shows simulated pulse train performance results in the presence of relaxation and diffusion. A primary tradeoff in the super-STE is between the pulse train length and T2 decay. Using longer pulse trains better approximates a square-wave and preserves more magnetization, but there is also increased T2 decay which can negate the improvements in magnetization preservation. For ΔT/T2 > 0.5 with an SLR train design as shown in (), there is less than 10% gain by using a super-STE. The gain is much greater for smaller ΔT/T2. For example, at ΔT/T2 = 0.1, going up to 18 pulses in the train still provided improved signal.
Fig. 4 Simulated Menc preserved for SLR super-STE designs with various numbers of pulses. (T1 = ∞. N = 2 is conventional 90°-90° STE.) (A) As the pulse train length increases, T2 decay can outweigh the improved encoding efficiency. (B) (more ...)
Another tradeoff in the super-STE pulse train design is the diffusion-weighting, which can be represented by a sum of exponentials (Eq. 21
). Simulation results for isotropic diffusion using an EPG are shown in . (The anisotropic tensor response can be derived as in [34
]). Most notably, the longer pulse trains have generally more rapid signal loss as a function of D
and a clearly different shape from the STE. See the Appendix
for an analysis of the trade-offs of T2
decay and diffusion-weighting for typical relaxation rates, as well as a comparison to spin-echo diffusion.
Phantom tests () showed that the super-STE encoding, with a gapped SLR and sech inversion pulse train design, had improved SNR over the STE for B1 ±20% the nominal amplitude. Using the STEP approach resulted in further SNR improvements over STE and super-STE encoding alone. The sech pulses had better B1 performance than the SLR pulses, as expected. However, at the desired B1, the SLR pulses performed slightly better, which was expected based on our difficulty optimizing the sech design. The sech profiles are slightly assymetric because these pulse trains are partially B1-insensitive above, but not below, the sech adiabatic threshold (also shown in ). Additional shifts between the simulated and measured profiles are a result of slight RF pulse power miscalibrations. Eddy currents, which accumulate constructively throughout the pulse train and are a common problem in diffusion-weighting imaging, and vibrational motion from the multiple gradient lobes could cause signal loss with in the super-STE pulse trains. Additional phantom experiments using maximal gradient strengths on all axes (not shown) caused up to 25% signal losses due to eddy currents and vibration.
Fig. 5 Phantom tests of the B1 response for STE and super-STE. (A) Comparison for various STEAM encoding schemes (). (B) Comparison for various STEP schemes (). The dashed lines are simulated profiles and X’s are acquired data, corrected (more ...)
We compared a conventional STEAM () and super-STEAM encoding () approaches in vivo () and found an average voxel-wise signal increase of 27% (29% pyr, 24% ala, 21% lac), which was statistically significant (p < .01) for each metabolite. Based on simulations, we predicted a 27.3% signal increase, which is in close agreement considering the potential physiologic and polarization differences. The metabolite amplitudes were also highly correlated (R2 = 0.75 for linear fit across all metabolites), indicating there was no difference in contrast between the two approaches. We also compared STEAM () and STEP () approaches (), and found an average voxel-wise increase in signal of 49% (66% pyr, 25% ala, 44% lac), which was statistically significant (p < .01) for each metabolite. A 60% signal increase was predicted based on simulations. There was a slightly weaker correlation (R2 = 0.70 for linear fit across all metabolites) and also more variation between metabolites. This variability between experiments is likely due to diffusion sensitivity that accumulates during the STEAM MRSI acquisition versus the STEP, which freezes the diffusion weighting prior to the acquisition.
Fig. 6 In vivo comparison between conventional and super STE methods. (A) Metabolite amplitudes for conventional STEAM () and super-STEAM encoding (, sech 180°, ΔT = 1.65ms, N = 12), normalized by the measured polarization, in a (more ...)
When applied to a transgenic prostate cancer model (TRAMP), the STEP approach improved the contrast for tumor lactate ( and ), which is a putative cancer biomarker. In the color overlays (), some high lactate in the gut is suppressed and the tumor lactate is the largest metabolite in the STEP. In this acquisition, the encoding strength (beff = 119.4 s/mm2) and mixing time (TM = 1 s) were chosen such that they would suppress flowing metabolites in both the vasculature and microvasculature. The improved tumor lactate delineation is reflected by the significant increase in both the lactate to pyruvate ratio within the tumors () and ratio of peak tumor lactate to peak kidney/liver lactate (). This indicates that the lactate observed in vivo in kidney and liver tissue may have had a larger vascular fraction than the lactate within tumor tissue. The pyruvate was the most attenuated of all metabolites, which implies that it is more concentrated in the vasculature compared to the lactate and alanine observed.
Representative TRAMP 13C data overlays with and without a super-STEP (beff = 119.4 s/mm2). The STEP highlights the lactate in the tumor.
Fig. 8 (A) The average metabolite amplitudes across several organs in normal (N = 4) and transgenic prostate tumor mice (N = 4) show how the distribution is affected by the super-STEP (beff = 119.4 s/mm2). The tumor lactate was the largest metabolite signal (more ...)
Initial results in a liver tumor model have also shown similar improvements in tumor lactate contrast (, circles in ). The tumor had elevated lactate with no preparation pulses, but the delineation improved with super-STEP. Alanine also showed improved delineation of the tumor in this animal. The pyruvate distributions were relatively similar. The perfusion agent 13
] was polarized and imaged simultaneously in this study, and also showed better localization to the tumor with the STEP. This indicates the urea was better perfused into the tumor tissue than elsewhere in the liver.
Fig. 9 Preliminary liver tumor model 13C data overlays with and without a super-STEP (beff = 119.4 s/mm2). (Color scale is same as .) In this experiment, 13C-urea was co-polarized with [1-13C]-pyruvate and both compounds were injected. As in the TRAMP, (more ...)