Yan Li

Alpha-ketoglutarate (aKG) is a central intermediate of cerebral energy metabolism and a precursor for glutamate synthesis in the brain. Alterations in aKG metabolism occur in pathological contexts, including isocitrate dehydrogenase (IDH) mutant astrocytomas and oligodendrogliomas, in which mutant IDH converts aKG to the oncometabolite 2-hydroxyglutarate. Given its central role in brain metabolism, non-invasive interrogation of aKG-dependent metabolic flux is needed. Hyperpolarized (HP) ¹³C MR enables real-time visualization of metabolic conversion by transiently enhancing signal intensity by several orders of magnitude. Leveraging this approach, we report the first-in-human feasibility and safety study of HP [1-¹³C]aKG MR spectroscopy in the healthy brain (n = 3). A standard operating procedure (SOP) was developed for sterile [1-¹³C]aKG dose production, achieving reproducible polarization levels averaging 30.5 ± 2.2%. Following intravenous administration, time-resolved ¹³C spectra in healthy volunteers demonstrated the detection of HP aKG resonance and a measurable downstream glutamate signal, consistent across repeat acquisitions, with a delayed temporal profile relative to aKG observed in a representative dataset. Although performed in healthy volunteers, these results establish feasibility for HP [1-¹³C]aKG metabolic imaging to open a new window into normal and pathological brain cellular metabolism.

Hyperpolarized (HP) [1- ¹³ C]pyruvate MRI can noninvasively detect dynamic metabolic activity in the brain. This study utilizes HP-[1- ¹³ C]pyruvate MRI to monitor treatment-related metabolism and evaluate early therapeutic response in twenty patients with recurrent grade 4 glioma undergoing cytotoxic, antiangiogenic, and targeted chemotherapies. Metabolic changes within the T2-lesion and normal-appearing white matter were compared among patients with similar treatments and/or outcomes. The lactate/pyruvate ratio increased by 14.3% at 1 month in the group whose therapy included bevacizumab, while it decreased by 13.1% at 2 months in the normal-appearing white matter of patients on everolimus. In the T2-lesion, patients on bevacizumab showed a 24.3% increase, whereas patients on everolimus showed a 7.6% decrease in normalized lactate/pyruvate ratios. Patients with shorter 6-month progression-free survival showed an average 9.6% increase in the lactate/pyruvate ratio as early as 1 month after treatment. This study demonstrates the potential of real-time metabolic imaging for response monitoring in patients with glioma.

Hyperpolarized carbon-13 (HP-¹³C) MRI enables the real-time measurement of dynamic metabolism by utilizing molecular probes whose magnetization has been transiently enhanced via dynamic nuclear polarization of ¹³C labels. Based on pre-clinical and clinical investigations demonstrating Warburg-related metabolic dysfunction and tricarboxylic acid (TCA)-cycle alterations in gliomas, HP¹³C techniques appear very promising for overcoming conventional challenges to evaluating tumor burden and extent, early therapeutic response and progression among patients non-invasively. This article surveys the multi-faceted translational development of HP-¹³C MRI in the context of glioma imaging, while emphasizing innovation concerning the pharmacy production of HP probes - [1-¹³C]/[2-¹³C]-pyruvate and [1-¹³C,5-¹²C]-alpha-ketoglutarate - that serve as non-radioactive metabolic contrast agents. Borrowing from practical experience, we present specific probe indications for isocitrate dehydrogenase (IDH)-wildtype glioblastomas and IDH-mutant gliomas together with example data to show the targeted, pathway-dependent function of these agents and their utility. Additional information pertaining to HP-¹³C hardware, acquisition and post-processing techniques provides an overview of the imaging methodology as it is currently performed at a leading institution. Considering the developing markers for progressive disease in glioblastomas and rapidly advancing capability, this unique imaging technology appears poised for translational impact following further evaluation.ABBREVIATIONS: 2-HG = D-2-hydroxyglutarate; α-KG = alpha-ketoglutarate; CSI = chemical shift imaging; d-DNP = dissolution-dynamic nuclear polarization; EPI = echo-planar imaging; EPSI = echo-planar spectroscopic imaging; IDHm = isocitrate dehydrogenasemutant; IR-SPGR = inversion recovery-prepared fast spoiled gradient-recalled (imaging); LrGGs = lower-grade gliomas; NMR = nuclear magnetic resonance; OGC = oxoglutarate carrier; PHIP = parahydrogen-induced polarization; SNR = signal-to-noise ratio; TCA cycle = tricarboxylic acid cycle; WHO = World Health Organization.

Summary: Realistic, in vivo-like synthetic data is increasingly needed to develop and validate methods in magnetic resonance spectroscopy. MRS-Sim is a powerful, open-source framework for simulating such data while providing known ground truth values. Its modularity enables modeling the complexities of MRS data for various in vivo scenarios. The underlying physical equations include both commonly used spectral components of linear-combination fitting routines and two novel components. The first is a 3D B ₀ field map simulator that models B ₀ field inhomogeneities, ranging from slight variations to severe distortions. The second is a novel semi-parametric generator that mimics signals from poorly characterized residual water regions and spectral baseline contributions. This framework can simulate scenarios ranging from raw multi-coil transients to preprocessed, coil-combined multi-average data. Simulating realistic in vivo-like datasets requires appropriate model parameter ranges and distributions, best determined by analyzing the fitting parameters from existing in vivo data. Therefore, MRS-Sim includes tools for analyzing the ranges and statistical distributions of those parameters from in vivo datasets fitted with Osprey, allowing simulations to be tailored to specific datasets. Additionally, the accompanying repository of supplemental information assists non-expert users with general simulations of MRS data. The modularity of this framework facilitates easy customization various in vivo scenarios and promotes continued community development. Using a single framework for diverse applications addresses the inconsistencies in current protocols. By simulating in vivo-like data, MRS-Sim supports many MRS tasks, including verifying spectral fitting protocols and conducting reproducibility analyses. Readily available synthetic data also benefits deep learning research, particularly when sufficient in vivo data is unavailable for training. Overall, MRS-Sim will promote reproducibility and make MRS research more accessible to a wider audience.

Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed (n = 208) and treated (n = 221) gliomas in training, applying transfer learning (TL) from pre- to post-treatment imaging domains, and incorporating spatial regularization for T2-lesion segmentation using only T2 FLAIR images as input to improve generalization post-treatment. These approaches were evaluated on 24 patients suspected of progression who had received prior treatment. Including 26% of treated patients in training improved performance by 13.9%, and including more treated and untreated patients resulted in minimal changes. Fine-tuning with treated glioma improved sensitivity compared to data mixing by 2.5% (p < 0.05), and spatial regularization further improved performance when used with TL by 95th HD, Dice, and sensitivity (6.8%, 0.8%, 2.2%; p < 0.05). While training with ≥60 treated patients yielded the majority of performance gain, TL and spatial regularization further improved T2-lesion segmentation to treated gliomas using a single MR contrast and minimal processing, demonstrating clinical utility in response assessment.

This study aimed to implement a multimodal ¹H/HP-¹³C imaging protocol to augment the serial monitoring of patients with glioma, while simultaneously pursuing methods for improving the robustness of HP-¹³C metabolic data. A total of 100 ¹H/HP [1-¹³C]-pyruvate MR examinations (104 HP-¹³C datasets) were acquired from 42 patients according to the comprehensive multimodal glioma imaging protocol. Serial data coverage, accuracy of frequency reference, and acquisition delay were evaluated using a mixed-effects model to account for multiple exams per patient. Serial atlas-based HP-¹³C MRI demonstrated consistency in volumetric coverage measured by inter-exam dice coefficients (0.977 ± 0.008, mean ± SD; four patients/11 exams). The atlas-derived prescription provided significantly improved data quality compared to manually prescribed acquisitions (n = 26/78; p = 0.04). The water-based method for referencing [1-¹³C]-pyruvate center frequency significantly reduced off-resonance excitation relative to the coil-embedded [¹³C]-urea phantom (4.1 ± 3.7 Hz vs. 9.9 ± 10.7 Hz; p = 0.0007). Significantly improved capture of tracer inflow was achieved with the 2-s versus 5-s HP-¹³C MRI acquisition delay (p = 0.007). This study demonstrated the implementation of a comprehensive multimodal ¹H/HP-¹³C MR protocol emphasizing the monitoring of steady-state/dynamic metabolism in patients with glioma.

Purpose: To investigate high‐resolution hyperpolarized (HP) ¹³C pyruvate MRI for measuring cerebral perfusion in the human brain.Methods: HP [1‐¹³C]pyruvate MRI was acquired in five healthy volunteers with a multi‐resolution EPI sequence with 7.5 × 7.5 mm² resolution for pyruvate. Perfusion parameters were calculated from pyruvate MRI using block‐circulant singular value decomposition and compared to relative cerebral blood flow calculated from arterial spin labeling (ASL). To examine regional perfusion patterns, correlations between pyruvate and ASL perfusion were performed for whole brain, gray matter, and white matter voxels.Results: High resolution 7.5 × 7.5 mm² pyruvate images were used to obtain relative cerebral blood flow (rCBF) values that were significantly positively correlated with ASL rCBF values (r = 0.48, 0.20, 0.28 for whole brain, gray matter, and white matter voxels respectively). Whole brain voxels exhibited the highest correlation between pyruvate and ASL perfusion, and there were distinct regional patterns of relatively high ASL and low pyruvate normalized rCBF found across subjects.Conclusion: Acquiring HP ¹³C pyruvate metabolic images at higher resolution allows for finer spatial delineation of brain structures and can be used to obtain cerebral perfusion parameters. Pyruvate perfusion parameters were positively correlated to proton ASL perfusion values, indicating a relationship between the two perfusion measures. This HP ¹³C study demonstrated that hyperpolarized pyruvate MRI can assess cerebral metabolism and perfusion within the same study.

Purpose: While 3D MR spectroscopic imaging (MRSI) provides valuable spatial metabolic information, one of the hurdles for clinical translation is its interpretation, with voxel-wise quality control (QC) as an essential and the most time-consuming step. This work evaluates the accuracy of machine learning (ML) models for automated QC filtering of individual spectra from 3D healthy control and patient datasets.Methods: A total of 53 3D MRSI datasets from prior studies (30 neurological diseases, 13 brain tumors, and 10 healthy controls) were included in the study. Three ML models were evaluated: a random forest classifier (RF), a convolutional neural network (CNN), and an inception CNN (ICNN) along with two hybrid models: CNN + RF, ICNN + RF. QC labels used for training were determined manually through consensus of two MRSI experts. Normalized and cropped real-valued spectra was used as input. A cross-validation approach was used to separate datasets into training/validation/testing sets of aggregated voxels.Results: All models achieved a minimum AUC of 0.964 and accuracy of 0.910. In datasets from neurological disease and controls, the CNN model produced the highest AUC (0.982), while the RF model achieved the highest AUC in patients with brain tumors (0.976). Within tumor lesions, which typically exhibit abnormal metabolism, the CNN AUC was 0.973 while that of the RF was 0.969. Data quality inference times were on the order of seconds for an entire 3D dataset, offering drastic time reduction compared to manual labeling.Conclusion: ML methods accurately and rapidly performed automated QC. Results in tumors highlights the applicability to a variety of metabolic conditions.

Background: This study aimed to develop a time-efficient method of acquiring simultaneous, dual-slice MR spectroscopic imaging (MRSI) for the evaluation of brain metabolism. Methods: Adaptive Hadamard-encoded pulses were developed and integrated with atlas-based automatic prescription. The excitation profiles were evaluated via simulation, phantom and volunteer experiments. The feasibility of γ-aminobutyric acid (GABA)-edited dual-slice MRSI was also assessed. Results: The signal between slices in the dual-band MRSI was less than 1% of the slice profiles. Data from a homemade phantom containing separate, interfacing compartments of creatine and acetate solutions demonstrated ~0.4% acetate signal contamination relative to the amplitude in the excited creatine compartment. The normalized signal-to-noise ratios from atlas-based acquisitions in volunteers were found to be comparable between dual-slice, Hadamard-encoded MRSI and 3D acquisitions. The mean and standard deviation of the coefficients of variation for NAA/Cho from the repeated volunteer scans were 8.2% ± 0.8% and 10.1% ± 3.7% in the top and bottom slices, respectively. GABA-edited, dual-slice MRSI demonstrated simultaneous detection of signals from GABA and coedited macromolecules (GABA+) from both superior grey and deep grey regions of volunteers. Conclusion: This study demonstrated a fully automated dual-slice MRSI acquisition using atlas-based automatic prescription and adaptive Hadamard-encoded pulses.