Magnetic resonance fingerprinting with dictionary‐based fat and water separation (DBFW MRF): A multi‐component approach

Abstract

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.

Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations

“…Assländer et al analytically calculated the CRLB and its gradient for the pSSFP sequence, but this is valid only for balanced sequences with constraints on the dephasing between TRs, TR ≪ , , and restrictions on the sign of the magnetization. Overall, these approaches may face difficulty when applied to MRF sequences with gradient spoiling, RF‐spoiling, additional parameter estimates such as fat fraction or , or long repetition times …”

“…Assländer et al analytically calculated the CRLB and its gradient for the pSSFP sequence, but this is valid only for balanced sequences with constraints on the dephasing between TRs, TR ≪ T 1 , T 2 , and restrictions on the sign of the magnetization. Overall, these approaches may face difficulty when applied to MRF sequences with gradient spoiling, 12 RF-spoiling, 28,29 additional parameter estimates such as fat fraction 30 or T * 2 , 28,31 or long repetition times. 32 Automatic differentiation allows us to jointly optimize over flip angles and TRs with high-computational efficiency in MRF, where finite differences would be infeasible due to the number of control variables.…”

Water–fat Dixon cardiac magnetic resonance fingerprinting

“…In previous approaches to enable water–fat MRF, additional parameters such as B 0 and/or B 1 needed to be included in the dictionary for correction. The correction was achieved in earlier water–fat MRF works using separate acquisitions to obtain the field maps or adding the additional parameters as part of the dictionary matching step . Both techniques significantly increase the dictionary size (and corresponding computation time) without providing additional diagnostic information.…”

“…The correction was achieved in earlier water-fat MRF works using separate acquisitions to obtain the field maps 25,26 or adding the additional parameters as part of the dictionary matching step. [22][23][24] Both techniques significantly increase the dictionary size (and corresponding computation time) without providing additional diagnostic information. Since cardiac MRF requires subject-specific dictionaries, shortening dictionary computation times is essential to maintain feasible reconstruction times.…”

Magnetic resonance fingerprinting review part 2: Technique and directions

“…Some examples include sequences that are sensitive to T 2 *, 61-64 perfusion, 65,66 and water-fat quantification. 43,67 A more complicated model is needed in the case of MRF for chemical exchange, or MRF-X, 68 in which six properties are quantified, including two relaxation properties to characterize two exchanging components within a voxel, volume fraction, and exchange rate.…”