基于复谐波心脏编码的生成式MR多任务成像：弥合门控成像与实时成像的鸿沟
Generative MR Multitasking with complex-harmonic cardiac encoding: Bridging the gap between gated imaging and real-time imaging
AI总结：
提出生成式多任务框架，利用条件变分自编码器（CVAE）和复谐波心脏坐标统一门控与实时心脏MRI，实现柔性心脏运动表示、抑制伪影并提高定量T1/T2映射质量。
摘要：
目的：开发一个统一的图像重建框架，桥接实时和门控心脏MRI，包括定量MRI。方法：我们引入生成式多任务成像，它从序列时序中学习隐式神经时间基，并为心脏和呼吸运动学习可解释的潜空间。心脏运动被建模为复谐波，相位编码时序和潜振幅捕获逐搏功能变异性，连接心脏相位分辨（“类门控”）和时间分辨（“类实时”）视图。我们使用条件变分自编码器（CVAE）实现该框架，并在三种设置下评估自由呼吸、非ECG门控径向GRE：稳态电影成像、多对比T2prep/IR成像和双翻转角T1/T2映射，与传统多任务成像进行比较。结果：生成式多任务成像提供了灵活的心脏运动表示，能够重建典型的心脏相位分辨电影（如门控）以及揭示逐搏变异性的时间分辨序列（如实时成像）。对前一k空间角度进行条件化并在推理时修改该项，可去除涡流伪影而不会全局平滑高频时间成分。对于定量映射，与传统多任务成像相比，生成式多任务成像降低了间隔内T1和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：生成式多任务成像使用带有复谐波心脏坐标的CVAE，在单次自由呼吸、非ECG门控采集中统一了门控和实时CMR。它允许灵活的心脏运动表示，抑制轨迹相关伪影，并改善T1和T2映射，为无需单独门控和实时扫描的电影、多对比和定量成像提供了途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
