bibtext: @InProceedings{ Liu_TaggedtoCine_MICCAI2024,
                   author = { Liu, Xiaofeng and Xing, Fangxu and Bian, Zhangxing and Arias-Vergara, Tomas and Pérez-Toro, Paula Andrea and Maier, Andreas and Stone, Maureen and Zhuo, Jiachen and Prince, Jerry L. and Woo, Jonghye },
                   title = { { Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 spatial-temporal modeling efficiency and consistency. The light spatial-temporal transformer (LiST$^2$) 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.
title: Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer
authors: Liu, Xiaofeng Xing, Fangxu Bian, Zhangxing Arias-Vergara, Tomas Pérez-Toro, Paula Andrea Maier, Andreas Stone, Maureen Zhuo, Jiachen Prince, Jerry L. Woo, Jonghye