Purpose To develop a new high‐dimensionality undersampled patch‐based reconstruction (HD‐PROST) for highly accelerated 2D and 3D multi‐contrast MRI. Methods HD‐PROST jointly reconstructs multi‐contrast MR images by exploiting the highly redundant information, on a local and non‐local scale, and the strong correlation shared between the multiple contrast images. This is achieved by enforcing multi‐dimensional low‐rank in the undersampled images. 2D magnetic resonance fingerprinting (MRF) phantom and in vivo brain acquisitions were performed to evaluate the performance of HD‐PROST for highly accelerated simultaneous T 1 and T 2 mapping. Additional in vivo experiments for reconstructing multiple undersampled 3D magnetization transfer (MT)‐weighted images were conducted to illustrate the impact of HD‐PROST for high‐resolution multi‐contrast 3D imaging. Results In the 2D MRF phantom study, HD‐PROST provided accurate and precise estimation of the T 1 and T 2 values in comparison to gold standard spin echo acquisitions. HD‐PROST achieved good quality maps for the in vivo 2D MRF experiments in comparison to conventional low‐rank inversion reconstruction. T 1 and T 2 values of white matter and gray matter were in good agreement with those reported in the literature for MRF acquisitions with reduced number of time point images (500 time point images, ~2.5 s scan time). For in vivo MT‐weighted 3D acquisitions (6 different contrasts), HD‐PROST achieved similar image quality than the fully sampled reference image for an undersampling factor of 6.5‐fold. Conclusion HD‐PROST enables multi‐contrast 2D and 3D MR images in a short acquisition time without compromising image quality. Ultimately, this technique may increase the potential of conventional parameter mapping.

Purpose Cardiac magnetic resonance fingerprinting (cMRF) has been recently introduced to simultaneously provide T1, T2, and M0 maps. Here, we develop a 3‐point Dixon‐cMRF approach to enable simultaneous water specific T1, T2, and M0 mapping of the heart and fat fraction (FF) estimation in a single breath‐hold scan. Methods Dixon‐cMRF is achieved by combining cMRF with several innovations that were previously introduced for other applications, including a 3‐echo GRE acquisition with golden angle radial readout and a high‐dimensional low‐rank tensor constrained reconstruction to recover the highly undersampled time series images for each echo. Water–fat separation of the Dixon‐cMRF time series is performed to allow for water‐ and fat‐specific T1, T2, and M0 estimation, whereas FF estimation is extracted from the M0 maps. Dixon‐cMRF was evaluated in a standardized T1–T2 phantom, in a water–fat phantom, and in healthy subjects in comparison to current clinical standards: MOLLI, SASHA, T2‐GRASE, and 6‐point Dixon proton density FF (PDFF) mapping. Results Dixon‐cMRF water T1 and T2 maps showed good agreement with reference T1 and T2 mapping techniques (R2 > 0.99 and maximum normalized RMSE ~5%) in a standardized phantom. Good agreement was also observed between Dixon‐cMRF FF and reference PDFF (R2 > 0.99) and between Dixon‐cMRF water T1 and T2 and water selective T1 and T2 maps (R2 > 0.99) in a water–fat phantom. In vivo Dixon‐cMRF water T1 values were in good agreement with MOLLI and water T2 values were slightly underestimated when compared to T2‐GRASE. Average myocardium septal T1 values were 1129 ± 38 ms, 1026 ± 28 ms, and 1045 ± 32 ms for SASHA, MOLLI, and the proposed water Dixon‐cMRF. Average T2 values were 51.7 ± 2.2 ms and 42.8 ± 2.6 ms for T2‐GRASE and water Dixon‐cMRF, respectively. Dixon‐cMRF FF maps showed good agreement with in vivo PDFF measurements (R2 > 0.98) and average FF in the septum was measured at 1.3%. Conclusion The proposed Dixon‐cMRF allows to simultaneously quantify myocardial water T1, water T2, and FF in a single breath‐hold scan, enabling multi‐parametric T1, T2, and fat characterization. Moreover, reduced T1 and T2 quantification bias caused by water–fat partial volume was demonstrated in phantom experiments.

Purpose To develop a novel respiratory motion compensated three‐dimensional (3D) cardiac magnetic resonance fingerprinting (cMRF) approach for whole‐heart myocardial T1 and T2 mapping from a free‐breathing scan. Methods Two‐dimensional (2D) cMRF has been recently proposed for simultaneous, co‐registered T1 and T2 mapping from a breath‐hold scan; however, coverage is limited. Here we propose a novel respiratory motion compensated 3D cMRF approach for whole‐heart myocardial T1 and T2 tissue characterization from a free‐breathing scan. Variable inversion recovery and T2 preparation modules are used for parametric encoding, respiratory bellows driven localized autofocus is proposed for beat‐to‐beat translation motion correction and a subspace regularized reconstruction is employed to accelerate the scan. The proposed 3D cMRF approach was evaluated in a standardized T1/T2 phantom in comparison with reference spin echo values and in 10 healthy subjects in comparison with standard 2D MOLLI, SASHA and T2‐GraSE mapping techniques at 1.5 T. Results 3D cMRF T1 and T2 measurements were generally in good agreement with reference spin echo values in the phantom experiments, with relative errors of 2.9% and 3.8% for T1 and T2 (T2 < 100 ms), respectively. in vivo left ventricle (LV) myocardial T1 values were 1054 ± 19 ms for MOLLI, 1146 ± 20 ms for SASHA and 1093 ± 24 ms for the proposed 3D cMRF; corresponding T2 values were 51.8 ± 1.6 ms for T2‐GraSE and 44.6 ± 2.0 ms for 3D cMRF. LV coefficients of variation were 7.6 ± 1.6% for MOLLI, 12.1 ± 2.7% for SASHA and 5.8 ± 0.8% for 3D cMRF T1, and 10.5 ± 1.4% for T2‐GraSE and 11.7 ± 1.6% for 3D cMRF T2. Conclusion The proposed 3D cMRF can provide whole‐heart, simultaneous and co‐registered T1 and T2 maps with accuracy and precision comparable to those of clinical standards in a single free‐breathing scan of about 7 min.

Purpose Quantitative T1, T2, T2*, and fat fraction (FF) maps are promising imaging biomarkers for the assessment of liver disease, however these are usually acquired in sequential scans. Here we propose an extended MR fingerprinting (MRF) framework enabling simultaneous liver T1, T2, T2*, and FF mapping from a single ~14 s breath‐hold scan. Methods A gradient echo (GRE) liver MRF sequence with nine readouts per TR, low flip angles (5‐15°), varying magnetisation preparation and golden angle radial trajectory is acquired at 1.5T to encode T1, T2, T2*, and FF simultaneously. The nine‐echo time‐series are reconstructed using a low‐rank tensor constrained reconstruction and used to fit T2*, B0 and to separate the water and fat signals. Water‐ and fat‐specific T1, T2, and M0 are obtained through dictionary matching, whereas FF estimation is extracted from the M0 maps. The framework was evaluated in a standardized T1/T2 phantom, a water‐fat phantom, and 12 subjects in comparison to reference methods. Preliminary clinical feasibility is shown in four patients. Results The proposed water T1, water T2, T2*, and FF maps in phantoms showed high coefficients of determination (r2 > 0.97) relative to reference methods. Measured liver MRF values in vivo (mean ± SD) for T1, T2, T2*, and FF were 671 ± 60 ms, 43.2 ± 6.8 ms, 29 ± 6.6 ms, and 3.2 ± 2.6% with biases of 92 ms, −7.1 ms, −1.4 ms, and 0.63% when compared to conventional methods. Conclusion A nine‐echo liver MRF sequence allows for quantitative multi‐parametric liver tissue characterization in a single breath‐hold scan of ~14 s. Future work will aim to validate the proposed approach in patients with liver disease.