Novel methods for quantitative, transient-state multiparametric imaging are increasingly being demonstrated for assessment of disease and treatment efficacy. Here, we build on these by assessing the most common Non-Cartesian readout trajectories (2D/3D radials and spirals), demonstrating efficient anti-aliasing with a k- space view-sharing technique, and proposing novel methods for parameter inference with neural networks that incorporate the estimation of proton density. Our results show good agreement with gold standard and phantom references for all readout trajectories at 1.5 T and 3 T. Parameters inferred with the neural network were within 6.58% difference from the parameters inferred with a high-resolution dictionary. Concordance correlation coefficients were above 0.92 and the normalized root mean squared error ranged between 4.2 and 12.7% with respect to gold-standard phantom references for T1 and T2. In vivo acquisitions demonstrate sub-millimetric isotropic resolution in under five minutes with reconstruction and inference times < 7 min. Our 3D quantitative transient-state imaging approach could enable high-resolution multiparametric tissue quantification within clinically acceptable acquisition and reconstruction times.

Purpose To obtain a fast and robust fat‐water separation with simultaneous estimation of water T 1 , fat T 1 , and fat fraction maps. Methods We modified an MR fingerprinting (MRF) framework to use a single dictionary combination of a water and fat dictionary. A variable TE acquisition pattern with maximum TE = 4.8 ms was used to increase the fat–water separability. Radiofrequency (RF) spoiling was used to reduce the size of the dictionary by reducing T 2 sensitivity. The technique was compared both in vitro and in vivo to an MRF method that incorporated 3‐point Dixon (DIXON MRF), as well as Cartesian IDEAL with different acquisition parameters. Results The proposed dictionary‐based fat–water separation technique (DBFW MRF) successfully provided fat fraction, water, and fat T 1 , B 0 , and B 1+ maps both in vitro and in vivo. The fat fraction and water T 1 values obtained with DBFW MRF show excellent agreement with DIXON MRF as well as with the reference values obtained using a Cartesian IDEAL with a long TR (concordance correlation coefficient: 0.97/0.99 for fat fraction–water T 1 ). Whereas fat fraction values with Cartesian IDEAL were degraded in the presence of T 1 saturation, MRF methods successfully estimated and accounted for T 1 in the fat fraction estimates. Conclusion The DBFW MRF technique can successfully provide T 1 and fat fraction quantification in under 20 s per slice, intrinsically correcting T 1 biases typical of fast Dixon techniques. These features could improve the diagnostic quality and use of images in presence of fat.

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