Deep learning reconstruction for cardiac magnetic resonance fingerprinting T₁ and T₂ mapping

To develop a deep learning method for rapidly reconstructing T₁ and T₂ maps from undersampled electrocardiogram (ECG) triggered cardiac magnetic resonance fingerprinting (cMRF) images.

A neural network was developed that outputs T₁ and T₂ values when given a measured cMRF signal time course and cardiac RR interval times recorded by an ECG. Over 8 million cMRF signals, corresponding to 4000 random cardiac rhythms, were simulated for training. The training signals were corrupted by simulated k-space undersampling artifacts and random phase shifts to promote robust learning. The deep learning reconstruction was evaluated in Monte Carlo simulations for a variety of cardiac rhythms and compared with dictionary-based pattern matching in 58 healthy subjects at 1.5T.

In simulations, the normalized root-mean-square error (nRMSE) for T₁ was below 1% in myocardium, blood, and liver for all tested heart rates. For T₂ , the nRMSE was below 4% for myocardium and liver and below 6% for blood for all heart rates. The difference in the mean myocardial T₁ or T₂ observed in vivo between dictionary matching and deep learning was 3.6 ms for T₁ and -0.2 ms for T₂ . Whereas dictionary generation and pattern matching required more than 4 min per slice, the deep learning reconstruction only required 336 ms.

A neural network is introduced for reconstructing cMRF T₁ and T₂ maps directly from undersampled spiral images in under 400 ms and is robust to arbitrary cardiac rhythms, which paves the way for rapid online display of cMRF maps.