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Imaging tumoral pH may help to characterize aggressiveness, metastasis, and therapeutic response. We report the development of hyperpolarized [2-13C,D10]diethylmalonic acid, which exhibits a large pH-dependent 13C chemical shift over the physiological range. We demonstrate that co-polarization with [1-13C,D9]tert-butanol accurately measures pH via13C NMR and magnetic resonance spectroscopic imaging in phantoms.

Imaging tumoral pH may help to characterize aggressiveness, metastasis, and therapeutic response. We report the development of hyperpolarized [2-13C,D10]diethylmalonic acid, which exhibits a large pH-dependent 13C chemical shift over the physiological range. We demonstrate that co-polarization with [1-13C,D9]tert-butanol accurately measures pH via13C NMR and magnetic resonance spectroscopic imaging in phantoms.

Purpose: This study leverages the echo planar time-resolved imaging (EPTI) concept in MR fingerprinting (MRF) framework for a new time-resolved MRF (TRMRF) approach, and explores its capability for fast simultaneous quantification of multiple MR parameters including T 1 , T 2 , T 2 *, proton density, off resonance, and B 1 + ., Methods: The proposed TRMRF method uses the concept of EPTI to track the signal change along the EPI echo train for T 2 * weighting with a k-t Poisson-based sampling order designed for acquisition. A two-dimensional decomposition algorithm was designed for the image reconstruction, enabling fast and precise subspace modeling. The accuracy of proposed method was evaluated by a T 1 /T 2 phantom. The feasibility was demonstrated through 5 healthy volunteer brain studies., Results: In the phantom studies, T 1 , T 2 , and T 2 * maps of TRMRF correlated strongly with gold-standard methods. The concordance correlation coefficients are 0.9999, 0.9984 and 0.9978, and R 2 s are 0.9998, 0.9971, and 0.9983. In the in vivo studies, quantitative maps were acquired with 5 healthy volunteers. TRMRF was demonstrated to have comparable results with spiral MRF and gradient-echo EPTI. TRMRF scans using 16, 10, and 6 s per slice were also evaluated to demonstrate the capability of shorter scan times., Conclusion: A new approach is proposed to exploit the advantage of EPTI in the MRF framework. We demonstrate in phantom and in vivo experiments that T 1 , T 2 , T 2 *, proton density, off resonance, and B 1 + can be simultaneously quantified within 6 s/slice by TRMRF., (© 2024 International Society for Magnetic Resonance in Medicine.)

Background: In modern positron emission tomography (PET) with multi-modality imaging (e.g., PET/CT and PET/MR), the attenuation correction (AC) is the single largest correction factor for image reconstruction. One way to assess AC methods and other reconstruction parameters is to utilize software-based simulation tools, such as a lesion insertion tool. Extensive validation of these simulation tools is required to ensure results of the study are clinically meaningful., Purpose: To evaluate different PET AC methods using a synthetic lesion insertion tool that simulates lesions in a patient cohort that has both PET/MR and PET/CT images. To further demonstrate how lesion insertion tool may be used to extend knowledge of PET reconstruction parameters, including but not limited to AC., Methods: Lesion quantitation is compared using conventional Dixon-based MR-based AC (MRAC) to that of using CT-based AC (CTAC, a "ground truth"). First, the pre-existing lesions were simulated in a similar environment; a total of 71 lesions were identified in 18 pelvic PET/MR patient images acquired with a time-of-flight simultaneous PET/MR scanner, and matched lesions were inserted contralaterally on the same axial slice. Second, synthetic lesions were inserted into four anatomic target locations in a cohort of four patients who didn't have any observed clinical lesions in the pelvis., Results: The matched lesion insertions resulted in unity between the lesion error ratios (mean SUVs), demonstrating that the inserted lesions successfully simulated the original lesions. In the second study, the inserted lesions had distinct characteristics by target locations and demonstrated negative max-SUV%diff trends for bone-dominant sites across the patient cohort., Conclusions: The current work demonstrates that the applied lesion insertion tool can simulate uptake in pelvic lesions and their expected SUV values, and that the lesion insertion tool can be extended to evaluate further PET reconstruction corrections and algorithms and their impact on quantitation accuracy and precision., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Hyperpolarized (HP) 13 C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth, it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper, we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13 C MRI studies. We then show where the majority of these can be fit into existing DICOM attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13 C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13 C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13 C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13 C MRI data storage that will support future multi-site trials, research studies, and technical developments of this imaging technique., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Purpose: Metabolite-specific balanced SSFP (MS-bSSFP) sequences are increasingly used in hyperpolarized [1- 13 C]Pyruvate (HP 13 C) MRI studies as they improve SNR by refocusing the magnetization each TR. Currently, pharmacokinetic models used to fit conversion rate constants, k PL and k PB , and rate constant maps do not account for differences in the signal evolution of MS-bSSFP acquisitions., Methods: In this work, a flexible MS-bSSFP model was built that can be used to fit conversion rate constants for these experiments. The model was validated in vivo using paired animal (healthy rat kidneys n = 8, transgenic adenocarcinoma of the mouse prostate n = 3) and human renal cell carcinoma (n = 3) datasets. Gradient echo (GRE) acquisitions were used with a previous GRE model to compare to the results of the proposed GRE-bSSFP model., Results: Within simulations, the proposed GRE-bSSFP model fits the simulated data well, whereas a GRE model shows bias because of model mismatch. For the in vivo datasets, the estimated conversion rate constants using the proposed GRE-bSSFP model are consistent with a previous GRE model. Jointly fitting the lactate T 2 with k PL resulted in less precise k PL estimates., Conclusion: The proposed GRE-bSSFP model provides a method to estimate conversion rate constants, k PL and k PB , for MS-bSSFP HP 13 C experiments. This model may also be modified and used for other applications, for example, estimating rate constants with other hyperpolarized reagents or multi-echo bSSFP., (© 2024 International Society for Magnetic Resonance in Medicine.)

Tumor acidosis is one of the hallmarks indicating the initiation and progression of various cancers. Here, we present a protocol for preparing a hyperpolarized (HP) 13 C-bicarbonate tissue pH MRI imaging contrast agent to detect aggressive tumors. We describe the steps for the formulation and polarization of a precursor molecule 13 C-glycerol carbonate ( 13 C-GLC), the post-dissolution reaction, and converting HP 13 C-GLC to an injectable HP 13 C-bicarbonate solution. We then detail procedures for MRI data acquisition to generate tumor pH maps for assessing tumor aggressiveness. For complete details on the use and execution of this protocol, please refer to Mu et al. 1 ., Competing Interests: Declaration of interests Some elements of the original work are published in Mu et al. ACS Sens. 8, 4042–4054. 10.1021/acssensors.3c00851. P.E.Z.L. receives research support from GE Healthcare. R.R.F. receives research funding from Bristol Meyers Squibb., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Kidney transplant is the treatment of choice for patients with end-stage renal disease. Early detection of allograft injury is important to delay or prevent irreversible damage., Purpose: To investigate the feasibility of hyperpolarized (HP) [1- 13 C]pyruvate MRI for assessing kidney allograft metabolism., Study Type: Prospective., Subjects: 6 participants (mean age, 45.2 ± 12.4 years, 2 females) scheduled for kidney allograft biopsy and 5 patients (mean age, 59.6 ± 10.4 years, 2 females) with renal cell carcinoma (RCC)., Field Strength/sequence: 3 Tesla, T2-weighted fast spin echo, multi-echo gradient echo, single shot diffusion-weighted echo-planar imaging, and time-resolved HP 13 C metabolite-selective imaging., Assessment: Five of the six kidney allograft participants underwent biopsy after MRI. Estimated glomerular filtration rate (eGFR) and urine protein-to-creatine ratio (uPCR) were collected within 4 weeks of MRI. Kidney metabolism was quantified from HP [1- 13 C]pyruvate MRI using the lactate-to-pyruvate ratio in allograft kidneys and non-tumor bearing kidneys from RCC patients., Statistical Tests: Descriptive statistics (mean ± standard deviation)., Results: Biopsy was performed a mean of 9 days (range 5-19 days) after HP [1- 13 C]pyruvate MRI. Three biopsies were normal, one showed low-grade fibrosis and one showed moderate microvascular inflammation. All had stable functioning allografts with eGFR > 60 mL/min/1.73 m 2 and normal uPCR. One participant who did not undergo biopsy had reduced eGFR of 49 mL/min/1.73 m 2 and elevated uPCR. The mean lactate-to-pyruvate ratio was 0.373 in participants with normal findings (n = 3) and 0.552 in participants with abnormal findings (n = 2). The lactate-to-pyruvate ratio was highest (0.847) in the participant with reduced eGFR and elevated uPRC. Native non-tumor bearing kidneys had a mean lactate-to-pyruvate ratio of 0.309., Data Conclusion: Stable allografts with normal findings at biopsy showed lactate-to-pyruvate ratios similar to native non-tumor bearing kidneys, whereas allografts with abnormal findings showed higher lactate-to-pyruvate ratios.

Purpose: CT Hounsfield Units (HUs) are converted to electron density using a calibration curve obtained from physical measurements of an electron density phantom. HU values assigned to an MRI-derived synthetic computed tomography (sCT) may present a different relationship with electron density compared to CT HU. Correct assignment of sCT HU values is critical for accurate dose calculation and delivery. The goals of this work were to develop a sCT calibration curve using patient data acquired on a clinically commissioned CT scanner and assess for CyberKnife- and volumetric modulated arc therapy (VMAT)-based MR-only treatment planning of prostate SBRT., Methods: Same-day CT and MRI simulation in the treatment position were performed on 10 patients treated with SBRT to the prostate. Dixon in-phase and out-of-phase MRIs were acquired on a 3T scanner using a 3D T1-weighted gradient-echo sequence to generate sCTs using a commercial sCT algorithm. CT and sCT datasets were co-registered and HU values compared using mean absolute error (MAE). An optimized HU-to-density calibration curve was created based on average HU values across an institutional patient database for each of the four sCT tissue types. Clinical CyberKnife and VMAT treatment plans were generated on each patient CT and recomputed onto corresponding sCTs. Dose distributions computed using CT and sCT were compared using gamma criteria and dose-volume-histograms., Results: For the optimized calibration curve, HU values were -96, 37, 204, and 1170 and relative electron densities were 0.95, 1.04, 1.1, and 1.7 for adipose, soft tissue, inner bone, and outer bone, respectively. The proposed sCT protocol produced total MAE of 94 ± 20HU. Gamma values mean ± std (min-max) were 98.9% ± 0.9% (97.1%-100%) and 97.7% ± 1.3% (95.3%-99.3%) for VMAT and CyberKnife plans, respectively., Conclusion: MRI-derived sCT using the proposed approach shows excellent dosimetric agreement with conventional CT simulation, demonstrating the feasibility of MRI-derived sCT for prostate SBRT treatment planning., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

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- 13 C]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 13 C echoplanar imaging., Assessment: Time-resolved HP [1- 13 C]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- 13 C]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., (© 2023 International Society for Magnetic Resonance in Medicine.)

Purpose: This study aimed to develop a new high-resolution MRI sequence for the imaging of the ultra-short transverse relaxation time (uT 2 ) components in the brain, while simultaneously providing proton density (PD) contrast for reference and quantification., Theory: The sequence combines low flip angle balanced SSFP (bSSFP) and UTE techniques, together with a 3D dual-echo rosette k-space trajectory for readout., Methods: The expected image contrast was evaluated by simulations. A study cohort of six healthy volunteers and eight multiple sclerosis (MS) patients was recruited to test the proposed sequence. Subtraction between two TEs was performed to extract uT 2 signals. In addition, conventional longitudinal relaxation time (T 1 ) weighted, T 2 -weighted, and PD-weighted MRI sequences were also acquired for comparison., Results: Typical PD-contrast was found in the second TE images, while uT 2 signals were selectively captured in the first TE images. The subtraction images presented signals primarily originating from uT 2 components, but only if the first TE is short enough. Lesions in the MS subjects showed hyperintense signals in the second TE images but were hypointense signals in the subtraction images. The lesions had significantly lower signal intensity in subtraction images than normal white matter (WM), which indicated a reduction of uT 2 components likely associated with myelin., Conclusion: 3D isotropic sub-millimeter (0.94 mm) spatial resolution images were acquired with the novel bSSFP UTE sequence within 3 min. It provided easy extraction of uT 2 signals and PD-contrast for reference within a single acquisition., (© 2024 International Society for Magnetic Resonance in Medicine.)

Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a image-based deep learning method to predict clinically significant prostate cancer from screening MRI in patients that subsequently underwent biopsy with results ranging from benign pathology to the highest grade tumors. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. Where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=198 ) multi-parametric prostate MRI exams collected at UCSF from 2016-2019 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can feasibly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation (71.6% vs 66.7% balanced accuracy and 0.724 vs 0.716 AUC).

MRI with hyperpolarized (HP) 13 C agents, also known as HP 13 C 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- 13 C]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 13 C-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 13 C 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., (© 2024 International Society for Magnetic Resonance in Medicine.)

Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising technique for reconstructing sub-sampled MRI. However, supervised deep learning requires a large dataset of fully-sampled data. Although unsupervised or self-supervised deep learning methods have emerged to address the limitations of supervised deep learning approaches, they still require a database of images. In contrast, scan-specific deep learning methods learn and reconstruct using only the sub-sampled data from a single scan. Here, we introduce Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion (DNLINV) that does not require an auto calibration scan region. DNLINV utilizes a Deep Image Prior-type generative modeling approach and relies on approximate Bayesian inference to regularize the deep convolutional neural network. We demonstrate our approach on several anatomies, contrasts, and sampling patterns and show improved performance over existing approaches in scan-specific calibrationless parallel imaging and compressed sensing.

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 k global and k local 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 k global  = 0.2 and k local  = 0.9 denoises abdominal HP data while retaining image fidelity when evaluated by RMSE. The k PX (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 (SNR AUC  > 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 SNR AUC . The improvement in metabolite SNR enabled a more robust quantification of k PL and k PA . 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 k PL and k PA 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., (© 2024 International Society for Magnetic Resonance in Medicine.)

Introduction: The healthy heart has remarkable metabolic flexibility that permits rapid switching between mitochondrial glucose oxidation and fatty acid oxidation to generate ATP. Loss of metabolic flexibility has been implicated in the genesis of contractile dysfunction seen in cardiomyopathy. Metabolic flexibility has been imaged in experimental models, using hyperpolarized (HP) [2- 13 C]pyruvate MRI, which enables interrogation of metabolites that reflect tricarboxylic acid (TCA) cycle flux in cardiac myocytes. This study aimed to develop methods, demonstrate feasibility for [2- 13 C]pyruvate MRI in the human heart for the first time, and assess cardiac metabolic flexibility., Methods: Good manufacturing practice [2- 13 C]pyruvic acid was polarized in a 5 T polarizer for 2.5-3 h. Following dissolution, quality control parameters of HP pyruvate met all safety and sterility criteria for pharmacy release, prior to administration to study subjects. Three healthy subjects each received two HP injections and MR scans, first under fasting conditions, followed by oral glucose load. A 5 cm axial slab-selective spectroscopy approach was prescribed over the left ventricle and acquired at 3 s intervals on a 3 T clinical MRI scanner., Results: The study protocol, which included HP substrate injection, MR scanning, and oral glucose load, was performed safely without adverse events. Key downstream metabolites of [2- 13 C]pyruvate metabolism in cardiac myocytes include the glycolytic derivative [2- 13 C]lactate, TCA-associated metabolite [5- 13 C]glutamate, and [1- 13 C]acetylcarnitine, catalyzed by carnitine acetyltransferase (CAT). After glucose load, 13 C-labeling of lactate, glutamate, and acetylcarnitine from 13 C-pyruvate increased by an average of 39.3%, 29.5%, and 114% respectively in the three subjects, which could result from increases in lactate dehydrogenase, pyruvate dehydrogenase, and CAT enzyme activity as well as TCA cycle flux (glucose oxidation)., Conclusions: HP [2- 13 C]pyruvate imaging is safe and permits noninvasive assessment of TCA cycle intermediates and the acetyl buffer, acetylcarnitine, which is not possible using HP [1- 13 C]pyruvate. Cardiac metabolite measurement in the fasting/fed states provides information on cardiac metabolic flexibility and the acetylcarnitine pool., (© 2023 John Wiley & Sons, Ltd.)

This study aimed to implement a multimodal 1 H/HP- 13 C imaging protocol to augment the serial monitoring of patients with glioma, while simultaneously pursuing methods for improving the robustness of HP- 13 C metabolic data. A total of 100 1 H/HP [1- 13 C]-pyruvate MR examinations (104 HP- 13 C 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- 13 C 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- 13 C]-pyruvate center frequency significantly reduced off-resonance excitation relative to the coil-embedded [ 13 C]-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- 13 C MRI acquisition delay ( p = 0.007). This study demonstrated the implementation of a comprehensive multimodal 1 H/HP- 13 C MR protocol emphasizing the monitoring of steady-state/dynamic metabolism in patients with glioma.

Background: The heart has metabolic flexibility, which is influenced by fed/fasting states, and pathologies such as myocardial ischemia and hypertrophic cardiomyopathy (HCM). Hyperpolarized (HP) 13 C-pyruvate MRI is a promising new tool for non-invasive quantification of myocardial glycolytic and Krebs cycle flux. However, human studies of HP 13 C-MRI have yet to demonstrate regional quantification of metabolism, which is important in regional ischemia and HCM patients with asymmetric septal/apical hypertrophy., Methods: We developed and applied methods for whole-heart imaging of 13 C-pyruvate, 13 C-lactate and 13 C-bicarbonate, following intravenous administration of [1- 13 C]-pyruvate. The image acquisition used an autonomous scanning method including bolus tracking, real-time magnetic field calibrations and metabolite-specific imaging. For quantification of metabolism, we evaluated 13 C metabolite images, ratio metrics, and pharmacokinetic modeling to provide measurements of myocardial lactate dehydrogenase (LDH) and pyruvate dehydrogenase (PDH) mediated metabolic conversion in 5 healthy volunteers (fasting & 30 min following oral glucose load)., Results: We demonstrate whole heart coverage for dynamic measurement of pyruvate-to-lactate conversion via LDH and pyruvate-to-bicarbonate conversion via PDH at a resolution of 6 × 6 × 21 mm 3 ( 13 C-pyruvate) and 12 × 12 × 21 mm 3 ( 13 C-lactate, 13 C-bicarbonate). 13 C-pyruvate and 13 C-lactate were detected simultaneously in the RV blood pool, immediately after intravenous injection, reflecting LDH activity in blood. In healthy volunteers, myocardial 13 C-pyruvate-SNR, 13 C-lactate-SNR, 13 C-bicarbonate-SNR, 13 C-lactate/pyruvate ratio, 13 C-pyruvate-to-lactate conversion rate, k PL , and 13 C-pyruvate-to-bicarbonate conversion rate, k PB , all had statistically significant increases following oral glucose challenge. k PB , reflecting PDH activity and pyruvate entering the Krebs Cycle, had the highest correlation with blood glucose levels and was statistically significant., Conclusions: We demonstrate first-in-human regional quantifications of cardiac metabolism by HP 13 C-pyruvate MRI that aims to reflect LDH and PDH activity., (© 2023. The Author(s).)

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.)

Solid tumors such as prostate cancer (PCa) commonly develop an acidic microenvironment with pH 6.5-7.2, owing to heterogeneous perfusion, high metabolic activity, and rapid cell proliferation. In preclinical prostate cancer models, disease progression is associated with a decrease in tumor extracellular pH, suggesting that pH imaging may reflect an imaging biomarker to detect aggressive and high-risk disease. Therefore, we developed a hyperpolarized carbon-13 MRI method to image the tumor extracellular pH (pH e ) and prepared it for clinical translation for detection and risk stratification of PCa. This method relies on the rapid breakdown of hyperpolarized (HP) 1,2-glycerol carbonate (carbonyl- 13 C) via base-catalyzed hydrolysis to produce HP 13 CO 3 2- , which is neutralized and converted to HP H 13 CO 3 - . After injection, HP H 13 CO 3 - equilibrates with HP 13 CO 2 in vivo and enables the imaging of pH e . Using insights gleaned from mechanistic studies performed in the hyperpolarized state, we solved issues of polarization loss during preparation in a clinical polarizer system. We successfully customized a reaction apparatus suitable for clinical application, developed clinical standard operating procedures, and validated the radiofrequency pulse sequence and imaging data acquisition with a wide range of animal models. The results demonstrated that we can routinely produce a highly polarized and safe HP H 13 CO 3 - contrast agent suitable for human injection. Preclinical imaging studies validated the reliability and accuracy of measuring acidification in healthy kidney and prostate tumor tissue. These methods were used to support an Investigational New Drug application to the U.S. Food and Drug Administration. This methodology is now ready to be implemented in human trials, with the ultimate goal of improving the management of PCa.

Hyperpolarized (HP) 13 C Magnetic Resonance Imaging (MRI) was applied for the first time to image and quantify the uptake and metabolism of [2- 13 C]pyruvate in the human brain to provide new metabolic information on cerebral energy metabolism. HP [2- 13 C]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 13 C-labeled volumetric and dynamic images of [2- 13 C]pyruvate and downstream metabolites [5- 13 C]glutamate and [2- 13 C]lactate. Metabolic ratios and apparent conversion rates of pyruvate-to-lactate (k PL ) and pyruvate-to-glutamate (k PG ) were quantified to investigate simultaneously glycolytic and oxidative metabolism in a single injection., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yaewon Kim reports financial support was provided by NIH., (Copyright © 2023. Published by Elsevier Inc.)

Purpose: Three-dimensional UTE MRI has shown the ability to provide simultaneous structural and functional lung imaging, but it is limited by respiratory motion and relatively low lung parenchyma SNR. The purpose of this paper is to improve this imaging by using a respiratory phase-resolved reconstruction approach, named motion-compensated low-rank reconstruction (MoCoLoR), which directly incorporates motion compensation into a low-rank constrained reconstruction model for highly efficient use of the acquired data., Theory and Methods: The MoCoLoR reconstruction is formulated as an optimization problem that includes a low-rank constraint using estimated motion fields to reduce the rank, optimizing over both the motion fields and reconstructed images. The proposed reconstruction along with XD and motion state-weighted motion-compensation (MostMoCo) methods were applied to 18 lung MRI scans of pediatric and young adult patients. The data sets were acquired under free-breathing and without sedation with 3D radial UTE sequences in approximately 5 min. After reconstruction, they went through ventilation analyses. Performance across reconstruction regularization and motion-state parameters were also investigated., Results: The in vivo experiments results showed that MoCoLoR made efficient use of the data, provided higher apparent SNR compared with state-of-the-art XD reconstruction and MostMoCo reconstructions, and yielded high-quality respiratory phase-resolved images for ventilation mapping. The method was effective across the range of patients scanned., Conclusion: The motion-compensated low-rank regularized reconstruction approach makes efficient use of acquired data and can improve simultaneous structural and functional lung imaging with 3D-UTE MRI. It enables the scanning of pediatric patients under free-breathing and without sedation., (© 2023 International Society for Magnetic Resonance in Medicine.)

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.

13 C-bicarbonate is a crucial measure of pyruvate oxidation and TCA cycle flux, but is challenging to measure due to its relatively low concentration and thus will greatly benefit from improved signal-to-noise ratio (SNR). To address this, we developed and investigated the feasibility of a 3D stack-of-spirals metabolite-specific balanced steady-state free precession (MS-bSSFP) sequence for improving the SNR and spatial resolution of dynamic 13 C-bicarbonate imaging in hyperpolarized [1- 13 C]pyruvate studies. The bicarbonate MS-bSSFP sequence was evaluated by simulations, phantoms studies, preclinical studies on five rats, brain studies on two healthy volunteers and renal study on one renal cell carcinoma patient. The simulations and phantom results showed that the bicarbonate-specific pulse had minimal perturbation of other metabolites (<1%). In the animal studies, the MS-bSSFP sequence provided an approximately 2.6-3 × improvement in 13 C-bicarbonate SNR compared to a metabolite-specific gradient echo (MS-GRE) sequence without altering the bicarbonate or pyruvate kinetics, and the shorter spiral readout in the MS-bSSFP approach reduced blurring. Using the SNR ratio between MS-bSSFP and MS-GRE, the T 2 values of bicarbonate and lactate in the rat kidneys were estimated as 0.5 s and 1.1 s, respectively. The in-vivo feasibility of bicarbonate MS-bSSFP sequence was demonstrated in two human brain studies and one renal study. These studies demonstrate the potential of the sequence for in-vivo applications, laying the foundation for future studies to observe this relatively low concentration metabolite with high-quality images and improve measurements of pyruvate oxidation., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Peder Larson reports equipment, drugs, or supplies was provided by GE Healthcare. Shuyu Tang reports a relationship with Heartvista Inc that includes: employment.], (Copyright © 2023 Elsevier Inc. All rights reserved.)

The non-uniform Discrete Fourier Transform algorithm has shown great utility for reconstructing images from non-uniformly spaced Fourier samples in several imaging modalities. Due to the non-uniform spacing, some correction for the variable density of the samples must be made. Common methods for generating density compensation values are either sub-optimal or only consider a finite set of points in the optimization. This manuscript presents an algorithm for generating density compensation values from a set of Fourier samples that takes into account the point spread function over an entire rectangular region in the image domain. We show that the reconstructed images using the density compensation values of this method are of superior quality when compared to other standard methods. Results are shown with a numerical phantom and with magnetic resonance images of the abdomen and the knee., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 18 F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training, we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET (sPET) time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic 18 F-FDG uptake, e.g., high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart, and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this sPET data can be used interchangeably with real PET data for the PET quantification task of comparing CTAC and MRAC methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed sPET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.

Rationale and Objectives: Early prostate cancer detection and staging from MRI is extremely challenging for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their performance within and across institutions. To enable this for prototype-stage algorithms, where the majority of existing research remains, we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of custom deep learning prostate cancer detection algorithms., Materials and Methods: We introduce an abstraction of prostate cancer groundtruth that represents diverse annotation and histopathology data. We maximize use of this groundtruth if and when they are available using UCNet, a custom 3D UNet that enables simultaneous supervision of pixel-wise, region-wise, and gland-wise classification. We leverage these modules to perform cross-site federated training using 1400+ heterogeneous multi-parameteric prostate MRI exams from two University hospitals., Results: We observe a positive result, with significant improvements in cross-site generalization performance with negligible intra-site performance degradation for both lesion segmentation and per-lesion binary classification of clinically-significant prostate cancer. Cross-site lesion segmentation performance intersection-over-union (IoU) improved by 100%, while cross-site lesion classification performance overall accuracy improved by 9.5-14.8%, depending on the optimal checkpoint selected by each site., Conclusion: Federated learning can improve the generalization performance of prostate cancer detection models across institutions while protecting patient health information and institution-specific code and data. However, even more data and participating institutions are likely required to improve the absolute performance of prostate cancer classification models. To enable adoption of federated learning with limited re-engineering of federated components, we open-source our FLtools system at https://federated.ucsf.edu, including examples that can be easily adapted to other medical imaging deep learning projects., Competing Interests: Declaration of Competing Interest Authors have no competing interests to declare., (Copyright © 2023. Published by Elsevier Inc.)

Metabolite-specific echo-planar imaging (EPI) sequences with spectral-spatial (spsp) excitation are commonly used in clinical hyperpolarized [1- 13 C]pyruvate studies because of their speed, efficiency, and flexibility. In contrast, preclinical systems typically rely on slower spectroscopic methods, such as chemical shift imaging (CSI). In this study, a 2D spspEPI sequence was developed for use on a preclinical 3T Bruker system and tested on in vivo mice experiments with patient-derived xenograft renal cell carcinoma (RCC) or prostate cancer tissues implanted in the kidney or liver. Compared to spspEPI sequences, CSI were found to have a broader point spread function via simulations and exhibited signal bleeding between vasculature and tumors in vivo. Parameters for the spspEPI sequence were optimized using simulations and verified with in vivo data. The expected lactate SNR and pharmacokinetic modeling accuracy increased with lower pyruvate flip angles (less than 15°), intermediate lactate flip angles (25° to 40°), and temporal resolution of 3 s. Overall SNR was also higher with coarser spatial resolution (4 mm isotropic vs. 2 mm isotropic). Pharmacokinetic modelling used to fit k PL maps showed results consistent with the previous literature and across different sequences and tumor xenografts. This work describes and justifies the pulse design and parameter choices for preclinical spspEPI hyperpolarized 13 C-pyruvate studies and shows superior image quality to CSI.

Despite the proliferation of deep learning techniques for accelerated MRI acquisition and enhanced image reconstruction, the construction of large and diverse MRI datasets continues to pose a barrier to effective clinical translation of these technologies. One major challenge is in collecting the MRI raw data (required for image reconstruction) from clinical scanning, as only magnitude images are typically saved and used for clinical assessment and diagnosis. The image phase and multi-channel RF coil information are not retained when magnitude-only images are saved in clinical imaging archives. Additionally, preprocessing used for data in clinical imaging can lead to biased results. While several groups have begun concerted efforts to collect large amounts of MRI raw data, current databases are limited in the diversity of anatomy, pathology, annotations, and acquisition types they contain. To address this, we present a method for synthesizing realistic MR data from magnitude-only data, allowing for the use of diverse data from clinical imaging archives in advanced MRI reconstruction development. Our method uses a conditional GAN-based framework to generate synthetic phase images from input magnitude images. We then applied ESPIRiT to derive RF coil sensitivity maps from fully sampled real data to generate multi-coil data. The synthetic data generation method was evaluated by comparing image reconstruction results from training Variational Networks either with real data or synthetic data. We demonstrate that the Variational Network trained on synthetic MRI data from our method, consisting of GAN-derived synthetic phase and multi-coil information, outperformed Variational Networks trained on data with synthetic phase generated using current state-of-the-art methods. Additionally, we demonstrate that the Variational Networks trained with synthetic k -space data from our method perform comparably to image reconstruction networks trained on undersampled real k -space data.

Background: Dynamic hyperpolarized (HP)- 13 C MRI has enabled real-time, non-invasive assessment of Warburg-related metabolic dysregulation in glioma using a [1- 13 C]pyruvate tracer that undergoes conversion to [1- 13 C]lactate and [ 13 C]bicarbonate. Using a multi-parametric 1 H/HP- 13 C imaging approach, we investigated dynamic and steady-state metabolism, together with physiological parameters, in high-grade gliomas to characterize active tumor., Methods: Multi-parametric 1 H/HP- 13 C 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 (G4A IDH+ , 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- 13 C/spectroscopic) metabolic data were collectively examined at the 13 C resolution. Prog/TN GBM were similarly compared against progressive G4A IDH+ and treatment effects., Results: Regional analysis of Prog/TN GBM metabolism revealed statistically significant heterogeneity in 1 H choline-to-N-acetylaspartate index (CNI) max , [1- 13 C]lactate, modified [1- 13 C]lactate-to-[1- 13 C]pyruvate ratio (CEL val  > NEL val  > NAWM val ); [1- 13 C]lactate-to-[ 13 C]bicarbonate ratio (CEL val  > NEL val /NAWM val ); and 1 H-lactate (CEL val /NEL val  > NAWM undetected ). Significant associations were found between normalized perfusion (cerebral blood volume, nCBV; peak height, nPH) and levels of [1- 13 C]pyruvate and [1- 13 C]lactate, as well as between CNI max and levels of [1- 13 C]pyruvate, [1- 13 C]lactate and modified ratio. GBM, by comparison to G4A IDH+ , displayed lower perfusion %-recovery and modeled rate constants for [1- 13 C]pyruvate-to-[1- 13 C]lactate conversion (k PL ), and higher 1 H-lactate and [1- 13 C]pyruvate levels, while having higher nCBV, %-recovery, k PL , [1- 13 C]pyruvate-to-[1- 13 C]lactate and modified ratios relative to treatment effects., Conclusions: GBM consistently displayed aberrant, Warburg-related metabolism and regional heterogeneity detectable by novel HP- 13 C/ 1 H imaging techniques., 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 © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose: To develop techniques and establish a workflow using hyperpolarized carbon-13 ( 13 C) MRI and the pyruvate-to-lactate conversion rate (k PL ) biomarker to guide MR-transrectal ultrasound fusion prostate biopsies., Methods: The integrated multiparametric MRI (mpMRI) exam consisted of a 1-min hyperpolarized 13 C-pyruvate EPI acquisition added to a conventional prostate mpMRI exam. Maps of k PL values were calculated, uploaded to a picture archiving and communication system and targeting platform, and displayed as color overlays on T 2 -weighted anatomic images. Abdominal radiologists identified 13 C research biopsy targets based on the general recommendation of focal lesions with k PL  >0.02(s -1 ), and created a targeting report for each study. Urologists conducted transrectal ultrasound-guided MR fusion biopsies, including the standard 1 H-mpMRI targets as well as 12-14 core systematic biopsies informed by the research 13 C-k PL targets. All biopsy results were included in the final pathology report and calculated toward clinical risk., Results: This study demonstrated the safety and technical feasibility of integrating hyperpolarized 13 C metabolic targeting into routine 1 H-mpMRI and transrectal ultrasound fusion biopsy workflows, evaluated via 5 men (median age 71 years, prostate-specific antigen 8.4 ng/mL, Cancer of the Prostate Risk Assessment score 2) on active surveillance undergoing integrated scan and subsequent biopsies. No adverse event was reported. Median turnaround time was less than 3 days from scan to 13 C-k PL targeting, and scan-to-biopsy time was 2 weeks. Median number of 13 C targets was 1 (range: 1-2) per patient, measuring 1.0 cm (range: 0.6-1.9) in diameter, with a median k PL of 0.0319 s -1 (range: 0.0198-0.0410)., Conclusions: This proof-of-concept work demonstrated the safety and feasibility of integrating hyperpolarized 13 C MR biomarkers to the standard mpMRI workflow to guide MR-transrectal ultrasound fusion biopsies., (© 2022 International Society for Magnetic Resonance in Medicine.)

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: T 1 -weighted spoiled gradient echo, T 2 -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 (ΔB 0 ) determined using multiecho proton acquisitions were compared to carbon-13 acquisitions. Changes in ΔB 0 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 (k PL ) and alanine (k PA ) 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 ΔB 0 maps correlated with carbon-13 ΔB 0 maps (slope = 0.93, y-intercept = -2.88, R 2  = 0.73). Localized shimming resulted in mean frequency offset within ±25 Hz for all organs. Metabolite SNR significantly increased after denoising. Mean k PL and k PA 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., (© 2022 International Society for Magnetic Resonance in Medicine.)

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 mm 2 resolution for pyruvate and 15 × 15 mm 2 resolution for lactate and bicarbonate. With the same lactate data, 2 quantitative maps of pyruvate-to-lactate conversion (k PL ) maps were generated: 1 using 7.5 × 7.5 mm 2 resolution pyruvate data and the other using synthetic 15 × 15 mm 2 resolution pyruvate data to simulate a standard constant resolution acquisition. To examine local k PL 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 mm 2 pyruvate images increased the spatial delineation of brain structures and decreased partial volume effects compared to coarser resolution 15 × 15 mm 2 pyruvate images. Voxels near arteries, veins and in white matter exhibited higher calculated k PL 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., (© 2022 International Society for Magnetic Resonance in Medicine.)

Background: Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MR-only workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation., Purpose: In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning., Methods: MVCTs and T1-weighted MRIs for 120 patients treated for head-and-neck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs)., Results: MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning, or when immobilization devices were absent from diagnostic MRIs. Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively., Conclusions: MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow., (© 2022 American Association of Physicists in Medicine.)

Purpose: We constructed a 13 C/ 31 P surface coil at 3 T for studying cancer metabolism and bioenergetics. In a single scan session, hyperpolarized 13 C-pyruvate MRS and 31 P MRS was carried out for a healthy rat brain., Methods: All experiments were carried out at 3 Tesla. The multinuclear surface coil was designed as two coplanar loops each tuned to either the 13 C or 31 P operating frequency with an LCC trap on the 13 C loop. A commercial volume proton coil was used for anatomical localization and B 0 shimming. Single tuned coils operating at either the 13 C or 31 P frequency were built to evaluate the relative performance of the multinuclear coil. Coil performance metrics consisted of measuring Q factor ratio, calculating system input power using a single-pulse acquisition, and acquiring SNR and flip angle maps using 2D CSI sequences. To observe in vivo spectra, a bolus of hyperpolarized [1- 13 C] pyruvate was administered via tail vein. In vivo 13 C and endogenous 31 P spectra were obtained in a single scan session using 1D slice selective acquisitions., Results: When compared with single tuned surface coils, the multinuclear coil performance showed a decrease in Q factor ratio, SNR, and transmit efficiency. Flip angle maps showed adequate flip angles within the phantom when the transmit voltage was set using an external phantom. Results show good detection of 13 C labeled lactate, alanine, and bicarbonate in addition to ATP from 31 P MRS., Conclusions: The coil enables obtaining complementary information within a scan session, thus reducing the number of trials and minimizing biological variability for studies of metabolism and bioenergetics., 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 Elsevier Inc. All rights reserved.)

Purpose: This study aimed to develop and demonstrate the in vivo feasibility of a 3D stack-of-spiral balanced steady-state free precession(3D-bSSFP) urea sequence, interleaved with a metabolite-specific gradient echo (GRE) sequence for pyruvate and metabolic products, for improving the SNR and spatial resolution of the first hyperpolarized 13 C-MRI human study with injection of co-hyperpolarized [1- 13 C]pyruvate and [ 13 C, 15 N 2 ]urea., Methods: A metabolite-specific bSSFP urea imaging sequence was designed using a urea-specific excitation pulse, optimized TR, and 3D stack-of-spiral readouts. Simulations and phantom studies were performed to validate the spectral response of the sequence. The image quality of urea data acquired by the 3D-bSSFP sequence and the 2D-GRE sequence was evaluated with 2 identical injections of co-hyperpolarized [1- 13 C]pyruvate and [ 13 C, 15 N 2 ]urea formula in a rat. Subsequently, the feasibility of the acquisition strategy was validated in a prostate cancer patient., Results: Simulations and phantom studies demonstrated that 3D-bSSFP sequence achieved urea-only excitation, while minimally perturbing other metabolites (<1%). An animal study demonstrated that compared to GRE, bSSFP sequence provided an ∼2.5-fold improvement in SNR without perturbing urea or pyruvate kinetics, and bSSFP approach with a shorter spiral readout reduced blurring artifacts caused by J-coupling of [ 13 C, 15 N 2 ]urea. The human study demonstrated the in vivo feasibility and data quality of the acquisition strategy., Conclusion: The 3D-bSSFP urea sequence with a stack-of-spiral acquisition demonstrated significantly increased SNR and image quality for [ 13 C, 15 N 2 ]urea in co-hyperpolarized [1- 13 C]pyruvate and [ 13 C, 15 N 2 ]urea imaging studies. This work lays the foundation for future human studies to achieve high-quality and high-SNR metabolism and perfusion images., (© 2022 International Society for Magnetic Resonance in Medicine.)

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echotime and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.

Objective . Kilovoltage computed tomography (kVCT) is the cornerstone of radiotherapy treatment planning for delineating tissues and towards dose calculation. For the former, kVCT provides excellent contrast and signal-to-noise ratio. For the latter, kVCT may have greater uncertainty in determining relative electron density (ρe) and proton stopping power ratio (SPR). Conversely, megavoltage CT (MVCT) may result in superior dose calculation accuracy. The purpose of this work was to convert kVCT HU to MVCT HU using deep learning to obtain higher accuracyρeand SPR. Approach . Tissue-mimicking phantoms were created to compare kVCT- and MVCT-determinedρeand SPR to physical measurements. Using 100 head-and-neck datasets, an unpaired deep learning model was trained to learn the relationship between kVCTs and MVCTs, creating synthetic MVCTs (sMVCTs). Similarity metrics were calculated between kVCTs, sMVCTs, and MVCTs in 20 test datasets. An anthropomorphic head phantom containing bone-mimicking material with known composition was scanned to provide an independent determination ofρeand SPR accuracy by sMVCT. Main results . In tissue-mimicking bone,ρeerrors were 2.20% versus 0.19% and SPR errors were 4.38% versus 0.22%, for kVCT versus MVCT, respectively. Compared to MVCT, in vivo mean difference (MD) values were 11 and 327 HU for kVCT and 2 and 3 HU for sMVCT in soft tissue and bone, respectively.ρeMD decreased from 1.3% to 0.35% in soft tissue and 2.9% to 0.13% in bone, for kVCT and sMVCT, respectively. SPR MD decreased from 1.8% to 0.24% in soft tissue and 6.8% to 0.16% in bone, for kVCT and sMVCT, respectively. Relative to physical measurements,ρeand SPR error in anthropomorphic bone decreased from 7.50% and 7.48% for kVCT to <1% for both MVCT and sMVCT. Significance . Deep learning can be used to map kVCT to sMVCT, suggesting higher accuracyρeand SPR is achievable with sMVCT versus kVCT., (© 2022 Institute of Physics and Engineering in Medicine.)

Purpose: The goal of this study was to combine a specialized acquisition method with a new quantification pipeline to accurately and efficiently probe the metabolism of hyperpolarized 13 C-labeled compounds in vivo. In this study, we tested our approach on [2- 13 C]pyruvate and [1- 13 C]α-ketoglutarate data in rat orthotopic brain tumor models at 3T., Methods: We used a multiband metabolite-specific radiofrequency (RF) excitation in combination with a variable flip angle scheme to minimize substrate polarization loss and measure fast metabolic processes. We then applied spectral-temporal denoising using singular value decomposition to enhance spectral quality. This was combined with LCModel-based automatic 13 C spectral fitting and flip angle correction to separate overlapping signals and rapidly quantify the different metabolites., Results: Denoising improved the metabolite signal-to-noise ratio (SNR) by approximately 5. It also improved the accuracy of metabolite quantification as evidenced by a significant reduction of the Cramer Rao lower bounds. Furthermore, the use of the automated and user-independent LCModel-based quantification approach could be performed rapidly, with the kinetic quantification of eight metabolite peaks in a 12-spectrum array achieved in less than 1 minute., Conclusion: The specialized acquisition method combined with denoising and a new quantification pipeline using LCModel for the first time for hyperpolarized 13 C data enhanced our ability to monitor the metabolism of [2- 13 C]pyruvate and [1- 13 C]α-ketoglutarate in rat orthotopic brain tumor models in vivo. This approach could be broadly applicable to other hyperpolarized agents both preclinically and in the clinical setting., (© 2021 International Society for Magnetic Resonance in Medicine.)

Background: Magnetic resonance imaging (MRI) with hyperpolarized [1- 13 C]pyruvate allows real-time and pathway specific clinical detection of otherwise unimageable in vivo metabolism. However, the comparability between sites and protocols is unknown. Here, we provide initial experiences on the agreement of hyperpolarized MRI between sites and protocols by repeated imaging of same healthy volunteers in Europe and the US., Methods: Three healthy volunteers traveled for repeated multicenter brain MRI exams with hyperpolarized [1- 13 C]pyruvate within one year. First, multisite agreement was assessed with the same echo-planar imaging protocol at both sites. Then, this was compared to a variable resolution echo-planar imaging protocol. In total, 12 examinations were performed. Common metrics of 13 C-pyruvate to 13 C-lactate conversion were calculated, including the k PL , a model-based kinetic rate constant, and its model-free equivalents. Repeatability was evaluated with intraclass correlation coefficients (ICC) for absolute agreement computed using two-way random effects models., Results: The mean k PL across all examinations in the multisite comparison was 0.024 ± 0.0016 s -1 . The ICC of the k PL was 0.83 ( p = 0.14) between sites and 0.7 ( p = 0.09) between examinations of the same volunteer at any of the two sites. For the model-free metrics, the lactate Z-score had similar site-to-site ICC, while it was considerably lower for the lactate-to-pyruvate ratio., Conclusions: Estimation of metabolic conversion from hyperpolarized [1- 13 C]pyruvate to lactate using model-based metrics such as k PL suggests close agreement between sites and examinations in volunteers. Our initial results support harmonization of protocols, support multicenter studies, and inform their design.

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.)