Denoising of hyperpolarized 13 C MR images of the human brain using patch-based higher-order singular value decomposition

Hyperpolarized 13C Metabolic MRI of Patients with Pancreatic Ductal Adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDA) is the third leading cause of cancer-related death in the United States. However, early response assessment using the current approach of measuring changes in tumor size on computed tomography (CT) or MRI is challenging.

PURPOSE

To investigate the feasibility of hyperpolarized (HP) [1-13C]pyruvate MRI to quantify metabolism in the normal appearing pancreas and PDA, and to assess changes in PDA metabolism following systemic chemotherapy.

STUDY TYPE

Prospective.

SUBJECTS

Six patients (65.0 ± 7.6 years, 2 females) with locally advanced or metastatic PDA enrolled prior to starting a new line of systemic chemotherapy.

FIELD STRENGTH/SEQUENCE

3-T, T1-weighted gradient echo, metabolite-selective 13C echoplanar imaging.

ASSESSMENT

Time-resolved HP [1-13C]pyruvate data were acquired before (N = 6) and 4-weeks after (N = 3) treatment initiation. Pyruvate metabolism, as quantified by pharmacokinetic modeling and metabolite area-under-the-curve ratios, was assessed in manually segmented PDA and normal appearing pancreas ROIs (N = 5). The change in tumor metabolism before and 4-weeks after treatment initiation was assessed in primary PDA (N = 2) and liver metastases (N = 1), and was compared to objective tumor response defined by response evaluation criteria in solid tumors (RECIST) on subsequent CTs.

STATISTICAL TESTS

Descriptive tests (mean ± standard deviation), model fit error for pharmacokinetic rate constants.

RESULTS

Primary PDA showed reduced alanine-to-lactate ratios when compared to normal pancreas, due to increased lactate-to-pyruvate or reduced alanine-to-pyruvate ratios. Of the three patients who received HP [1-13C]pyruvate MRI before and 4-weeks after treatment initiation, one patient had a primary tumor with early metabolic response (increase in alanine-to-lactate) and subsequent partial response according to RECIST, one patient had a primary tumor with relatively stable metabolism and subsequent stable disease by RECIST, and one patient had metastatic PDA with increase in lactate-to-pyruvate of the liver metastases and corresponding progressive disease according to RECIST.

DATA CONCLUSION

Altered pyruvate metabolism with increased lactate or reduced alanine was observed in the primary tumor. Early metabolic response assessed at 4-weeks after treatment initiation correlated with subsequent objective tumor response assessed using RECIST.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Tensor Methods in Biomedical Image Analysis

In the past decade, tensors have become increasingly attractive in different aspects of signal and image processing areas. The main reason is the inefficiency of matrices in representing and analyzing multimodal and multidimensional datasets. Matrices cannot preserve the multidimensional correlation of elements in higher-order datasets and this highly reduces the effectiveness of matrix-based approaches in analyzing multidimensional datasets. Besides this, tensor-based approaches have demonstrated promising performances. These together, encouraged researchers to move from matrices to tensors. Among different signal and image processing applications, analyzing biomedical signals and images is of particular importance. This is due to the need for extracting accurate information from biomedical datasets which directly affects patient's health. In addition, in many cases, several datasets have been recorded simultaneously from a patient. A common example is recording electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) of a patient with schizophrenia. In such a situation, tensors seem to be among the most effective methods for the simultaneous exploitation of two (or more) datasets. Therefore, several tensor-based methods have been developed for analyzing biomedical datasets. Considering this reality, in this paper, we aim to have a comprehensive review on tensor-based methods in biomedical image analysis. The presented study and classification between different methods and applications can show the importance of tensors in biomedical image enhancement and open new ways for future studies.

Brain metabolism after therapeutic hypothermia for murine hypoxia-ischemia using hyperpolarized [1-13C] pyruvate magnetic resonance spectroscopy

Hypoxic-ischemic encephalopathy (HIE) is a common neurological syndrome in newborns with high mortality and morbidity. Therapeutic hypothermia (TH), which is standard of care for HIE, mitigates brain injury by suppressing anaerobic metabolism. However, more than 40% of HIE neonates have a poor outcome, even after TH. This study aims to provide metabolic biomarkers for predicting the outcomes of hypoxia-ischemia (HI) after TH using hyperpolarized [1-13C] pyruvate magnetic resonance spectroscopy. Postnatal day 10 (P10) mice with HI underwent TH at 1 h and were scanned at 6-8 h (P10), 24 h (P11), 7 days (P17), and 21 days (P31) post-HI on a 14.1-T NMR spectrometer. The metabolic images were collected, and the conversion rate from pyruvate to lactate and the ratio of lactate to pyruvate in the injured left hemisphere (kPL(L) and Lac/Pyr(L), respectively) were calculated at each timepoint. The outcomes of TH were determined by the assessments of brain injury on T2-weighted images and behavioral tests at later timepoint. kPL(L) and Lac/Pyr(L) over time between the good-outcome and poor-outcome groups and across timepoints within groups were analyzed. We found significant differences in temporal trends of kPL(L) and Lac/Pyr(L) between groups. In the good-outcome group, kPL(L) increased until P31 with a significantly higher value at P31 compared with that at P10, while the level of Lac/Pyr(L) at P31 was notably higher than those at all other timepoints. In the poor-outcome group, kPL(L) and Lac/Pyr(L) increased within 24 h. The kPL(L) value at P11 was considerably higher compared with P10. Discrete temporal changes of kPL(L) and Lac/Pyr(L) after TH between the good-outcome and poor-outcome groups were seen as early as 24 h after HI, reflecting various TH effects on brain anaerobic metabolism, which may provide insights for early screening for response to TH.

Current methods for hyperpolarized [1-13C]pyruvate MRI human studies

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of HP agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate-by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation; (2) MRI system setup and calibrations; (3) data acquisition and image reconstruction; and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the "HP 13C MRI Consensus Group" as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods and Equipment study groups. It further aims to provide a comprehensive reference for future consensus, building as the field continues to advance human studies with this metabolic imaging modality.

Hyperpolarized 13 C metabolic imaging of the human abdomen with spatiotemporal denoising

PURPOSE

Improving the quality and maintaining the fidelity of large coverage abdominal hyperpolarized (HP) 13 C MRI studies with a patch based global-local higher-order singular value decomposition (GL-HOVSD) spatiotemporal denoising approach.

METHODS

Denoising performance was first evaluated using the simulated [1-13 C]pyruvate dynamics at different noise levels to determine optimal kglobal and klocal parameters. The GL-HOSVD spatiotemporal denoising method with the optimized parameters was then applied to two HP [1-13 C]pyruvate EPI abdominal human cohorts (n = 7 healthy volunteers and n = 8 pancreatic cancer patients).

RESULTS

The parameterization of kglobal  = 0.2 and klocal  = 0.9 denoises abdominal HP data while retaining image fidelity when evaluated by RMSE. The kPX (conversion rate of pyruvate-to-metabolite, X = lactate or alanine) difference was shown to be <20% with respect to ground-truth metabolic conversion rates when there is adequate SNR (SNRAUC  > 5) for downstream metabolites. In both human cohorts, there was a greater than nine-fold gain in peak [1-13 C]pyruvate, [1-13 C]lactate, and [1-13 C]alanine apparent SNRAUC . The improvement in metabolite SNR enabled a more robust quantification of kPL and kPA . After denoising, we observed a 2.1 ± 0.4 and 4.8 ± 2.5-fold increase in the number of voxels reliably fit across abdominal FOVs for kPL and kPA quantification maps.

CONCLUSION

Spatiotemporal denoising greatly improves visualization of low SNR metabolites particularly [1-13 C]alanine and quantification of [1-13 C]pyruvate metabolism in large FOV HP 13 C MRI studies of the human abdomen.

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.

Quo Vadis Hyperpolarized 13C MRI?

Over the last two decades, hyperpolarized 13C MRI has gained significance in both preclinical and clinical studies, hereby relying on technologies like PHIP-SAH (ParaHydrogen-Induced Polarization-Side Arm Hydrogenation), SABRE (Signal Amplification by Reversible Exchange), and dDNP (dissolution Dynamic Nuclear Polarization), with dDNP being applied in humans. A clinical dDNP polarizer has enabled studies across 24 sites, despite challenges like high cost and slow polarization. Parahydrogen-based techniques like SABRE and PHIP offer faster, more cost-efficient alternatives but require molecule-specific optimization. The focus has been on imaging metabolism of hyperpolarized probes, which requires long T1, high polarization and rapid contrast generation. Efforts to establish novel probes, improve acquisition techniques and enhance data analysis methods including artificial intelligence are ongoing. Potential clinical value of hyperpolarized 13C MRI was demonstrated primarily for treatment response assessment in oncology, but also in cardiology, nephrology, hepatology and CNS characterization. In this review on biomedical hyperpolarized 13C MRI, we summarize important and recent advances in polarization techniques, probe development, acquisition and analysis methods as well as clinical trials. Starting from those we try to sketch a trajectory where the field of biomedical hyperpolarized 13C MRI might go.

A user independent denoising method for x-nuclei MRI and MRS

PURPOSE

X-nuclei (also called non-proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x-nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state-of-the-art denoising method.

METHODS

Denoising approaches were compared on human acquisitions of sodium (23 Na) brain, deuterium (2 H) brain, carbon (13 C) heart and brain, and simulated dynamic hyperpolarized 13 C brain scans, with and without additional noise. The current state-of-the-art denoising method Global-local higher order singular value decomposition (GL-HOSVD) was compared to the few-input method tensor Marchenko-Pastur principal component analysis (tMPPCA). Noise-removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean-square-error and Bland-Altman analysis to quantify agreement between original and denoised results of noise-added data.

RESULTS

GL-HOSVD and tMPPCA showed similar performance for the variety of x-nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL-HOSVD. The mean ratio between noise-added and denoising reproducibility coefficients of the Bland-Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL-HOSVD and tMPPCA, respectively.

CONCLUSION

The strength of tMPPCA lies in the few-input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x-nuclei imaging improvements and the preferred denoising method.

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.

Current Methods for Hyperpolarized [1-13C]pyruvate MRI Human Studies

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the HP 13C MRI Consensus Group as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality.

Hyperpolarized [2-13C]pyruvate MR molecular imaging with whole brain coverage

Hyperpolarized (HP) 13C Magnetic Resonance Imaging (MRI) was applied for the first time to image and quantify the uptake and metabolism of [2-13C]pyruvate in the human brain to provide new metabolic information on cerebral energy metabolism. HP [2-13C]pyruvate was injected intravenously and imaged in 5 healthy human volunteer exams with whole brain coverage in a 1-minute acquisition using a specialized spectral-spatial multi-slice echoplanar imaging (EPI) pulse sequence to acquire 13C-labeled volumetric and dynamic images of [2-13C]pyruvate and downstream metabolites [5-13C]glutamate and [2-13C]lactate. Metabolic ratios and apparent conversion rates of pyruvate-to-lactate (kPL) and pyruvate-to-glutamate (kPG) were quantified to investigate simultaneously glycolytic and oxidative metabolism in a single injection.

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.

Tensor denoising of multidimensional MRI data

PURPOSE

To develop a denoising strategy leveraging redundancy in high-dimensional data.

THEORY AND METHODS

The SNR fundamentally limits the information accessible by MRI. This limitation has been addressed by a host of denoising techniques, recently including the so-called MPPCA: principal component analysis of the signal followed by automated rank estimation, exploiting the Marchenko-Pastur distribution of noise singular values. Operating on matrices comprised of data patches, this popular approach objectively identifies noise components and, ideally, allows noise to be removed without introducing artifacts such as image blurring, or nonlocal averaging. The MPPCA rank estimation, however, relies on a large number of noise singular values relative to the number of signal components to avoid such ill effects. This condition is unlikely to be met when data patches and therefore matrices are small, for example due to spatially varying noise. Here, we introduce tensor MPPCA (tMPPCA) for the purpose of denoising multidimensional data, such as from multicontrast acquisitions. Rather than combining dimensions in matrices, tMPPCA uses each dimension of the multidimensional data's inherent tensor-structure to better characterize noise, and to recursively estimate signal components.

RESULTS

Relative to matrix-based MPPCA, tMPPCA requires no additional assumptions, and comparing the two in a numerical phantom and a multi-TE diffusion MRI data set, tMPPCA dramatically improves denoising performance. This is particularly true for small data patches, suggesting that tMPPCA can be especially beneficial in such cases.

CONCLUSIONS

The MPPCA denoising technique can be extended to high-dimensional data with improved performance for smaller patch sizes.

Whole-Abdomen Metabolic Imaging of Healthy Volunteers Using Hyperpolarized [1-13 C]pyruvate MRI

BACKGROUND

Hyperpolarized 13 C MRI quantitatively measures enzyme-catalyzed metabolism in cancer and metabolic diseases. Whole-abdomen imaging will permit dynamic metabolic imaging of several abdominal organs simultaneously in healthy and diseased subjects.

PURPOSE

Image hyperpolarized [1-13 C]pyruvate and products in the abdomens of healthy volunteers, overcoming challenges of motion, magnetic field variations, and spatial coverage. Compare hyperpolarized [1-13 C]pyruvate metabolism across abdominal organs of healthy volunteers.

STUDY TYPE

Prospective technical development.

SUBJECTS

A total of 13 healthy volunteers (8 male), 21-64 years (median 36).

FIELD STRENGTH/SEQUENCE

A 3 T. Proton: T1 -weighted spoiled gradient echo, T2 -weighted single-shot fast spin echo, multiecho fat/water imaging. Carbon-13: echo-planar spectroscopic imaging, metabolite-specific echo-planar imaging.

ASSESSMENT

Transmit magnetic field was measured. Variations in main magnetic field (ΔB0 ) determined using multiecho proton acquisitions were compared to carbon-13 acquisitions. Changes in ΔB0 were measured after localized shimming. Improvements in metabolite signal-to-noise ratio were calculated. Whole-organ regions of interests were drawn over the liver, spleen, pancreas, and kidneys by a single investigator. Metabolite signals, time-to-peak, decay times, and mean first-order rate constants for pyruvate-to-lactate (kPL ) and alanine (kPA ) conversion were measured in each organ.

STATISTICAL TESTS

Linear regression, one-sample Kolmogorov-Smirnov tests, paired t-tests, one-way ANOVA, Tukey's multiple comparisons tests. P ≤ 0.05 considered statistically significant.

RESULTS

Proton ΔB0 maps correlated with carbon-13 ΔB0 maps (slope = 0.93, y-intercept = -2.88, R2  = 0.73). Localized shimming resulted in mean frequency offset within ±25 Hz for all organs. Metabolite SNR significantly increased after denoising. Mean kPL and kPA were highest in liver, followed by pancreas, spleen, and kidneys (all comparisons with liver were significant).

DATA CONCLUSION

Whole-abdomen coverage with hyperpolarized carbon-13 MRI was feasible despite technical challenges. Multiecho gradient echo 1 H acquisitions accurately predicted chemical shifts observed using carbon-13 spectroscopy. Carbon-13 acquisitions benefited from local shimming. Metabolite energetics in the abdomen compiled for healthy volunteers can be used to design larger clinical trials in patients with metabolic diseases.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Denoise ultra-low-field 3D magnetic resonance images using a joint signal-image domain filter

Ultra-low-field magnetic resonance imaging (MRI) could suffer from heavy uncorrelated noise, and its removal could be a critical post-processing task. As a primary source of interference, Gaussian noise could corrupt the sampled MR signal (k-space data), especially at lower B₀ field strength. For this reason, we consider both signal and image domains by proposing a new joint filter characterized by a Kalman filter with linear prediction and a nonlocal mean filter with higher-order singular value decomposition (HOSVD) for denoising 3D MR data. The Kalman filter first attenuates the noise in k-space, and then its reconstruction images are used to guide HOSVD denoising process with exploring self-similarity among 3D structures. The clearer prefiltered images could also generate improved HOSVD learned bases used to transform the noise corrupted patch groups in the original MR data. The flexibility of proposed method is also demonstrated by integrating other k-space filters into the algorithm scheme. Experimental data includes simulated MR images with the varying noise level and real MR images obtained from our 50 mT MRI scanner. The results reveal that our method has a better noise-removal ability and introduces lesser unexpected artifacts than other related MRI denoising approaches.

Kinetic analysis of multi-resolution hyperpolarized 13 C human brain MRI to study cerebral metabolism

PURPOSE

To investigate multi-resolution hyperpolarized (HP) 13 C pyruvate MRI for measuring kinetic conversion rates in the human brain.

METHODS

HP [1-13 C]pyruvate MRI was acquired in 6 subjects with a multi-resolution EPI sequence at 7.5 × 7.5 mm2 resolution for pyruvate and 15 × 15 mm2 resolution for lactate and bicarbonate. With the same lactate data, 2 quantitative maps of pyruvate-to-lactate conversion (kPL ) maps were generated: 1 using 7.5 × 7.5 mm2 resolution pyruvate data and the other using synthetic 15 × 15 mm2 resolution pyruvate data to simulate a standard constant resolution acquisition. To examine local kPL values, 4 voxels were manually selected in each study representing brain tissue near arteries, brain tissue near veins, white matter, and gray matter.

RESULTS

High resolution 7.5 × 7.5 mm2 pyruvate images increased the spatial delineation of brain structures and decreased partial volume effects compared to coarser resolution 15 × 15 mm2 pyruvate images. Voxels near arteries, veins and in white matter exhibited higher calculated kPL for multi-resolution images.

CONCLUSION

Acquiring HP 13 C pyruvate metabolic data with a multi-resolution approach minimized partial volume effects from vascular pyruvate signals while maintaining the SNR of downstream metabolites. Higher resolution pyruvate images for kinetic fitting resulted in increased kinetic rate values, particularly around the superior sagittal sinus and cerebral arteries, by reducing extracellular pyruvate signal contributions from adjacent blood vessels. This HP 13 C study showed that acquiring pyruvate with finer resolution improved the quantification of kinetic rates throughout the human brain.

Lactate saturation limits bicarbonate detection in hyperpolarized 13 C-pyruvate MRI of the brain

PURPOSE

To investigate the potential effects of [1-¹³ C]lactate RF saturation pulses on [¹³ C]bicarbonate detection in hyperpolarized [1-¹³ C]pyruvate MRI of the brain.

METHODS

Thirteen healthy rats underwent MRI with hyperpolarized [1-¹³ C]pyruvate of either the brain (n = 8) or the kidneys, heart, and liver (n = 5). Dynamic, metabolite-selective imaging was used in a cross-over experiment in which [1-¹³ C]lactate was excited with either 0° or 90° flip angles. The [¹³ C]bicarbonate SNR and apparent [1-¹³ C]pyruvate-to-[¹³ C]bicarbonate conversion (k_PB ) were determined. Furthermore, simulations were performed to identify the SNR optimal flip-angle scheme for detection of [1-¹³ C]lactate and [¹³ C]bicarbonate.

RESULTS

In the brain, the [¹³ C]bicarbonate SNR was 64% higher when [1-¹³ C]lactate was not excited (5.8 ± 1.5 vs 3.6 ± 1.3; 1.2 to 3.3-point increase; p = 0.0027). The apparent k_PB decreased 25% with [1-¹³ C]lactate saturation (0.0047 ± 0.0008 s^-1 vs 0.0034 ± 0.0006 s^-1 ; 95% confidence interval, 0.0006-0.0019 s^-1 increase; p = 0.0049). These effects were not present in the kidneys, heart, or liver. Simulations suggest that the optimal [¹³ C]bicarbonate SNR with a TR of 1 s in the brain is obtained with [¹³ C]bicarbonate, [1-¹³ C]lactate, and [1-¹³ C]pyruvate flip angles of 60°, 15°, and 10°, respectively.

CONCLUSIONS

Radiofrequency saturation pulses on [1-¹³ C]lactate limit [¹³ C]bicarbonate detection in the brain specifically, which could be due to shuttling of lactate from astrocytes to neurons. Our results have important implications for experimental design in studies in which [¹³ C]bicarbonate detection is warranted.

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.

Deep Learning-Based Diffusion-Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction

The prefiltered image was imported into the local higher-order singular value decomposition (HOSVD) denoising algorithm (GL-HOSVD)-optimized diffusion-weighted imaging (DWI) image, which was compared with the deviation correction nonlocal mean (NL mean) and low-level edge algorithm (LR + edge). Regarding the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), sensitivity, specificity, accuracy, and consistency, the application effect of the GL-HOSVD algorithm in DWI was investigated, and its adoption effect in the examination of ischemic penumbra (IP) of early acute cerebral infarction (ACI) patients was evaluated. A total of 210 patients with ACI were selected as the research subjects, who were randomly rolled into two groups. Those who were checked by conventional DWI were set as the control group, and those who used DWI based on the GL-HOSVD denoising algorithm were set as the observation group, with 105 people in each. Positron emission tomography (PET) test results were set as the gold standard to evaluate the application value of the two examination methods. It was found that under different noise levels, the peak signal-to-noise ratio (PSNR) of the GL-HOSVD algorithm and the root mean square error (RMSE) of the FA parameter were better than those of the nonlocal means (NL-means) of deviation correction and low-rank edge algorithm (LR + edge). The sensitivity, specificity, accuracy, and consistency (8.76%, 81.25%, 87.62%, and 0.52) of the observation group were higher than those of the control group (57.78%, 53.33%, 57.14%, and 0.35) (P < 0.05). Moreover, the apparent diffusion coefficient (ADC) of the DWI images of the observation group was basically consistent with that of the PET images, while the control group had a poor display effect and low definition. In summary, under different noise levels, the GL-HOSVD algorithm had a good denoising effect and greatly reduced fringe artifacts. DWI after denoising had high sensitivity, specificity, accuracy, and consistency in the detection of IP, which was worthy of clinical application and promotion.

Current human brain applications and challenges of dynamic hyperpolarized carbon-13 labeled pyruvate MR metabolic imaging

The ability of hyperpolarized carbon-13 MR metabolic imaging to acquire dynamic metabolic information in real time is crucial to gain mechanistic insights into metabolic pathways, which are complementary to anatomic and other functional imaging methods. This review presents the advantages of this emerging functional imaging technology, describes considerations in clinical translations, and summarizes current human brain applications. Despite rapid development in methodologies, significant technological and physiological related challenges continue to impede broader clinical translation.