁共振成像相关的额外采集时间和成本,同时又不会影响下游的运动分析任务。以往的方法对每一帧图像进行独立处理,从而忽略了标签图案闭塞区域的补充信息可能存在于显示运动的相邻帧图像中这一事实。此外,各帧之间不一致的视觉外观(如标签褪色)也会降低合成性能。为了解决这个问题,我们提出了一个高效的框架,利用空间和时间信息,在数据相对有限的情况下进行标记到线性 MR 序列合成。具体来说,我们采用分割-积分协议来平衡时空建模效率和一致性。轻型时空变换器(LiST2)旨在利用运动序列中的局部和全局注意力,训练参数相对较轻。通过调整方向积相对位置-时间偏置,使模型意识到时空相关性,同时使用移动窗口进行运动对齐。然后,采用循环滑动微调(ReST)方案进一步增强时间一致性。我们的框架在成对标记和电影核磁共振成像序列上进行了评估,证明其性能优于比较方法。
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Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer.

Tagged magnetic resonance imaging (MRI) has been successfully used to track the motion of internal tissue points within moving organs. Typically, to analyze motion using tagged MRI, cine MRI data in the same coordinate system are acquired, incurring additional time and costs. Consequently, tagged-to-cine MR synthesis holds the potential to reduce the extra acquisition time and costs associated with cine MRI, without disrupting downstream motion analysis tasks. Previous approaches have processed each frame independently, thereby overlooking the fact that complementary information from occluded regions of the tag patterns could be present in neighboring frames exhibiting motion. Furthermore, the inconsistent visual appearance, e.g., tag fading, across frames can reduce synthesis performance. To address this, we propose an efficient framework for tagged-to-cine MR sequence synthesis, leveraging both spatial and temporal information with relatively limited data. Specifically, we follow a split-and-integral protocol to balance spatialtemporal modeling efficiency and consistency. The light spatial-temporal transformer (LiST2) is designed to exploit the local and global attention in motion sequence with relatively lightweight training parameters. The directional product relative position-time bias is adapted to make the model aware of the spatial-temporal correlation, while the shifted window is used for motion alignment. Then, a recurrent sliding fine-tuning (ReST) scheme is applied to further enhance the temporal consistency. Our framework is evaluated on paired tagged and cine MRI sequences, demonstrating superior performance over comparison methods.

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