Developing a metabolic clearance rate framework as a translational analysis approach for hyperpolarized 13C magnetic resonance imaging

Abstract

Hyperpolarized carbon-13 magnetic resonance imaging is a promising technique for in vivo metabolic interrogation of alterations between health and disease. This study introduces a formalism for quantifying the metabolic information in hyperpolarized imaging. This study investigated a novel perfusion formalism and metabolic clearance rate (MCR) model in pre-clinical stroke and in the healthy human brain. Simulations showed that the proposed model was robust to perturbations in T₁, transmit B₁, and k_PL. A significant difference in ipsilateral vs contralateral pyruvate derived cerebral blood flow (CBF) was detected in rats (140 ± 2 vs 89 ± 6 mL/100 g/min, p < 0.01, respectively) and pigs (139 ± 12 vs 95 ± 5 mL/100 g/min, p = 0.04, respectively), along with an increase in fractional metabolism (26 ± 5 vs 4 ± 2%, p < 0.01, respectively) in the rodent brain. In addition, a significant increase in ipsilateral vs contralateral MCR (0.034 ± 0.007 vs 0.017 ± 0.02/s, p = 0.03, respectively) and a decrease in mean transit time (31 ± 8 vs 60 ± 2 s, p = 0.04, respectively) was observed in the porcine brain. In conclusion, MCR mapping is a simple and robust approach to the post-processing of hyperpolarized magnetic resonance imaging.

Figure 1

An example overview of the processing used in this methodology. The graph on the left side of the figure demonstrates the change in relative T₁ or R₂^* observed in a DSC/DCE experiment. The graph on the left demonstrates the signal time course of [1-¹³C]pyruvate (blue) and subsequent exchange to [1-¹³C]lactate (brown) observed in a hyperpolarized experiment. The text shows the flow of data from each experiment for the Metabolic Clearance Rate formalism.

Figure 2

Example simulated metabolic curves for intracellular pyruvate (red) and lactate (black). T₁Pyruvate, T₁Lactate, TR, k_PL, and SNR are assumed to be 35 s, 30 s, 4 s, 0.012/s, and 100, respectively.

Figure 3

Simulation results demonstrating relative model stability down to SNR of 15 for all parameters. T₁pyruvate, k_PL, flip angle, and B₁ error are assumed to be 35 s, 0/s, 10°, and a factor of 1, respectively.

Figure 4

Simulation results demonstrating relative model stability for all parameters due to B₁₊ deviation. T₁pyruvate, k_PL, flip angle, and SNR are assumed to be 35 s, 0, 10°, and 100, respectively.

Figure 5

Simulation results demonstrating sensitivity of the model to k_PL. T₁pyruvate, B₁₊ deviation, flip angle, and B₁ error are assumed to be 35 s, 1, 10°, and 1, respectively.

Figure 6

Simulation results demonstrating model variation with pyruvate T1 across all parameters. kPL, flip angle, SNR, and B1 error are assumed to be 0, 10°, 100, and 1, respectively.

Figure 7

Simulation results showing the variation in FM (%) as both AIF/TIF and kPL are varied. SNR and B1 error are assumed to be 100, and 1, respectively. Generally, an increase in metabolism is observed as perfusion decreased and kPL increased.

Figure 8

Parametric mapping demonstrated in a rodent and procine models of stroke. The lesion (denoted by the yellow arrow on Apparent Diffusion Coefficient (A) and T₂ FLAIR weighted imaging (E), with CBV (B and F, respectively), CBF (C and G, respectively), and MCR (D and H, respectively). Also, parametric mapping of the healthy human brain is shown: T₂ FLAIR (I), CBV (J), CBF (K), and MCR (L).

Figure 9

Correlation results from rCBF analysis from the healthy human brain demonstrating a strong correlation between ASL rCBF and pyruvate rCBF.