Purpose Fluorescence lifetime imaging microscopy (FLIM) of endogenous fluorescent metabolites permits the measurement of cellular metabolism in cell, tissue and animal models. In parallel, magnetic resonance spectroscopy (MRS) of dynamic nuclear (hyper)polarized (DNP) 13C‐pyruvate enables measurement of metabolism at larger in vivo scales. Presented here are the design and initial application of a bioreactor that connects these 2 metabolic imaging modalities in vitro, using 3D cell cultures. Methods The model fitting for FLIM data analysis and the theory behind a model for the diffusion of pyruvate into a collagen gel are detailed. The device is MRI‐compatible, including an optical window, a temperature control system and an injection port for the introduction of contrast agents. Three‐dimensional printing, computer numerical control machining and laser cutting were used to fabricate custom parts. Results Performance of the bioreactor is demonstrated for 4 T1 murine breast cancer cells under glucose deprivation. Mean nicotinamide adenine dinucleotide (NADH) fluorescence lifetimes were 10% longer and hyperpolarized 13C lactate:pyruvate (Lac:Pyr) ratios were 60% lower for glucose‐deprived 4 T1 cells compared to 4 T1 cells in normal medium. Looking at the individual components of the NADH fluorescent lifetime, τ1 (free NADH) showed no significant change, while τ2 (bound NADH) showed a significant increase, suggesting that the increase in mean lifetime was due to a change in bound NADH. Conclusion A novel bioreactor that is compatible with, and can exploit the benefits of, both FLIM and 13C MRS in 3D cell cultures for studies of cell metabolism has been designed and applied.

This study uses dynamic hyperpolarized [1-13C]pyruvate magnetic resonance spectroscopic imaging (MRSI) to estimate differences in glycolytic metabolism between highly metastatic (4T1, n = 7) and metastatically dormant (4T07, n = 7) murine breast cancer models. The apparent conversion rate of pyruvate-to-lactate (kPL) and lactate-to-pyruvate area-under-the-curve ratio (AUCL/P) were estimated from the metabolite images and compared with biochemical metabolic measures and immunohistochemistry (IHC). A non-significant trend of increasing kPL (p = 0.17) and AUCL/P (p = 0.11) from 4T07 to 4T1 tumors was observed. No significant differences in tumor IHC lactate dehydrogenase-A (LDHA), monocarboxylate transporter-1 (MCT1), cluster of differentiation 31 (CD31), and hypoxia inducible factor-α (HIF-1α), tumor lactate-dehydrogenase (LDH) activity, or blood lactate or glucose levels were found between the two tumor lines. However, AUCL/P was significantly correlated with tumor LDH activity (ρspearman = 0.621, p = 0.027) and blood glucose levels (ρspearman = −0.474, p = 0.042). kPL displayed a similar, non-significant trend for LDH activity (ρspearman = 0.480, p = 0.114) and blood glucose levels (ρspearman = −0.414, p = 0.088). Neither kPL nor AUCL/P were significantly correlated with blood lactate levels or tumor LDHA or MCT1. The significant positive correlation between AUCL/P and tumor LDH activity indicates the potential of AUCL/P as a biomarker of glycolytic metabolism in breast cancer models. However, the lack of a significant difference between in vivo tumor metabolism for the two models suggest similar pyruvate-to-lactate conversion despite differing metastatic potential.

Purpose To introduce proton density water fraction (PDWF) as a confounder‐corrected (CC) MR‐based biomarker of mammographic breast density, a known risk factor for breast cancer. Methods Chemical shift encoded (CSE) MR images were acquired using a low flip angle to provide proton density contrast from multiple echo times. Fat and water images, corrected for known biases, were produced by a six‐echo CC CSE‐MRI algorithm. Fibroglandular tissue (FGT) volume was calculated from whole‐breast segmented PDWF maps at 1.5T and 3T. The method was evaluated in (1) a physical fat‐water phantom and (2) normal volunteers. Results from two‐ and three‐echo CSE‐MRI methods were included for comparison. Results Six‐echo CC‐CSE‐MRI produced unbiased estimates of the total water volume in the phantom (mean bias 3.3%) and was reproducible across protocol changes (repeatability coefficient [RC] = 14.8 cm3 and 13.97 cm3 at 1.5T and 3.0T, respectively) and field strengths (RC = 51.7 cm3) in volunteers, while the two‐ and three‐echo CSE‐MRI approaches produced biased results in phantoms (mean bias 30.7% and 10.4%) that was less reproducible across field strengths in volunteers (RC = 82.3 cm3 and 126.3 cm3). Significant differences in measured FGT volume were found between the six‐echo CC‐CSE‐MRI and the two‐ and three‐echo CSE‐MRI approaches (p = 0.002 and p = 0.001, respectively). Conclusion The use of six‐echo CC‐CSE‐MRI to create unbiased PDWF maps that reproducibly quantify FGT in the breast is demonstrated. Further studies are needed to correlate this quantitative MR biomarker for breast density with mammography and overall risk for breast cancer.

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