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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

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.