Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence
CMR Multitasking is a promising approach for quantitative imaging without breath-holds or ECG monitoring but standard iterative reconstruction is too long for clinical use. Supervised artificial intelligence (AI) can accelerate reconstruction but lacks generalizability and transparency, and T1 mapping precision has not been sufficient. Here we propose an AI-Assisted Iterative (AAI) reconstruction which takes an AI reconstruction output as a “warm start” to a well-characterized iterative reconstruction algorithm with only 2 iterations. The proposed method produces better image fidelity and more precise T1 maps than other accelerated reconstruction methods, in less than 15 seconds (16x faster than conventional iterative reconstruction).
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