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

Hyperpolarized 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, k , a metabolic biomarker of cancer aggressiveness and progression. Therefore, it is crucial to evaluate k reliably. In this study, three sequence components and parameters that modulate k 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 k fitting. In vivo studies found a significantly higher apparent k with an additional gradient that leads to flow suppression (k /k  = 1.37 ± 0.33, P < 0.01, N = 6), which agrees with simulation outcomes (12.5% k error with Δv = 40 cm/s), indicating that the gradients predominantly suppressed flowing pyruvate spins. Significantly lower k was found using a delayed free induction decay (FID) acquisition versus a minimum-T version (k /k  = 0.67 ± 0.09, P < 0.01, N = 5), and the lactate peak had broader linewidth than pyruvate (Δω /Δω  = 1.32 ± 0.07, P < 0.000 01, N = 13). This illustrated that lactate's T *, shorter than that of pyruvate, can affect calculated k values. We also found that an FID sequence yielded significantly lower k versus a double spin-echo sequence that includes spin-echo spoiling, flow suppression from crusher gradients, and more T weighting (k /k  = 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 k . The data acquisition and analysis pipelines can work synergistically to provide more robust and reproducible k measures for future preclinical and clinical studies.