Deep learning reconstruction for cardiac magnetic resonance fingerprinting T<sub>1</sub> and T<sub>2</sub> mapping
Jesse Hamilton, Danielle Currey, Sanjay Rajagopalan, Nicole Seiberlich
Abstract
Purpose To develop a deep learning method for rapidly reconstructing T 1 and T 2 maps from undersampled electrocardiogram (ECG) triggered cardiac magnetic resonance fingerprinting (cMRF) images. Methods A neural network was developed that outputs T 1 and T 2 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. Results In simulations, the normalized root‐mean‐square error (nRMSE) for T 1 was below 1% in myocardium, blood, and liver for all tested heart rates. For T 2 , the nRMSE was below 4% for myocardium and liver and below 6% for blood for all heart rates. The difference in the mean myocardial T 1 or T 2 observed in vivo between dictionary matching and deep learning was 3.6 ms for T 1 and −0.2 ms for T 2 . Whereas dictionary generation and pattern matching required more than 4 min per slice, the deep learning reconstruction only required 336 ms. Conclusion A neural network is introduced for reconstructing cMRF T 1 and T 2 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.