Advanced Hyperpolarized 13C Metabolic Imaging Protocol for Patients with Gliomas: A Comprehensive Multimodal MRI Approach.

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.

Evaluation of deep learning models for quality control of MR spectra.

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.

Investigating cerebral perfusion with high resolution hyperpolarized [1-13 C]pyruvate MRI.

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 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 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.

Multi-parametric hyperpolarized 13C/1H imaging reveals Warburg-related metabolic dysfunction and associated regional heterogeneity in high-grade human gliomas.

BACKGROUND: Dynamic 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. METHODS: Multi-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. RESULTS: Regional 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. CONCLUSIONS: GBM consistently displayed aberrant, Warburg-related metabolism and regional heterogeneity detectable by novel HP-13C/1H imaging techniques.

Assessment of higher-order singular value decomposition denoising methods on dynamic hyperpolarized [1-13C]pyruvate MRI data from patients with glioma.

BACKGROUND: Real-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. METHODS: Dynamic 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. 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 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). CONCLUSION: Post-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.

Extracting information from RNA SHAPE data: Kalman filtering approach.

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.

Data-directed RNA secondary structure prediction using probabilistic modeling.

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.