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, k(PL) , a metabolic biomarker of cancer aggressiveness and progression. Therefore, it is crucial to evaluate k(PL) reliably. In this study, three sequence components and parameters that modulate k(PL) 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(PL) fitting. In vivo studies found a significantly higher apparent k(PL) with an additional gradient that leads to flow suppression (k(PL,FID-Delay,Crush) /k(PL,FID-Delay) = 1.37 ± 0.33, P < 0.01, N = 6), which agrees with simulation outcomes (12.5% k(PL) error with Δv = 40 cm/s), indicating that the gradients predominantly suppressed flowing pyruvate spins. Significantly lower k(PL) was found using a delayed free induction decay (FID) acquisition versus a minimum-T(E) version (k(PL,FID-Delay) /k(PL,FID) = 0.67 ± 0.09, P < 0.01, N = 5), and the lactate peak had broader linewidth than pyruvate (Δω(lactate) /Δω(pyruvate) = 1.32 ± 0.07, P < 0.000 01, N = 13). This illustrated that lactate's T(2) *, shorter than that of pyruvate, can affect calculated k(PL) values. We also found that an FID sequence yielded significantly lower k(PL) versus a double spin-echo sequence that includes spin-echo spoiling, flow suppression from crusher gradients, and more T(2) weighting (k(PL,DSE) /k(PL,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 k(PL) . The data acquisition and analysis pipelines can work synergistically to provide more robust and reproducible k(PL) measures for future preclinical and clinical studies.