Search

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.

PurposeTo investigate high-resolution hyperpolarized (HP) 13 C pyruvate MRI for measuring cerebral perfusion in the human brain.MethodsHP [1-13 C]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.ResultsHigh 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.ConclusionAcquiring HP 13 C 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 13 C study demonstrated that hyperpolarized pyruvate MRI can assess cerebral metabolism and perfusion within the same study.

BackgroundDynamic hyperpolarized (HP)-13C MRI has enabled real-time, non-invasive assessment of Warburg-related metabolic dysregulation in glioma using a [1-13C]pyruvate tracer that undergoes conversion to [1-13C]lactate and [13C]bicarbonate. Using a multi-parametric 1H/HP-13C imaging approach, we investigated dynamic and steady-state metabolism, together with physiological parameters, in high-grade gliomas to characterize active tumor.MethodsMulti-parametric 1H/HP-13C MRI data were acquired from fifteen patients with progressive/treatment-naïve glioblastoma [prog/TN GBM, IDH-wildtype (n = 11)], progressive astrocytoma, IDH-mutant, grade 4 (G4AIDH+, n = 2) and GBM manifesting treatment effects (n = 2). Voxel-wise regional analysis of the cohort with prog/TN GBM assessed imaging heterogeneity across contrast-enhancing/non-enhancing lesions (CEL/NEL) and normal-appearing white matter (NAWM) using a mixed effects model. To enable cross-nucleus parameter association, normalized perfusion, diffusion, and dynamic/steady-state (HP-13C/spectroscopic) metabolic data were collectively examined at the 13C resolution. Prog/TN GBM were similarly compared against progressive G4AIDH+ and treatment effects.ResultsRegional analysis of Prog/TN GBM metabolism revealed statistically significant heterogeneity in 1H choline-to-N-acetylaspartate index (CNI)max, [1-13C]lactate, modified [1-13C]lactate-to-[1-13C]pyruvate ratio (CELval > NELval > NAWMval); [1-13C]lactate-to-[13C]bicarbonate ratio (CELval > NELval/NAWMval); and 1H-lactate (CELval/NELval > NAWMundetected). Significant associations were found between normalized perfusion (cerebral blood volume, nCBV; peak height, nPH) and levels of [1-13C]pyruvate and [1-13C]lactate, as well as between CNImax and levels of [1-13C]pyruvate, [1-13C]lactate and modified ratio. GBM, by comparison to G4AIDH+, displayed lower perfusion %-recovery and modeled rate constants for [1-13C]pyruvate-to-[1-13C]lactate conversion (kPL), and higher 1H-lactate and [1-13C]pyruvate levels, while having higher nCBV, %-recovery, kPL, [1-13C]pyruvate-to-[1-13C]lactate and modified ratios relative to treatment effects.ConclusionsGBM consistently displayed aberrant, Warburg-related metabolism and regional heterogeneity detectable by novel HP-13C/1H imaging techniques.

BackgroundReal-time metabolic conversion of intravenously-injected hyperpolarized [1-13C]pyruvate to [1-13C]lactate and [13C]bicarbonate in the brain can be measured using dynamic hyperpolarized carbon-13 (HP-13C) MRI. However, voxel-wise evaluation of metabolism in patients with glioma is challenged by the limited signal-to-noise ratio (SNR) of downstream 13C metabolites, especially within lesions. The purpose of this study was to evaluate the ability of higher-order singular value decomposition (HOSVD) denoising methods to enhance dynamic HP [1-13C]pyruvate MRI data acquired from patients with glioma.MethodsDynamic HP-13C MRI were acquired from 14 patients with glioma. The effects of two HOSVD denoising techniques, tensor rank truncation-image enhancement (TRI) and global-local HOSVD (GL-HOSVD), on the SNR and kinetic modeling were analyzed in [1-13C]lactate data with simulated noise that matched the levels of [13C]bicarbonate signals. Both methods were then evaluated in patient data based on their ability to improve [1-13C]pyruvate, [1-13C]lactate and [13C]bicarbonate SNR. The effects of denoising on voxel-wise kinetic modeling of kPL and kPB was also evaluated. The number of voxels with reliable kinetic modeling of pyruvate-to-lactate (kPL) and pyruvate-to-bicarbonate (kPB) conversion rates within regions of interest (ROIs) before and after denoising was then compared.ResultsBoth denoising methods improved metabolite SNR and regional signal coverage. In patient data, the average increase in peak dynamic metabolite SNR was 2-fold using TRI and 4-5 folds using GL-HOSVD denoising compared to acquired data. Denoising reduced kPL modeling errors from a native average of 23% to 16% (TRI) and 15% (GL-HOSVD); and kPB error from 42% to 34% (TRI) and 37% (GL-HOSVD) (values were averaged voxelwise over all datasets). In contrast-enhancing lesions, the average number of voxels demonstrating within-tolerance kPL modeling error relative to the total voxels increased from 48% in the original data to 84% (TRI) and 90% (GL-HOSVD), while the number of voxels showing within-tolerance kPB modeling error increased from 0% to 15% (TRI) and 8% (GL-HOSVD).ConclusionPost-processing denoising methods significantly improved the SNR of dynamic HP-13C imaging data, resulting in a greater number of voxels satisfying minimum SNR criteria and maximum kinetic modeling errors in tumor lesions. This enhancement can aid in the voxel-wise analysis of HP-13C data and thereby improve monitoring of metabolic changes in patients with glioma following treatment.

Abstract INTRODUCTION Mutations in isocitrate dehydrogenase (IDH) have been investigated as a prognostic biomarker in glioma. The presence of the IDH mutation (IDHm) is associated with 2-hydroxyglutarate (2HG) production and inhibition of glutamate synthesis (McBrayer, Cell 2018). Hyperpolarized carbon-13 (HP-13C) MRI enables dynamic measurements of in-vivo metabolism using a [2-13C]pyruvate labeled probe that undergoes conversion to [2-13C]lactate and [5-13C]glutamate. Here, we present HP [2-13C]pyruvate data from healthy volunteers and patients with IDHm diffuse glioma. Due to its intrinsic low signal-to-noise ratio (SNR), we demonstrate the ability of post-processing denoising to improve its utility and aid in detection of metabolic changes associated with IDHm. METHODS Dynamic HP 13C data were acquired following intravenous injection of [2-13C]pyruvate from five healthy volunteers and one patient with IDHm grade III astrocytoma. A novel multi-resolution frequency specific multislice EPI sequence was used to obtain [2-13C]pyruvate, [5-13C]glutamate, and downfield and upfield [2-13C]lactate signals (3s temporal resolution, pyruvate/lactate/glutamate spatial resolutions = 0.75x0.75cm2/ 2.25x2.25cm2/ 2.25x2.25cm2, 5 slices 3cm thick). Following phase correction, patch-based tensor decomposition denoising was applied to metabolite images. Metabolite differences between normal-appearing white matter (NAWM) and T2 lesion were examined for the patient data. RESULTS HP [2-13C]pyruvate imaging is able to simultaneously probe glycolytic ([2-13C]lactate) and oxidative ([5-13C]glutamate) metabolism. Denoised pyruvate/lactate/glutamate signals achieved a 4-9/3-6/3-7 fold increase in SNR. T2 lesion exhibited decreased glutamate-to-pyruvate and glutamate-to-lactate AUC ratios versus contralateral NAWM (p< 0.018, p < 1.5e-5), consistent with IDH mutant status. CONCLUSION We successfully demonstrated the feasibility of applying variable resolution HP [2-13C]pyruvate metabolic imaging to detect IDHm specific metabolism. This technique addresses a major hurdle in HP 13C MRI by improving SNR while permitting robust metabolism quantification. Future studies will optimize methods for acquiring and processing data to evaluate further data acquired from IDHm glioma patients. Supported by NIH T32 CA151022, P01 CA118816, and NICO.

RNA SHAPE experiments have become important and successful sources of information for RNA structure prediction. In such experiments, chemical reagents are used to probe RNA backbone flexibility at the nucleotide level, which in turn provides information on base pairing and therefore secondary structure. Little is known, however, about the statistics of such SHAPE data. In this work, we explore different representations of noise in SHAPE data and propose a statistically sound framework for extracting reliable reactivity information from multiple SHAPE replicates. Our analyses of RNA SHAPE experiments underscore that a normal noise model is not adequate to represent their data. We propose instead a log-normal representation of noise and discuss its relevance. Under this assumption, we observe that processing simulated SHAPE data by directly averaging different replicates leads to bias. Such bias can be reduced by analyzing the data following a log transformation, either by log-averaging or Kalman filtering. Application of Kalman filtering has the additional advantage that a prior on the nucleotide reactivities can be introduced. We show that the performance of Kalman filtering is then directly dependent on the quality of that prior. We conclude the paper with guidelines on signal processing of RNA SHAPE data.

Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations.

Simple Summary: Dynamic hyperpolarized carbon-13 (HP-13C) MRI is a novel molecular imaging technique that allows for real-time in vivo imaging of glycolysis and oxidative phosphorylation using [1-13C]pyruvate as a non-radioactive and non-toxic metabolic probe. While HP-13C MRI has recently demonstrated the potential to capture Warburg-related metabolic dysregulation in patients with progressive/treatment-naïve high-grade gliomas, further improvement in methodologies is still necessary to help monitor disease status and evaluate early predictors of therapeutic response. To this end, a multimodal 1H/HP-13C MRI protocol was implemented in this study, which incorporated advanced brain tumor MR acquisitions and improved HP-13C metabolic imaging methodologies for the serial monitoring of patients with glioma over the course of clinical treatment. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: To investigate high-resolution hyperpolarized (HP) 13 C pyruvate MRI for measuring cerebral perfusion in the human brain., Methods: HP [1- 13 C]pyruvate MRI was acquired in five healthy volunteers with a multi-resolution EPI sequence with 7.5 × 7.5 mm 2 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 mm 2 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 13 C 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 13 C study demonstrated that hyperpolarized pyruvate MRI can assess cerebral metabolism and perfusion within the same study., (© 2023 International Society for Magnetic Resonance in Medicine.)

Background: Real-time metabolic conversion of intravenously-injected hyperpolarized [1- 13 C]pyruvate to [1- 13 C]lactate and [ 13 C]bicarbonate in the brain can be measured using dynamic hyperpolarized carbon-13 (HP- 13 C) MRI. However, voxel-wise evaluation of metabolism in patients with glioma is challenged by the limited signal-to-noise ratio (SNR) of downstream 13 C metabolites, especially within lesions. The purpose of this study was to evaluate the ability of higher-order singular value decomposition (HOSVD) denoising methods to enhance dynamic HP [1- 13 C]pyruvate MRI data acquired from patients with glioma., Methods: Dynamic HP- 13 C MRI were acquired from 14 patients with glioma. The effects of two HOSVD denoising techniques, tensor rank truncation-image enhancement (TRI) and global-local HOSVD (GL-HOSVD), on the SNR and kinetic modeling were analyzed in [1- 13 C]lactate data with simulated noise that matched the levels of [ 13 C]bicarbonate signals. Both methods were then evaluated in patient data based on their ability to improve [1- 13 C]pyruvate, [1- 13 C]lactate and [ 13 C]bicarbonate SNR. The effects of denoising on voxel-wise kinetic modeling of k PL and k PB was also evaluated. The number of voxels with reliable kinetic modeling of pyruvate-to-lactate (k PL ) and pyruvate-to-bicarbonate (k PB ) conversion rates within regions of interest (ROIs) before and after denoising was then compared., Results: Both denoising methods improved metabolite SNR and regional signal coverage. In patient data, the average increase in peak dynamic metabolite SNR was 2-fold using TRI and 4-5 folds using GL-HOSVD denoising compared to acquired data. Denoising reduced k PL modeling errors from a native average of 23% to 16% (TRI) and 15% (GL-HOSVD); and k PB error from 42% to 34% (TRI) and 37% (GL-HOSVD) (values were averaged voxelwise over all datasets). In contrast-enhancing lesions, the average number of voxels demonstrating within-tolerance k PL modeling error relative to the total voxels increased from 48% in the original data to 84% (TRI) and 90% (GL-HOSVD), while the number of voxels showing within-tolerance k PB modeling error increased from 0% to 15% (TRI) and 8% (GL-HOSVD)., Conclusion: Post-processing denoising methods significantly improved the SNR of dynamic HP- 13 C imaging data, resulting in a greater number of voxels satisfying minimum SNR criteria and maximum kinetic modeling errors in tumor lesions. This enhancement can aid in the voxel-wise analysis of HP- 13 C data and thereby improve monitoring of metabolic changes in patients with glioma following treatment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)