Magnetic resonance in medicine · 2026
Generative MR Multitasking With Complex-Harmonic Cardiac Encoding: Bridging the Gap Between Gated Imaging and Real-Time Imaging.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Fang Xinguo, Christodoulou Anthony G
Аннотация
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 subject- and dataset-specific implicit neural temporal bases 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/inversion-recovery 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 (CoV) 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. The framework 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.
METHODS: We introduce generative multitasking, which learns subject- and dataset-specific implicit neural temporal bases 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/inversion-recovery 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 (CoV) 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. The framework 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.