3D compressed sensing for highly accelerated hyperpolarized (13)C MRSI with in vivo applications to transgenic mouse models of cancer.

High polarization of nuclear spins in liquid state through hyperpolarized technology utilizing dynamic nuclear polarization has enabled the direct monitoring of 13C metabolites in vivo at a high signal-to-noise ratio (SNR) (1,2). Metabolic imaging studies thus far have focused on injecting hyperpolarized [1-13C]pyruvate and observing its uptake and conversion to [1-13C]lactate, [1-13C]alanine, 13C-bicarbonate, and 13CO2 (3–11). These studies have shown the potential of hyperpolarized technology for the metabolic imaging of cardiac disease (4,10,11) and cancer (5,7,8,12). The hyperpolarized cancer studies have demonstrated that in tumors the lactate dehydrogenase–catalyzed reaction involving pyruvate and lactate results in dramatically higher levels of hyperpolarized 13C lactate than in normal tissue (5,7,8,12). Mechanistically, lactate dehydrogenase catalyzes rapid exchange of the 13C label between pyruvate and lactate; thus, detected 13C lactate likely reflects the size of the preexisting endogenous lactate pool and lactate dehydrogenase expression (8). Hyperpolarization of clinically relevant substrates other than [1-13C]pyruvate is also an active area of research. Promising results have been reported for the hyperpolarization of [1-13C]lactate (13), [5-13C]glutamine (14), and 13C bicarbonate (15). Regardless of the specific in vivo application or substrate being hyperpolarized, the overriding limitation in dynamic nuclear polarization hyperpolarized spectroscopic imaging is the rapid decay of the hyperpolarized signal, which decreases exponentially with the spin-lattice T1 of the 13C nucleus (on the order of 30 to 70 sec for most substrates polarized). The hyperpolarized signal is also irretrievably lost due to saturation from radiofrequency excitation. These factors severely limit the number of phase encodes and thus the spatial resolution/coverage achievable, making accelerated imaging methods very important. Our approach in this project was to apply compressed sensing, a recently developed technique based on random undersampling and a nonlinear reconstruction (16– 19). Compressed sensing works well when underlying signals exhibit sparsity and have adequate SNR but may fail if those conditions are not met (20). The key design challenge in implementing compressed sensing for specific MRI applications is the development of pulse sequences and acquisition schemes that incorporate “incoherent” sampling, which is achieved by pseudorandom undersampling (20). In a prior study, an initial design incorporated pseudorandom undersampling in one spatial dimension into a spectroscopic imaging sequence to achieve 2-fold acceleration for 2-fold finer spatial resolution without increasing scan time (21). In this project, we advanced this compressed sensing methodology to two spatial dimensions and developed a 7.53-fold accelerated sequence for three-dimensional (3D) spectroscopic imaging, which was investigated through simulations, phantom testing, and in vivo experiments. In simulations, the limits of undersampling and the influence of noise on the compressed sensing nonlinear reconstruction were explored. We also applied this technique for hyperpolarized 13C imaging research in transgenic mice that express human oncogenes and develop liver cancer, a new animal model in which the higher spatial resolution is particularly beneficial.