Alterations in metabolism following radiotherapy affect therapeutic efficacy, although the mechanism underlying such alterations is unclear. A new imaging technique—named dynamic nuclear polarization (DNP) carbon-13 magnetic resonance imaging (MRI)—probes the glycolytic flux in a real-time, dynamic manner. The [1-¹³C]pyruvate is transported by the monocarboxylate transporter (MCT) into cells and converted into [1-¹³C]lactate by lactate dehydrogenase (LDH). To capture the early glycolytic alterations in the irradiated cancer and immune cells, we designed a preliminary DNP ¹³C-MRI study by using hyperpolarized [1-¹³C]pyruvate to study human FaDu squamous carcinoma cells, HMC3 microglial cells, and THP-1 monocytes before and after irradiation. The pyruvate-to-lactate conversion rate (k_PL [Pyr.]) calculated by kinetic modeling was used to evaluate the metabolic alterations. Western blotting was performed to assess the expressions of LDHA, LDHB, MCT1, and MCT4 proteins. Following irradiation, the pyruvate-to-lactate conversion rates on DNP ¹³C-MRI were significantly decreased in the FaDu and the HMC3 cells but increased in the THP-1 cells. Western blot analysis confirmed the similar trends in LDHA and LDHB expression levels. In conclusion, DNP ¹³C-MRI non-invasively captured the different glycolytic alterations among cancer and immune systems in response to irradiation, implying its potential for clinical use in the future.

Hyperpolarized (HP) magnetic resonance imaging (MRI) is a groundbreaking imaging platform advancing from research to clinical practice, offering new possibilities for real-time, non-invasive metabolic imaging. This review explores the latest advancements, challenges, and future directions of HP MRI, emphasizing its transformative impact on both translational research and clinical applications. By employing techniques such as dissolution Dynamic Nuclear Polarization (dDNP), Parahydrogen-Induced Polarization (PHIP), Signal Amplification by Reversible Exchange (SABRE), and Spin-Exchange Optical Pumping (SEOP), HP MRI achieves enhanced nuclear spin polarization, enabling in vivo visualization of metabolic pathways with exceptional sensitivity. Current challenges, such as limited imaging windows, complex pre-scan protocols, and data processing difficulties, are addressed through innovative solutions like advanced pulse sequences, bolus tracking, and kinetic modeling. We highlight the evolution of HP MRI technology, focusing on its potential to revolutionize disease diagnosis and monitoring by revealing metabolic processes beyond the reach of conventional MRI and positron emission tomography (PET). Key advancements include the development of novel tracers like [2-13C]pyruvate and [1-13C]-alpha-ketoglutarate and improved data analysis techniques, broadening the scope of clinical metabolic imaging. Future prospects emphasize integrating artificial intelligence, standardizing imaging protocols, and developing new hyperpolarized agents to enhance reproducibility and expand clinical capabilities particularly in oncology, cardiology, and neurology. Ultimately, we envisioned HP MRI as a standardized modality for dynamic metabolic imaging in clinical practice.

Lai YC, et al. Korean J Radiol. 2024 May;25(5):459-472. https://doi.org/10.3348/kjr.2024.0069

We aim to assess the additional value of diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) for the risk stratification of sonographically indeterminate ovarian neoplasms. A total of 21 patients with diagnosed adnexal masses between 2014 and 2017 were divided into malignant (four serous cystadenocarcinomas, four endometrioid carcinomas, three clear cell carcinomas, and one carcinosarcoma) and benign (four cystadenomas, two teratomas, one fibroma, one endometrioma, and one corpus luteal cyst) groups. An apparent diffusion coefficient (ADC) value of 1.27 × 10⁻³ mm²/s was considered as the optimal threshold in distinguishing malignant from benign ovarian tumors (sensitivity and specificity: 100% and 77.8%, respectively). Choline peaks were detected in six of seven O-RADS (Ovarian-Adnexal Imaging-Reporting Data System) 4 lesions and corrected all of the DWI false-negative clear cell carcinoma. Based on the presence of the choline peaks, the diagnostic performance of MRS showed a sensitivity of 77.8%, a specificity of 100%, and an accuracy of 85.7%, respectively. In conclusion, MRS could potentially play a complementary role for DWI in tumor characterization, particularly for O-RADS 4 tumors or clear cell carcinomas.

Hyperpolarized carbon-13 MRI has the advantage of allowing the study of glycolytic flow in vivo or in vitro dynamically in real-time. The apparent exchange rate constant of a metabolite dynamic signal reflects the metabolite changes of a disease. Downstream metabolites can have a low signal-to-noise ratio (SNR), causing apparent exchange rate constant inconsistencies. Thus, we developed a method that estimates a more accurate metabolite signal. This method utilizes a kinetic model and background noise to estimate metabolite signals. Simulations and in vitro studies with photon-irradiated and control groups were used to evaluate the procedure. Simulated and in vitro exchange rate constants estimated using our method were compared with the raw signal values. In vitro data were also compared to the Area-Under-Curve (AUC) of the cell medium in ¹³C Nuclear Magnetic Resonance (NMR). In the simulations and in vitro experiments, our technique minimized metabolite signal fluctuations and maintained reliable apparent exchange rate constants. In addition, the apparent exchange rate constants of the metabolites showed differences between the irradiation and control groups after using our method. Comparing the in vitro results obtained using our method and NMR, both solutions showed consistency when uncertainty was considered, demonstrating that our method can accurately measure metabolite signals and show how glycolytic flow changes. The method enhanced the signals of the metabolites and clarified the metabolic phenotyping of tumor cells, which could benefit personalized health care and patient stratification in the future.