Purpose To introduce a 2D MR Fingerprinting technique for quantification of T1, T2, and M0 in myocardium. Methods An ECG-triggered MR Fingerprinting (MRF) method is introduced for mapping myocardial T1, T2, and M0 during a single breathhold in as short as four heartbeats. The pulse sequence employs variable flip angles, repetition times, inversion recovery times, and T2 preparation dephasing times. A dictionary of possible signal evolutions is simulated for each scan that incorporates the subject’s unique variations in heart rate. Aspects of the sequence design were explored in simulations, and the accuracy and precision of cardiac MRF were assessed in a phantom study. In vivo imaging was performed at 3T in eleven volunteers to generate native parametric maps. Results T1 and T2 measurements from the proposed cardiac MRF sequence correlated well with standard spin echo measurements in the phantom study (R2>0.99). A Bland-Altman analysis revealed good agreement for myocardial T1 measurements between MRF and MOLLI (bias 1ms, 95% limits of agreement −72 to 72ms) and T2 measurements between MRF and T2-prepared bSSFP (bias −2.6ms, 95% limits of agreement −8.5 to 3.3ms). Conclusions MRF can provide quantitative single slice T1, T2, and M0 maps in the heart within a single breathhold.

This study aims to improve the accuracy and consistency of T and T measurements using cardiac MR Fingerprinting (cMRF) by investigating and accounting for the effects of confounding factors including slice profile, inversion and T preparation pulse efficiency, and B. The goal is to understand how measurements with different pulse sequences are affected by these factors. This can be used to determine which factors must be taken into account for accurate measurements, and which may be mitigated by the selection of an appropriate pulse sequence. Simulations were performed using a numerical cardiac phantom to assess the accuracy of over 600 cMRF sequences with different flip angles, TRs, and preparation pulses. A subset of sequences, including one with the lowest errors in T and T maps, was used in subsequent analyses. Errors due to non-ideal slice profile, preparation pulse efficiency, and B were quantified in Bloch simulations. Corrections for these effects were included in the dictionary generation and demonstrated in phantom and in vivo cardiac imaging at 3 T. Neglecting to model slice profile and preparation pulse efficiency led to underestimated T and overestimated T for most cMRF sequences. Sequences with smaller maximum flip angles were less affected by slice profile and B. Simulating all corrections in the dictionary improved the accuracy of T and T phantom measurements, regardless of acquisition pattern. More consistent myocardial T and T values were measured using different sequences after corrections. Based on these results, a pulse sequence which is minimally affected by confounding factors can be selected, and the appropriate residual corrections included for robust T and T mapping.

Objective: This study aims for targeted biopsy validation of magnetic resonance fingerprinting (MRF) and diffusion mapping for characterizing peripheral zone (PZ) prostate cancer and noncancers. Materials and Methods: One hundred four PZ lesions in 85 patients who underwent magnetic resonance imaging were retrospectively analyzed with apparent diffusion coefficient (ADC) mapping, MRF, and targeted biopsy (cognitive or in-gantry). A radiologist blinded to pathology drew regions of interest on targeted lesions and visually normal peripheral zone on MRF and ADC maps. Mean T1, T2, and ADC were analyzed using linear mixed models. Generalized estimating equations logistic regression analyses were used to evaluate T1 and T2 relaxometry combined with ADC in differentiating pathologic groups. Results: Targeted biopsy revealed 63 cancers (low-grade cancer/Gleason score 6 = 10, clinically significant cancer/Gleason score ≥7 = 53), 15 prostatitis, and 26 negative biopsies. Prostate cancer T1, T2, and ADC (mean ± SD, 1660 ± 270 milliseconds, 56 ± 20 milliseconds, 0.70 Â 10 −3 ± 0.24 Â 10 −3 mm 2 /s) were significantly lower than prostatitis (mean ± SD, 1730 ± 350 milliseconds, 77 ± 36 milliseconds, 1.00 Â 10 −3 ± 0.30 Â 10 −3 mm 2 /s) and negative biopsies (mean ± SD, 1810 ± 250 milliseconds, 71 ± 37 milliseconds, 1.00 Â 10 −3 ± 0.33 Â 10 −3 mm 2 /s). For cancer versus prostatitis, ADC was sensitive and T2 specific with comparable area under curve (AUC; (AUC T2 = 0.71, AUC ADC = 0.79, difference between AUCs not significant P = 0.37). T1 + ADC (AUC T1 + ADC = 0.83) provided the best separation between cancer and negative biopsies. Low-grade cancer T2 and ADC (mean ± SD, 75 ± 29 milliseconds, 0.96 Â 10 −3 ± 0.34 Â 10 −3 mm 2 /s) were significantly higher than clinically significant cancers (mean ± SD, 52 ± 16 milliseconds, 0.65 ± 0.18 Â 10 −3 mm 2 /s), and T2 + ADC (AUC T2 + ADC = 0.91) provided the best separation. Conclusions: T1 and T2 relaxometry combined with ADC mapping may be useful for quantitative characterization of prostate cancer grades and differentiating cancer from noncancers for PZ lesions seen on T2-weighted images.

Spiral GRAPPA enabled 4-fold accelerated real-time MRI of speech with a low reconstruction latency. This approach is applicable to wide range of speech RT-MRI experiments that benefit from real-time feedback while visualizing rapid articulator movement. Magn Reson Med 78:2275-2282, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Purpose This work presents a 4D numerical abdominal phantom, which includes T1 and T2 relaxation times, proton density fat fraction, perfusion, and diffusion, as well as respiratory motion for the evaluation and comparison of acquisition and reconstruction techniques. Methods The 3D anatomical mesh models were non‐rigidly scaled and shifted by respiratory motion derived from an in vivo scan. A time series of voxelized 3D abdominal phantom images were obtained with contrast determined by the tissue properties and pulse sequence parameters. Two example simulations: (1) 3D T1 mapping under breath‐hold and free‐breathing acquisition conditions and (2) two different reconstruction techniques for accelerated 3D dynamic contrast‐enhanced MRI, are presented. The source codes can be found at https://github.com/SeiberlichLab/Abdominal_MR_Phantom. Results The proposed 4D abdominal phantom can successfully simulate images and MRI data with nonrigid respiratory motion and specific contrast settings and data sampling schemes. In example 1, the use of a numerical 4D abdominal phantom was demonstrated to aid in the comparison between different approaches for volumetric T1 mapping. In example 2, the average arterial fraction over the healthy hepatic parenchyma as calculated with spiral generalized autocalibrating partial parallel acquisition was closer to that from the fully sampled data than the arterial fraction from conjugate gradient sensitivity encoding, although both are elevated compared to the gold‐standard reference. Conclusion This realistic abdominal MR phantom can be used to simulate different pulse sequences and data sampling schemes for the comparison of acquisition and reconstruction methods under controlled conditions that are impossible or prohibitively difficult to perform in vivo.

The quantification of cardiac T relaxation time holds great potential for the detection of various cardiac diseases. However, as a result of both cardiac and respiratory motion, only one two-dimensional T map can be acquired in one breath-hold with most current techniques, which limits its application for whole heart evaluation in routine clinical practice. In this study, an electrocardiogram (ECG)-triggered three-dimensional Look-Locker method was developed for cardiac T measurement. Fast three-dimensional data acquisition was achieved with a spoiled gradient-echo sequence in combination with a stack-of-spirals trajectory and through-time non-Cartesian generalized autocalibrating partially parallel acquisition (GRAPPA) acceleration. The effects of different magnetic resonance parameters on T quantification with the proposed technique were first examined by simulating data acquisition and T map reconstruction using Bloch equation simulations. Accuracy was evaluated in studies with both phantoms and healthy subjects. These results showed that there was close agreement between the proposed technique and the reference method for a large range of T values in phantom experiments. In vivo studies further demonstrated that rapid cardiac T mapping for 12 three-dimensional partitions (spatial resolution, 2 × 2 × 8 mm ) could be achieved in a single breath-hold of ~12 s. The mean T values of myocardial tissue and blood obtained from normal volunteers at 3 T were 1311 ± 66 and 1890 ± 159 ms, respectively. In conclusion, a three-dimensional T mapping technique was developed using a non-Cartesian parallel imaging method, which enables fast and accurate T mapping of cardiac tissues in a single short breath-hold.