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A Comparsion Study of Ultrashort Echo Time Quantitative Susceptibility Mapping (UTE-QSM) with Different Sampling Trajectories1Institute of Electrical Engineering, Chinese Academy of Science, Beijing, China, 2Department of Radiology, University of California, San Diego, San Diego, CA, United States, 3Radiology Service, VA San Diego Healthcare System, San Diego, CA, United States
Synopsis
The ability to accurately and non-invasively quantify IONPs is desirable for many emerging applications, including for the evaluation of iron overload in the human body. 3D UTE Cones has demonstrated ability to detect high iron concentration with shorter echo times. In this study, we aimed to make clear whether the non-Cartesian sampling of Cones trajectory affects the accuracy of QSM. By comparing three different kinds of UTE sampling trajectory, as well as different stretch factors of Cones, the results show that no significant differences between these UTE QSM results were found.
Introduction
Iron is an essential element for life and is involved in many integral biologic processes. Iron overload can affect not only the central nervous system, but the liver, pancreas, myocardium, endocrine glands, and musculoskeletal structures, as well1. A reliable quantitative method to detect and measure high concentrations of iron in vivo would be of great clinical utility. Our previous preliminary study showed that 3D UTE Cones-QSM could increase the range of detection for iron concentration (up to 22 mM). Shorter first echo time and shorter echo spacing have been proven to be key factors contributing to increased detection range2. However, it is still unclear whether the non-Cartesian sampling essence of 3D UTE Cones will cause inaccuracy in QSM. Furthermore, 3D UTE-Cones with stretched spiral sampling has been shown to greatly reduce the total scan time without significant effect on the quantification of T1, T2*, T1r, and magnetization transfer (MT) modeling, but it is unclear whether extended sampling will negatively affect QSM results. In this study, three sampling strategies were compared: radial projection reconstruction (PR), Cones, and continuous Single Point Imaging (cSPI)3. Cones with different gradient stretch factors were also compared to investigate potential errors in UTE-QSM with extended sampling.Methods
An iron phantom was prepared with six tubes, each filled with 2 mL of six different concentrations of Feridex I.V. solution (Berlex Laboratories, Wayne, New Jersey, USA): 2, 6, 10, 14, 18, 22 mM. The tubes were placed in a cylinder container (10 cm in diameter) and filled with agarose gel (0.9% by weight) with the longitudinal direction of the tubes placed parallel to the B0 field.
To compare different non-Cartesian sampling trajectories for calculation of QSM, MEDI-based QSM was performed on a 3T GE MR750 scanner using three different UTE sequences: the 3D UTE-Cones sequence, the 3D UTE-PR sequence, and the 3D UTE-cSPI sequence, as shown in Figure 1(a,b,c). Figures 1a and 1b show 3D UTE Cones and PR sequences, which employ a short rectangular pulse excitation followed by 3D spiral and radial trajectories, respectively, with a conical view ordering. Figure 1-c shows the 3D UTE-cSPI sequence, where gradients are switched on, rapidly ramped up with maximum slew rate after RF excitation, ramped down, and switched off once the desired resolution is achieved3. After switching off the gradients, data are continuously acquired at a fixed k-space location. The scanning parameters are summarized in Table 1. To further study the effects of non-Cartesian sampling trajectory, UTE-Cones sequences with different stretch factors (SF) were also compared in this study. SF indicates a relative ratio of the length of k-space trajectory. UTE-Cones QSM with four SFs of 1.0 (default, acceleration factor (AF)=2.6 over PR sampling), 1.2 (AF=3.5), 1.4 (AF=4.1), and 1.6 (AF=5.0) were obtained, where the higher SF required smaller number of spokes to cover the 3D k-space and, therefore, required a shorter scan time. The other parameters were kept the same.
Each 3D UTE Cones acquisition was reconstructed into both magnitude and phase images using a re-gridding algorithm. Nominal TEs were used for QSM calculation. The MEDI QSM reconstruction algorithm4,5 was applied offline with the same complex matrix for measuring the susceptibility of each iron phantom. For all datasets, the regularization parameter λ and radius for the spherical mean value operator were kept as 500 and 5, respectively, for calculating magnetic susceptibility χ. User-defined regions of interest (ROIs) with fixed diameters of 1 cm were used to cover each tube.
Results
Figure 2 shows typical QSM results for the three UTE sequences with the same scale . As shown in Figure 2(d), excellent linearity was observed between iron concentration and QSM from all three different sequences. Figure 3 shows typical QSM results of Cones sequences with four different SFs. QSM of different SFs show almost the same results for most of the tubes, except the 22mM tube, where SF=1.0 had quite a higher QSM value than the other two SFs.Discussion and Conclusion
Results from this study show no significant difference in QSM using UTE acquisitions with radial, Cones, and cSPI sampling trajectories. Furthermore, UTE-Cones with SF up to 1.6 (5 times faster than 3D UTE-PR imaging) has minimal effect on QSM for iron concentration up to 18mM. Therefore, accelerated 3D UTE Cones-QSM sequences with high SFs could be used to reduce the scan time for clinical applications.Acknowledgements
The authors acknowledge grant support from NIH (1R01 AR062581 and 1R01 AR068987), VA Clinical Science Research and Development Service (Merit Award I01CX001388), National Natural Science Foundation of China (NSFC 51607169) and GE Healthcare.References
1. Taher AT, Musallam KM, Inati A. Iron overload: consequences, assessment, and monitoring. Hemoglobin. 2009; 33(S1):S46-57.
2. Lu X, Ma Y, Chang EY, He Q, Searleman A, von Drygalski A, Du J. Simultaneous quantitative susceptibility mapping (QSM) and R2* for high iron concentration quantification with 3D ultrashort echo time sequences: An echo dependence study. Magn Reson Med. 2018 Apr;79(4):2315-2322. doi: 10.1002/mrm.27062.
3. Jang H, Lu X, Carl M, et al.True phase quantitative susceptibility mapping using continuous single‐point imaging: a feasibility study.Magn Reson Med. 2018;00:1-8. https://doi.org/10.1002/mrm.27515
4. de Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, Wu J, Wang Y. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magn Reson Med 2010;63:194–206.
5. Liu T, Liu J, de Rochefort L, et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. Neuroimage 2012;59:2560–2568
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