Introduction

Cardiac T1 and T2 mapping have been shown to provide unique insights into cardiomyopathic tissue changes. T1 can be used to detect amyloid, inflammation and fat infiltration, while T2 is sensitive to edema ¹. T1ρ is sensitive to the slow motion of macromolecules within the lattice, and has shown the potential for diagnosing focal and diffuse myocardial fibrosis ^2,3. Simultaneous estimation of multiple parameters can provide complementary information for diagnosis and prognosis ⁴. A cardiac MR fingerprinting technique has been proposed for simultaneous T1, T2 and T1ρ mapping, which however requires complex reconstruction and a breath-hold of ~16s ⁵. Another relevant work is the Cartesian dictionary-based joint T1 and T2 mapping technique ⁶ which is also performed under breath-hold. In this study, we aim to propose a multi-parametric mapping sequence that can provide co-registered T1, T2, and T1ρ maps, where diaphragmatic navigator ^7,8 was adopted to correct through-plane respiratory motion and in-plane motion was corrected retrospectively. Quantitative parameters were obtained using dictionary matching to account for subjective heart rates and B1 inhomogeneities. Phantom and in vivo imaging experiments were performed to validate the proposed technique.

Methods

The framework of the proposed technique is shown in Fig. 1. The sequence consists of 16 single-shot acquisitions with a bSSFP readout and Cartesian sampling. Inversion recovery (IR), T2 preparation (T2-prep) and T1ρ preparation (T1ρ-prep) pulses are introduced to induce varying T1, T2, and T1ρ contrasts. The T1ρ-prep module consists of an adiabatic half-passage (AHP) tip-down pulse, a continuous-wave spin-lock pulse and a reverse AHP tip-up pulse (rAHP), which is designed to be robust to B1 and B0 inhomogeneities ⁹. Image acquisition is performed under free-breathing, and the imaging slice is adjusted in real time with the diaphragm navigator (dNav) to compensate for the through-plane respiratory motion ^7,8. To avoid the influence of IR on the dNav signal, a slice-selective IR (SSIR) is performed in the location of dNav along with IR. After the acquisition, the PCA-based groupwise registration is adopted to correct in-plane motion among the multi-contrast images¹⁰. For parameter quantification, dictionary matching is performed, where the dictionary is generated with Bloch simulations for a range of T1, T2, and T1ρ values as well as B1 factors to account for the inhomogeneous B1 fields. The T1 and T2 relaxations during the long AHP and rAHP pulses in the T1ρ prep are considered in the simulation.

Experiments

The proposed sequence was evaluated in phantoms made of different concentrations of agarose and Gadolinium-based contrast agent on a 3T United Imaging scanner. Reference T1, T2, and T1ρ values were respectively obtained using IR spin-echo (IRSE), multi-echo spin-echo (MESE), and T1ρ prep gradient echo (T1ρ-GRE) sequences ⁹. To test the influence of heart rates on the proposed technique, it was performed with simulated heart rates from 40 to 120bpm with a step of 20 bpm. The conventional MOLLI ¹¹, T2-prep bSSFP (T2-bSSFP) ¹², and T1ρ-prep bSSFP(T1ρ-bSSFP) ⁹ were also performed at 80 bpm for comparison. The imaging parameters were: FOV=320 × 280mm², in-plane resolution=2.02×1.82mm², thickness=8mm, TR/TE/flip angle=3.01ms/1.5ms/35°, bandwidth=1200 Hz/px, GRAPPA acceleration factor=2.
Six healthy subjects (age=24±2 years, 5 males) were imaged using the proposed technique at three short-axis slices with the same imaging parameters to the phantom imaging. The breath-hold MOLLI, T2-bSSFP and T1ρ-bSSFP were also performed for comparison. The American Heart Association’s (AHA) 16-segment model was adopted for segment-wise analysis ¹³. The mean and SD were calculated for each segment and all three acquired slices over all subjects.

Results

As the phantom results shown in Fig. 2, all the parameters estimated with the proposed sequence had a good agreement with the reference methods and showed robustness to the wide range of simulated heart rates, except that the short T1 values tend to be overestimated at low heart rates, which also leads to inaccurate estimation of T2 and T1ρ of that tube. Evaluating at heart rate of 80bpm, the conventional MOLLI, T2-bSSFP, and T1ρ-bSSFP had higher estimation errors than the proposed method.
Fig. 3 shows the mapping results of a representative subject. The proposed technique achieved similarly good mapping quality to the breath-hold techniques without obvious motion artifacts. The Bullseye plots of the mean and SD of the measurements averaged over the healthy subjects are shown in Fig. 4. The global T1, T2 and T1ρ values measured by the proposed method and the breath-hold techniques were respectively (mean ± SD): 1177±53ms vs. 1138±34ms, 40.1±2.7ms vs. 45.8±2.6ms, and 41.0±3.6ms vs. 51.3±3.9ms.

Discussion

The proposed technique provided T1, T2, and T1ρ maps simultaneously in a single acquisition of 16 heartbeats under free-breathing. Prospective through-plane and retrospective in-plane motion correction were performed to mitigate respiratory motion artifacts. Dictionary matching was performed to account for subjective heart rates and B1 inhomogeneities. The phantom results indicated better accuracy of the proposed technique than the conventional single-parameter mapping techniques. Similarly good mapping quality was observed for the proposed technique, compared with the conventional breath-hold methods, while the measured T1 was higher than MOLLI, and T2 and T1ρ were lower than the T2- and T1ρ-bSSFP.

Acknowledgements

No acknowledgement found.

References

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