用生成式多任务学习统一实时与门控心脏MRI，一次扫描搞定多种成像。
Generative MR Multitasking with complex-harmonic cardiac encoding: Bridging the gap between gated imaging and real-time imaging
- 基于条件变分自编码器建模心脏复杂谐波运动，隐空间捕捉心跳变异
- 重建出类门控的相位分辨电影和类实时的逐搏变化序列，提升定量映射精度
- 适合需快速获取多模态心脏影像的临床研究与无导管检查场景
目的：开发一种统一的图像重建框架，弥合实时与门控心脏MRI之间的差距，涵盖定量MRI。方法：提出生成式多任务学习，从序列时序中学习隐式神经时间基，并建立可解释的心脏与呼吸运动潜在空间。心脏运动建模为复数谐波，相位编码时序与潜在幅值共同捕捉心跳间功能变异，连接相位分辨（类门控）与时间分辨（类实时）视图。采用条件变分自编码器（CVAE）实现该框架，在自由呼吸、非心电门控径向梯度回波三种场景下评估：稳态电影成像、多对比T2prep/IR成像、双翻转角T1/T2映射，对比传统多任务方法。结果：生成式多任务学习实现了灵活的心脏运动表征，可重构类门控的典型相位分辨电影及揭示逐搏变异的时间分辨序列；推理时条件前一k空间角度并修改该项，有效消除涡流伪影而不全局平滑高时间频率。在定量映射中，生成式多任务学习相比传统多任务学习显著降低室间隔内T1与T2变异系数（T1: 0.13 vs. 0.31；T2: 0.12 vs. 0.32；p<0.001），表明信噪比更高。结论：生成式多任务学习通过结合复数谐波心脏坐标与CVAE，实现自由呼吸、非心电门控单次采集下门控与实时CMR的统一。该方法支持灵活的心脏运动表征，抑制轨迹依赖伪影，并改善T1/T2映射，为无需分离门控与实时扫描即可实现电影、多对比及定量成像提供新路径。
原文摘要 · Abstract (English)
Purpose: To develop a unified image reconstruction framework that bridges real-time and gated cardiac MRI, including quantitative MRI. Methods: We introduce Generative Multitasking, which learns an implicit neural temporal basis from sequence timings and an interpretable latent space for cardiac and respiratory motion. Cardiac motion is modeled as a complex harmonic, with phase encoding timing and a latent amplitude capturing beat-to-beat functional variability, linking cardiac phase-resolved ("gated-like") and time-resolved ("real-time-like") views. We implemented the framework using a conditional variational autoencoder (CVAE) and evaluated it for free-breathing, non-ECG-gated radial GRE in three settings: steady-state cine imaging, multicontrast T2prep/IR imaging, and dual-flip-angle T1/T2 mapping, compared with conventional Multitasking. Results: Generative Multitasking provided flexible cardiac motion representation, enabling reconstruction of archetypal cardiac phase-resolved cines (like gating) as well as time-resolved series that reveal beat-to-beat variability (like real-time imaging). Conditioning on the previous k-space angle and modifying this term at inference removed eddy-current artifacts without globally smoothing high temporal frequencies. For quantitative mapping, Generative Multitasking reduced intraseptal T1 and T2 coefficients of variation compared with conventional Multitasking (T1: 0.13 vs. 0.31; T2: 0.12 vs. 0.32; p<0.001), indicating higher SNR. Conclusion: Generative Multitasking uses a CVAE with complex harmonic cardiac coordinates to unify gated and real-time CMR within a single free-breathing, non-ECG-gated acquisition. It allows flexible cardiac motion representation, suppresses trajectory-dependent artifacts, and improves T1 and T2 mapping, suggesting a path toward cine, multicontrast, and quantitative imaging without separate gated and real-time scans.
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