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Background Post-COVID-19 condition encompasses symptoms following COVID-19 infection that linger at least 4 weeks after the end of active infection. Symptoms are wide ranging, but breathlessness is common. Purpose To determine if the previously described lung abnormalities seen on hyperpolarized (HP) pulmonary xenon 129 (129Xe) MRI scans in participants with post-COVID-19 condition who were hospitalized are also present in participants with post-COVID-19 condition who were not hospitalized. Materials and Methods In this prospective study, nonhospitalized participants with post-COVID-19 condition (NHLC) and posthospitalized participants with post-COVID-19 condition (PHC) were enrolled from June 2020 to August 2021. Participants underwent chest CT, HP 129Xe MRI, pulmonary function testing, and the 1-minute sit-to-stand test and completed breathlessness questionnaires. Control subjects underwent HP 129Xe MRI only. CT scans were analyzed for post-COVID-19 interstitial lung disease severity using a previously published scoring system and full-scale airway network (FAN) modeling. Analysis used group and pairwise comparisons between participants and control subjects and correlations between participant clinical and imaging data. Results A total of 11 NHLC participants (four men, seven women; mean age, 44 years ± 11 [SD]; 95% CI: 37, 50) and 12 PHC participants (10 men, two women; mean age, 58 years ±10; 95% CI: 52, 64) were included, with a significant difference in age between groups (P = .05). Mean time from infection was 287 days ± 79 (95% CI: 240, 334) and 143 days ± 72 (95% CI: 105, 190) in NHLC and PHC participants, respectively. NHLC and PHC participants had normal or near normal CT scans (mean, 0.3/25 ± 0.6 [95% CI: 0, 0.63] and 7/25 ± 5 [95% CI: 4, 10], respectively). Gas transfer (DLCO) was different between NHLC and PHC participants (mean DLCO, 76% ± 8 [95% CI: 73, 83] vs 86% ± 8 [95% CI: 80, 91], respectively; P = .04), but there was no evidence of other differences in lung function. Mean red blood cell–to-tissue plasma ratio was different between volunteers (mean, 0.45 ± 0.07; 95% CI: 0.43, 0.47]) and PHC participants (mean, 0.31 ± 0.10; 95% CI: 0.24, 0.37; P = .02) and between volunteers and NHLC participants (mean, 0.37 ± 0.10; 95% CI: 0.31, 0.44; P = .03) but not between NHLC and PHC participants (P = .26). FAN results did not correlate with DLCO) or HP 129Xe MRI results. Conclusion Nonhospitalized participants with post-COVID-19 condition (NHLC) and posthospitalized participants with post-COVID-19 condition (PHC) showed hyperpolarized pulmonary xenon 129 MRI and red blood cell–to-tissue plasma abnormalities, with NHLC participants demonstrating lower gas transfer than PHC participants despite having normal CT findings.

Objective: Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. Methods: 1027 signal–time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen’s κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal–time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. Results: Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. Conclusion: The reviewers showed good agreement. Machine learning classifiers trained on signal–time course measures and QR can assess quality. Combining multiple measures reduces misclassification. Advances in knowledge: A new automated quality control method was developed, which trained machine learning classifiers using QR results.

Purpose: To investigate the potential effects of [1- 13C]lactate RF saturation pulses on [ 13C]bicarbonate detection in hyperpolarized [1- 13C]pyruvate MRI of the brain. Methods: Thirteen healthy rats underwent MRI with hyperpolarized [1- 13C]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- 13C]lactate was excited with either 0° or 90° flip angles. The [ 13C]bicarbonate SNR and apparent [1- 13C]pyruvate-to-[ 13C]bicarbonate conversion (k PB) were determined. Furthermore, simulations were performed to identify the SNR optimal flip-angle scheme for detection of [1- 13C]lactate and [ 13C]bicarbonate. Results: In the brain, the [ 13C]bicarbonate SNR was 64% higher when [1- 13C]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- 13C]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 [ 13C]bicarbonate SNR with a TR of 1 s in the brain is obtained with [ 13C]bicarbonate, [1- 13C]lactate, and [1- 13C]pyruvate flip angles of 60°, 15°, and 10°, respectively. Conclusions: Radiofrequency saturation pulses on [1- 13C]lactate limit [ 13C]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 [ 13C]bicarbonate detection is warranted.

Hyperpolarized carbon-13 MRI is a promising technique forin vivometabolic interrogation of alterations between health and disease. This study introduces a model-free formalism for quantifying the metabolic information in hyperpolarized imaging.This study investigated a novel model-free perfusion and metabolic clearance rate (MCR) model in pre-clinical stroke and in the healthy human brain.Simulations showed that the proposed model was robust to perturbations in T1, transmit B1, and kPL. A significant difference in ipsilateral vs contralateral pyruvate derived cerebral blood flow (CBF) was detected in rats (140 ± 2 vs 89 ± 6 mL/100g/min, p < 0.01, respectively) and pigs (139 ± 12 vs 95 ± 5 mL/100g/min, p = 0.04, respectively), along with an increase in fractional metabolism (26 ± 5 vs 4 ± 2 %, p < 0.01, respectively) in the rodent brain. In addition, a significant increase in ipsilateral vs contralateral MCR (0.034 ± 0.007 vs 0.017 ± 0.02 s-1, p = 0.03, respectively) and a decrease in mean transit time (MTT) (31 ± 8 vs 60 ± 2, p = 0.04, respectively) was observed in the porcine brain. In conclusion, MCR mapping is a simple and robust approach to the post-processing of hyperpolarized magnetic resonance imaging.

Purpose To evaluate glioblastoma (GBM) metabolism by using hyperpolarized carbon 13 (

Age-related white matter hyperintensities are associated with cognitive impairment and dementia. Venous insufficiency has recently been proposed as a potential mechanism for the development of periventricular white matter hyperintensities based on the neuroanatomic distribution. The current study assesses age related changes of the internal jugular veins and its association with white matter hyperintensities. A retrospective study was performed assessing patients with computed tomography angiography (CTA) and magnetic resonance imaging (MRI) within a 4-week window. The size of the internal jugular veins, straight sinus, vein of Galen and internal cerebral veins were measured on the CT angiography. A normalized neck venous ratio was developed. Burden of white matter hyperintensities were quantified on MRI using periventricular/deep Fazekas scores. Association was assessed using correlation analysis and multrivariate linear modeling, and differences between groups were assessed using t test, ANOVA or Kruskal-Wallis test, using p 0.05 for significance. One hundred eighty-two patients were included with a mean age of 65.2 ± 16.8 (51.6% females). Age was correlated with the normalized neck venous ratio (r

Cerebral metabolism is tightly regulated and fundamental for healthy neurological function. There is increasing evidence that alterations in this metabolism may be a precursor and early biomarker of later stage disease processes. Proton magnetic resonance spectroscopy (1H-MRS) is a powerful tool to non-invasively assess tissue metabolites and has many applications for studying the normal and diseased brain. However, the technique has limitations including low spatial and temporal resolution, difficulties in discriminating overlapping peaks, and challenges in assessing metabolic flux rather than steady-state concentrations. Hyperpolarized carbon-13 magnetic resonance imaging is an emerging clinical technique that may overcome some of these spatial and temporal limitations, providing novel insights into neurometabolism in both health and in pathological processes such as glioma, stroke and multiple sclerosis. This review will explore the growing body of pre-clinical data that demonstrates a potential role for the technique in assessing metabolism in the central nervous system. There are now a number of clinical studies being undertaken in this area and this review will present the emerging clinical data as well as the potential future applications of hyperpolarized 13C magnetic resonance imaging in the brain, in both clinical and pre-clinical studies.

Sodium magnetic resonance imaging (MRI) is a powerful, non-invasive technique to assess sodium distribution within the kidney. Here we undertook pre-clinical and clinical studies to quantify the corticomedullary sodium gradient in healthy individuals and in a porcine model of diuresis. The results demonstrated that sodium MRI could detect spatial differences in sodium biodistribution across the kidney. The sodium gradient of the kidney changed significantly after diuresis in the pig model and was independent of blood electrolyte measurements. Thus, rapid sodium MRI can be used to dynamically quantify sodium biodistribution in the porcine and human kidney., Graphical abstract

To determine the effect of altering anesthetic oxygen protocols on measurements of cerebral perfusion and metabolism in the rodent brain.Seven rats were anesthetized and underwent serial MRI scans with hyperpolarized [1-There was a significant increase in bicarbonate:totalThese results demonstrate that the detection and quantification of cerebral metabolism and perfusion is dependent on the oxygen protocol used in the anesthetized rodent brain.

To develop a coil-based method to obtain accurate sensitivity profiles inA flexible coil array was tuned optimally forThe measured sensitivity profiles on a phantom showed high spatial similarity forA receive array has been developed in such a way that the

BackgroundLong-COVID is an umbrella term used to describe ongoing symptoms following COVID-19 infection after four weeks. Symptoms are wide-ranging but breathlessness is one of the most common and can persist for months after the initial infection. Investigations including Computed Tomography (CT), and physiological measurements (lung function tests) are usually unremarkable. The mechanisms driving breathlessness remain unclear, and this may be hindering the development of effective treatments.MethodsEleven non-hospitalised Long-COVID (NHLC, 4 male), 12 post-hospitalised COVID-19 (PHC, 10 male) patients were recruited from a Post-COVID Assessment clinic, and thirteen healthy controls (6 female) were recruited to undergo Hyperpolarized Xenon Magnetic Resonance Imaging (Hp-XeMRI). NHLC and PHC participants underwent contemporaneous CT, Hp-XeMRI, lung function tests, 1-minute sit-to-stand test and breathlessness questionnaires. Statistical analysis included group and pair-wise comparisons between patients and controls, and correlations between patient clinical and imaging data.ResultsNHLC and PHC patients were 287 ± 79 [range 190-437] and 149 ± 68 [range 68-269] days from infection, respectively. All NHLC patients had normal CT scans, and the PHC had normal or near normal CT scans (0.3/25 ± 0.6 [range 0-2] and 7/25 ± 5 [range 4-8], respectively). There was a significant difference in TLco (%) between NHLC and PHC patients (76 ± 8 % vs 86 ± 8%, respectively, p = 0.04) but no differences in other measurements of lung function. There were significant differences in RBC:TP mean between volunteers (0.45 ± 0.07, range [0.33-0.55]) and PHC (0.31 ± 0.11, [range 0.16-0.37]) and NHLC (0.35 ± 0.09, [range 0.26-0.58]) patients, but not between NHLC and PHC (p = 0.26).ConclusionThere are RBC:TP abnormalities in NHLC and PHC patients, with NHLC patients also demonstrating lower TLco than PHC patients despite their having normal CT scans. These abnormalities are present many months after the initial infection.Summary statementHyperpolarized Xenon MRI and TLco demonstrate significantly impaired gas transfer in non-hospitalised long-COVID patients when all other investigations are normal.Key resultsThere are significant differences in RBC:TP mean between healthy controls and PHC/NHLC patients (0.45 ± 0.07, range [0.33-0.55], 0.31 ± 0.11, [range 0.16-0.37], 0.35 ± 0.09, [range 0.26-0.58], respectively, p < 0.05 after correction for multiple comparisons) indicating a change in lung compartment volumes between groups.There was a significant difference in TLco (%) between NHLC and PHC patients (76 ± 8 % vs 86 ± 8%, respectively, p = 0.04), despite normal or near normal FEV (%) (100 ± 13% [range 72-123%] and 88 ± 21% [range 62-113%], p>0.05.There were significant differences in CT abnormalities between NHLC and PHC patients (0.3/25 ± 0.6 [range 0-2] and 7/25 ± 5 [range 4-8], respectively) despite similarly impaired RBC:TP.

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi‐class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi‐site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi‐class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave‐one‐out and k‐fold cross‐validation. Metabolites identified as crucial in tumour classification include myo‐inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N‐acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave‐one‐out cross‐validation was 85% for 1.5 T 1H‐MRS through support vector machine and 75% for 3 T 1H‐MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

Aims Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma. Method The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm and Naïve Bayes five-fold cross-validation to validate the final model. Results Treatment response assessment based on the standard-of-care reports by clinical neuroradiologists showed an accuracy of 33% (sensitivity/specificity 52%/3%) to distinguish between tPD and psPD from the early post-treatment MRI study at 4-6 weeks. Machine learning-based models based on clinical and molecular features alone demonstrated an AUC of 0.66 and models using radiomic features alone from the early post-treatment MRI demonstrated an AUC of 0.46-0.69 depending on the feature and mask subset. A combined clinico-radiomic model utilising top common features demonstrated an AUC of 0.80 and an accuracy of 74% (sensitivity/specificity 78%/67%). The features in the final model were age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask (elongation and sphericity), three radiomic features from the enhancing disease mask on ADC (kurtosis, correlation, contrast) and one radiomic feature from the perilesional oedema mask on T2WI (dependence entropy). Conclusion Current standard-of-care glioblastoma treatment response assessment imaging has limitations. In this study, the use of AI through a machine learning-based approach incorporating clinical characteristics and MGMT promoter methylation status with quantitative radiomic features from standard MRI sequences at early 4-6 weeks post-treatment imaging showed the best model performance and a higher accuracy to distinguish between tPD and psPD for early prediction of glioblastoma treatment response.

Molecular imaging techniques have rapidly progressed over recent decades providing unprecedented in vivo characterization of metabolic pathways and molecular biomarkers. Many of these new techniques have been successfully applied in the field of neuro-oncological imaging to probe tumor biology. Targeting specific signaling or metabolic pathways could help to address several unmet clinical needs that hamper the management of patients with brain tumors. This review aims to provide an overview of the recent advances in brain tumor imaging using molecular targeting with positron emission tomography and magnetic resonance imaging, as well as the role in patient management and possible therapeutic implications.

Purpose Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). Results Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.

Background:SARS-CoV-2 targets angiotensin-converting enzyme 2 (ACE2) expressing cells in the respiratory tract. There are reports of breathlessness in patients many months post-infection. Purpose:This study aimedd to determine if hyperpolarized129Xe MRI (XeMRI) imaging could identify the possible cause of breathlessness in patients three months after hospital discharge following COVID-19 infection. Materials and Methods:This prospective study was undertaken between August and December 2020, with patients and healthy control volunteers enrolled. All patients underwent: lung function tests; ventilation and dissolved phase XeMRI, with the mean Red Blood Cell (RBC):Tissue Plasma (TP) ratio to be calculated; and a low dose chest CT scored for the degree of post-COVID-19 abnormalities. Healthy controls underwent XeMRI. The intraclass correlation coefficient was calculated for volunteer and patient scans, to assess repeatability. A Wilcoxon rank-sum test and Cohen's effect size calculated to assess for differences between RBC:TP in patient and controls. Results:9 patients (mean age 57±7 years, Male = 6) and 5 volunteers (29 ± 3 years, Female = 5) were enrolled. Patient mean time from hospital discharge was 169, range 116-254 days. There was a difference in RBC:TP between patients and controls (0.3 ± 0.1 versus 0.5 ± 0.1, respectively, p = 0.001, effect size = 1.36). There was significant difference between the RBC and gas phase spectral full width at half maximum (FWHM) between volunteers and patients (median ± 95 % confidence interval, 567 ± 1 vs 507 ± 81, p = 0.002 and 104 ± 2 vs 122 ± 17, p = 0.004, respectively). Results were reproducible with Intraclass Correlation Coefficients of 0.82 and 0.88 for patients and volunteers respectively. Participants had normal or near normal CT scans, mean 7/25, range 0-10/25. Conclusion:Xe MRI showed alveolar-capillary diffusion limitation in all 9 post COVID-19 pneumonia patients despite normal or nearly normal CT scans. See also the editorial byDietrich.

PURPOSE: To assess the utility of Laplacian fitting to describe the differences in hyperpolarized [13C, 15N]urea T2 relaxation in ischemic and healthy rodent kidneys.METHODS: Six rats with unilateral renal ischemia were investigated. [13C, 15N]urea T2 mapping was undertaken with a radial fast spin echo method, with subsequent postprocessing performed with regularized Laplacian fitting.RESULTS: Simulations showed that Laplacian fitting was stable down to a signal-to-noise ratio of 20. In vivo results showed a significant increase in the mono- and decrease in biexponential pools in ischemia reperfusion injury kidneys, in comparison to healthy (14 ± 10% versus 4 ± 2%, 85 ± 10% versus 95 ± 3%; P < .05).CONCLUSION: We demonstrate, for the first time, the differences in multiexponential behavior of [13C, 15N]urea between the healthy and ischemic rodent kidney. The distribution of relaxation pools were found to be both visually and numerically significantly different. The ability to improve the information level in hyperpolarized MR, by using the relaxation contrast mechanisms is an appealing option, that can easily be adopted in large animals and even in clinical studies in the near future.

The aim of this study was to assess the feasibility of rapid sodium MRI (23Na-MRI) for the imaging of peritoneal cancer deposits in high grade serous ovarian cancer (HGSOC) and to evaluate the relationship of 23Na-MRI with tumour cellularity. 23Na-MRI was performed at 3 T on twelve HGSOC patients using a 3D-cones acquisition technique. Tumour biopsies specimens were collected after imaging and cellularity was measured from histology. Total 23Na-MRI scan time for each patient was approximately 11 min. At an isotropic resolution of 5.6 mm, signal-to-noise ratios (SNRs) of 82.2 ± 15.3 and 15.1 ± 7.1 (mean ± standard deviation) were achieved for imaging of tumour tissue sodium concentration (TSC) and intracellular weighted sodium concentration (IWS) respectively. Tumour TSC and IWS concentrations were: 56.8 ± 19.1 mM and 30.8 ± 9.2 mM respectively and skeletal muscle TSC and IWS concentrations were 33.2 ± 16.3 mM and 20.5 ± 9.9 mM respectively. There were significant sodium concentration differences between cancer and skeletal muscle, Wilcoxon signed-rank test, P

Funder: CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester; doi: http://dx.doi.org/10.13039/501100014679, Funder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266, Funder: CRUK Cambridge Centre, Funder: Addenbrooke's Charitable Trust, Cambridge University Hospitals; doi: http://dx.doi.org/10.13039/501100002927, Funder: National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre (BRC), Funder: Experimental Cancer Medicine Centre (ECMC), Funder: The Lundbeck Foundation, Funder: Evelyn Trust; doi: http://dx.doi.org/10.13039/501100004282, Funder: Mark Foundation for Integrative Cancer Research, Measurements of water diffusion with MRI have been used as a biomarker of tissue microstructure and heterogeneity. In this study, diffusion kurtosis tensor imaging (DKTI) of the brain was undertaken in 10 healthy volunteers at a clinical field strength of 3 T. Diffusion and kurtosis metrics were measured in regions-of-interest on the resulting maps and compared with quantitative analysis of normal post-mortem tissue histology from separate age-matched donors. White matter regions showed low diffusion (0.60 ± 0.04 × 10–3 mm2/s) and high kurtosis (1.17 ± 0.06), consistent with a structured heterogeneous environment comprising parallel neuronal fibres. Grey matter showed intermediate diffusion (0.80 ± 0.02 × 10–3 mm2/s) and kurtosis (0.82 ± 0.05) values. An important finding is that the subcortical regions investigated (thalamus, caudate and putamen) showed similar diffusion and kurtosis properties to white matter. Histological staining of the subcortical nuclei demonstrated that the predominant grey matter was permeated by small white matter bundles, which could account for the similar kurtosis to white matter. Quantitative histological analysis demonstrated higher mean tissue kurtosis and vector standard deviation values for white matter (1.08 and 0.81) compared to the subcortical regions (0.34 and 0.59). Mean diffusion on DKTI was positively correlated with tissue kurtosis (r = 0.82, p < 0.05) and negatively correlated with vector standard deviation (r = -0.69, p < 0.05). This study demonstrates how DKTI can be used to study regional structural variations in the cerebral tissue microenvironment and could be used to probe microstructural changes within diseased tissue in the future.

Sodium handling is a key physiological hallmark of renal function. Alterations are generally considered a pathophysiologic event associated with kidney injury, with disturbances in the corticomedullary sodium gradient being indicative of a number of conditions. This experimental protocol review describes the individual steps needed to perform 23Na MRI; allowing accurate monitoring of the renal sodium distribution in a step-by-step experimental protocol for rodents.This chapter is based upon work from the PARENCHIMA COST Action, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This experimental protocol chapter is complemented by two separate chapters describing the basic concept and data analysis.

The handling of sodium by the renal system is a key indicator of renal function. Alterations in the corticomedullary distribution of sodium are considered important indicators of pathology in renal diseases. The derangement of sodium handling can be noninvasively imaged using sodium magnetic resonance imaging (23Na MRI), with data analysis allowing for the assessment of the corticomedullary sodium gradient. Here we introduce sodium imaging, describe the existing methods, and give an overview of preclinical sodium imaging applications to illustrate the utility and applicability of this technique for measuring renal sodium handling.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by two separate chapters describing the experimental procedure and data analysis.

The signal acquired in sodium (23Na) MR imaging is proportional to the concentration of sodium in a voxel, and it is possible to convert between the two using external calibration phantoms. Postprocessing, and subsequent analysis, of sodium renal images is a simple task that can be performed with readily available software. Here we describe the process of conversion between sodium signal and concentration, estimation of the corticomedullary sodium gradient and the procedure used for quadrupolar relaxation analysis.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.

No abstract is available for this article.

The adoptive transfer of regulatory T-cells (Tregs) is a promising therapeutic approach in transplantation and autoimmunity. However, because large cell numbers are needed to achieve a therapeutic effect, in vitro expansion is required. By comparing their function, phenotype and transcriptomic profile against ex vivo Tregs, we demonstrate that expanded human Tregs switch their metabolism to aerobic glycolysis and show enhanced suppressive function through hypoxia-inducible factor 1-alpha (HIF1A) driven acquisition of CD73 expression. In conjunction with CD39, CD73 expression enables expanded Tregs to convert ATP to immunosuppressive adenosine. We conclude that for maximum therapeutic benefit, Treg expansion protocols should be optimised for CD39/CD73 co-expression., Jarvis et al demonstrate that expanded human Tregs switch their metabolism to aerobic glycolysis and show enhanced suppressive function through hypoxia-inducible factor 1-alpha (HIF1A)-driven acquisition of CD73 expression, which along with CD39, enables expanded Tregs to convert ATP to immunosuppressive adenosine. Given this, the data suggests that Treg expansion protocols should be optimised for CD39/CD73 co-expression to enhance therapeutic potential.

Purpose: Calibration of hyperpolarized 13C-MRI is limited by the low signal from endogenous carbon-containing molecules and consequently requires 13C-enriched external phantoms. This study investigated the feasibility of using either 23Na-MRI or 1H-MRI to calibrate the 13C excitation. Methods: Commercial 13C-coils were used to estimate the transmit gain and center frequency for 13C and 23Na resonances. Simulations of the transmit B1 profile of a Helmholtz loop were performed. Noise correlation was measured for both nuclei. A retrospective analysis of human data assessing the use of the 1H resonance to predict [1-13C]pyruvate center frequency was also performed. In vivo experiments were undertaken in the lower limbs of 6 pigs following injection of hyperpolarized 13C-pyruvate. Results: The difference in center frequencies and transmit gain between tissue 23Na and [1-13C]pyruvate was reproducible, with a mean scale factor of 1.05179 ± 0.00001 and 10.4 ± 0.2 dB, respectively. Utilizing the 1H water peak, it was possible to retrospectively predict the 13C-pyruvate center frequency with a standard deviation of only 11 Hz sufficient for spectral–spatial excitation-based studies. Conclusion: We demonstrate the feasibility of using the 23Na and 1H resonances to calibrate the 13C transmit B1 using commercially available 13C-coils. The method provides a simple approach for in vivo calibration and could improve clinical workflow.

A major issue in identification of protective T cell responses against SARS-CoV-2 lies in distinguishing people infected with SARS-CoV-2 from those with cross-reactive immunity generated by exposure to other coronaviruses. We characterised SARS-CoV-2 T cell immune responses in 168 PCR-confirmed SARS-CoV-2 infected subjects and 118 seronegative subjects without known SARS-CoV-2 exposure using a range of T cell assays that differentially capture immune cell function. Strong ex vivo ELISpot and proliferation responses to multiple antigens (including M, NP and ORF3) were found in those who had been infected by SARS-CoV-2 but were rare in pre-pandemic and unexposed seronegative subjects. However, seronegative doctors with high occupational exposure and recent COVID-19 compatible illness showed patterns of T cell responses characteristic of infection, indicating that these readouts are highly sensitive. By contrast, over 90% of convalescent or unexposed people showed proliferation and cellular lactate responses to spike subunits S1/S2, indicating pre-existing cross-reactive T cell populations. The detection of T cell responses to SARS-CoV-2 is therefore critically dependent on the choice of assay and antigen. Memory responses to specific non-spike proteins provides a method to distinguish recent infection from pre-existing immunity in exposed populations.

Rationale: The recent development of hyperpolarized 13 C magnetic resonance spectroscopy has made it possible to measure cellular metabolism in vivo, in real time. Objective: By comparing participants with and without type 2 diabetes mellitus (T2DM), we report the first case-control study to use this technique to record changes in cardiac metabolism in the healthy and diseased human heart. Methods and Results: Thirteen people with T2DM (glycated hemoglobin, 6.9±1.0%) and 12 age-matched healthy controls underwent assessment of cardiac systolic and diastolic function, myocardial energetics ( 31 P-magnetic resonance spectroscopy), and lipid content ( 1 H-magnetic resonance spectroscopy) in the fasted state. In a subset (5 T2DM, 5 control), hyperpolarized [1- 13 C]pyruvate magnetic resonance spectra were also acquired and in 5 of these participants (3 T2DM, 2 controls), this was successfully repeated 45 minutes after a 75 g oral glucose challenge. Downstream metabolism of [1- 13 C]pyruvate via PDH (pyruvate dehydrogenase, [ 13 C]bicarbonate), lactate dehydrogenase ([1- 13 C]lactate), and alanine transaminase ([1- 13 C]alanine) was assessed. Metabolic flux through cardiac PDH was significantly reduced in the people with T2DM (Fasted: 0.0084±0.0067 [Control] versus 0.0016±0.0014 [T2DM], Fed: 0.0184±0.0109 versus 0.0053±0.0041; P =0.013). In addition, a significant increase in metabolic flux through PDH was observed after the oral glucose challenge ( P Conclusions: This work represents the first demonstration of the ability of hyperpolarized 13 C magnetic resonance spectroscopy to noninvasively assess physiological and pathological changes in cardiac metabolism in the human heart. In doing so, we highlight the potential of the technique to detect and quantify metabolic alterations in the setting of cardiovascular disease.

Purpose To compare hyperpolarized carbon 13 (13C) MRI with dynamic contrast material–enhanced (DCE) MRI in the detection of early treatment response in breast cancer. Materials and Methods In this institutional review board–approved prospective study, a woman with triple-negative breast cancer (age, 49 years) underwent 13C MRI after injection of hyperpolarized [1–carbon 13 {13C}]-pyruvate and DCE MRI at 3 T at baseline and after one cycle of neoadjuvant therapy. The 13C-labeled lactate-to-pyruvate ratio derived from hyperpolarized 13C MRI and the pharmacokinetic parameters transfer constant (Ktrans) and washout parameter (kep) derived from DCE MRI were compared before and after treatment. Results Exchange of the 13C label between injected hyperpolarized [1-13C]-pyruvate and the endogenous lactate pool was observed, catalyzed by the enzyme lactate dehydrogenase. After one cycle of neoadjuvant chemotherapy, a 34% reduction in the 13C-labeled lactate-to-pyruvate ratio resulted in correct identification of the patient as a responder to therapy, which was subsequently confirmed via a complete pathologic response. However, DCE MRI showed an increase in mean Ktrans (132%) and mean kep (31%), which could be incorrectly interpreted as a poor response to treatment. Conclusion Hyperpolarized 13C MRI enabled successful identification of breast cancer response after one cycle of neoadjuvant chemotherapy and may improve response prediction when used in conjunction with multiparametric proton MRI. Published under a CC BY 4.0 license.

Supplemental Digital Content is available in the text., Rationale: The recent development of hyperpolarized 13C magnetic resonance spectroscopy has made it possible to measure cellular metabolism in vivo, in real time. Objective: By comparing participants with and without type 2 diabetes mellitus (T2DM), we report the first case-control study to use this technique to record changes in cardiac metabolism in the healthy and diseased human heart. Methods and Results: Thirteen people with T2DM (glycated hemoglobin, 6.9±1.0%) and 12 age-matched healthy controls underwent assessment of cardiac systolic and diastolic function, myocardial energetics (31P-magnetic resonance spectroscopy), and lipid content (1H-magnetic resonance spectroscopy) in the fasted state. In a subset (5 T2DM, 5 control), hyperpolarized [1-13C]pyruvate magnetic resonance spectra were also acquired and in 5 of these participants (3 T2DM, 2 controls), this was successfully repeated 45 minutes after a 75 g oral glucose challenge. Downstream metabolism of [1-13C]pyruvate via PDH (pyruvate dehydrogenase, [13C]bicarbonate), lactate dehydrogenase ([1-13C]lactate), and alanine transaminase ([1-13C]alanine) was assessed. Metabolic flux through cardiac PDH was significantly reduced in the people with T2DM (Fasted: 0.0084±0.0067 [Control] versus 0.0016±0.0014 [T2DM], Fed: 0.0184±0.0109 versus 0.0053±0.0041; P=0.013). In addition, a significant increase in metabolic flux through PDH was observed after the oral glucose challenge (P

Significance Carbon-13 MRI was used to assess exchange of hyperpolarized 13C label between injected [1-13C]pyruvate and the endogenous tumor lactate pool in breast cancer patients. Higher levels of 13C label exchange were observed in more-aggressive tumors, including all triple-negative cancers. The 13C label exchange correlated significantly with the expression of the transmembrane transporter mediating uptake of pyruvate into tumor cells and hypoxia inducible factor 1 (HIF1α), but no significant correlation with the expression of lactate dehydrogenase, the enzyme that catalyzes the exchange. The study has shown that 13C MRI can be used for metabolic imaging of breast cancer patients in the clinic, creating possibilities for noninvasive cancer monitoring in this patient group., Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2−), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2− invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia.

[No abstract available] Funding Agencies|Flinders University; RU, Radboud Universiteit; NIU, Northern Illinois University; University of Nottingham; Universität zu Köln; Charité – Universitätsmedizin Berlin; SLU, Saint Louis University; TU/e, Technische Universiteit Eindhoven; University of Leeds; IUPUI, Indiana University-Purdue University Indianapolis; UiT, Universitetet i Tromsø; EUR, Erasmus Universiteit Rotterdam; UvT, Universiteit van Tilburg; University of Oxford; UNIL, Université de Lausanne; SEBM, Society for Experimental Biology and Medicine; NYU, New York University; UiT, Universitetet i Tromsø; RWTH Aachen University; KU Leuven; Universität Bielefeld; University of Louisiana at Lafayette; UA, University of Arkansas; Genentech Foundation for Biomedical Sciences; University of Bristol; UV, University of Virginia; City of Mobile; Université de Fribourg; Universiteit Gent; Yale University; VT, Virginia Polytechnic Institute and State University; DIBS, Duke Institute for Brain Sciences, Duke University; UCB UK; University of Leeds; BIBS, Brown Institute for Brain Science; UU, Universiteit Utrecht; Keele University; HRD, Division of Human Resource Development; WVU, West Virginia University; King’s College London; NTU, Nottingham Trent University; Rijksuniversiteit Groningen; KACST, King Abdulaziz City for Science and Technology; University of Edinburgh; UCONN, University of Connecticut; University of Kent; University of Cambridge; UNLV, University of Nevada, Las Vegas; Universiteit Leiden; UBC, University of British Columbia; HGS, Human Genome Sciences; AV CR, Akademie Ved Ceské Republiky; IUPUI, Indiana University-Purdue University Indianapolis; UK, University of Kentucky; University of Texas at Austin; DRS, Dahlem Research School, Freie Universität Berlin; Bangor University; RAND Corporation; University of Glasgow; University of Chester; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas; Massey University; UC, University of California; NIH, National Institutes of Health; UFSC, Universidade Federal de Santa Catarina; OU, Open University; Electroencephalography and Clinical Neuroscience Society; UNAB, Universidad Autónoma de Bucaramanga; KACST, King Abdulaziz City for Science and Technology; SU, Stanford University; CSIR, Council for Scientific and Industrial Research; FSU, Florida State University; UW, University of Wisconsin-Madison; LUMC, Leids Universitair Medisch Centrum; CERSI, Center of Excellence in Regulatory Science and Innovation; DePaul University; Baylor University; SU, Sahlgrenska Universitetssjukhuset; NINDS, National Institute of Neurological Disorders and Stroke

AIM To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma. MATERIALS AND METHODS A retrospective analysis was undertaken of 76 patients (46 tPD, 30 psPD) with early enhancing disease following chemoradiotherapy for glioblastoma. Outcome was determined on follow-up until 6 months post-chemoradiotherapy. Models comprised clinical characteristics, O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and 307 quantitative imaging features extracted from enhancing disease and perilesional oedema masks on early post-chemoradiotherapy contrast-enhanced T1-weighted imaging, T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) maps. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm. Naive Bayes five-fold cross-validation was used to validate the final model. RESULTS Top selected features included age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask, three radiomic features from the enhancing disease mask on ADC, and one radiomic feature from the perilesional oedema mask on T2WI. The final model had an area under the receiver operating characteristics curve (AUC) of 0.80, sensitivity 78.2%, specificity 66.7%, and accuracy of 73.7%. CONCLUSION Incorporating a machine learning-based approach using quantitative radiomic features from standard-of-care magnetic resonance imaging (MRI), in combination with clinical characteristics and MGMT promoter methylation status has a complementary effect and improves model performance for early prediction of glioblastoma treatment response.