Pulse sequence considerations for quantification of pyruvate-to-lactate conversion kPL in hyperpolarized 13 C imaging.

ABSTRACT

Hyperpolarized 13 C MRI takes advantage of the unprecedented 50 000-fold signal-to-noise ratio enhancement to interrogate cancer metabolism in patients and animals. It can measure the pyruvate-to-lactate conversion rate, kPL , a metabolic biomarker of cancer aggressiveness and progression. Therefore, it is crucial to evaluate kPL reliably. In this study, three sequence components and parameters that modulate kPL estimation were identified and investigated in model simulations and through in vivo animal studies using several specifically designed pulse sequences. These factors included a magnetization spoiling effect due to RF pulses, a crusher gradient-induced flow suppression, and intrinsic image weightings due to relaxation. Simulation showed that the RF-induced magnetization spoiling can be substantially improved using an inputless kPL fitting. In vivo studies found a significantly higher apparent kPL with an additional gradient that leads to flow suppression (kPL,FID-Delay,Crush /kPL,FID-Delay  = 1.37 ± 0.33, P < 0.01, N = 6), which agrees with simulation outcomes (12.5% kPL error with Δv = 40 cm/s), indicating that the gradients predominantly suppressed flowing pyruvate spins. Significantly lower kPL was found using a delayed free induction decay (FID) acquisition versus a minimum-TE version (kPL,FID-Delay /kPL,FID  = 0.67 ± 0.09, P < 0.01, N = 5), and the lactate peak had broader linewidth than pyruvate (Δωlactate /Δωpyruvate  = 1.32 ± 0.07, P < 0.000 01, N = 13). This illustrated that lactate's T2 *, shorter than that of pyruvate, can affect calculated kPL values. We also found that an FID sequence yielded significantly lower kPL versus a double spin-echo sequence that includes spin-echo spoiling, flow suppression from crusher gradients, and more T2 weighting (kPL,DSE /kPL,FID  = 2.40 ± 0.98, P < 0.0001, N = 7). In summary, the pulse sequence, as well as its interaction with pharmacokinetics and the tissue microenvironment, can impact and be optimized for the measurement of kPL . The data acquisition and analysis pipelines can work synergistically to provide more robust and reproducible kPL measures for future preclinical and clinical studies.