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Purpose: Acetylcarnitine can be assessed in vivo using proton MRS (1H‐MRS) with long TEs and this has been previously applied successfully in muscle. The aim of this study was to evaluate a 1H‐MRS technique for liver acetylcarnitine quantification in healthy humans before and after l‐carnitine supplementation. Method: Baseline acetylcarnitine levels were quantified using a STEAM sequence with prolonged TE in 15 healthy adults. Using STEAM with four different TEs was evaluated in phantoms. To assess reproducibility of the measurements, five of the participants had repeated 1H‐MRS without receiving l‐carnitine supplementation. To determine if liver acetylcarnitine could be changed after l‐carnitine supplementation, acetylcarnitine was quantified 2 h after intravenous l‐carnitine supplementation (50 mg/kg body weight) in the other 10 participants. Hepatic lipids were also quantified from the 1H‐MRS spectra. Results: There was good separation between the acetylcarnitine and fat in the phantoms using TE = 100 ms. Hepatic acetylcarnitine levels were reproducible (coefficient of reproducibility = 0.049%) and there was a significant (p < 0.001) increase in the relative abundance after a single supplementation of l‐carnitine. Hepatic allylic, methyl, and methylene peaks were not altered by l‐carnitine supplementation in healthy volunteers. Conclusion: Our results demonstrate that our 1H‐MRS technique could be used to measure acetylcarnitine in the liver and detect changes following intravenous supplementation in healthy adults despite the presence of lipids. Our techniques should be explored further in the study of fatty liver disease, where acetylcarnitine is suggested to be altered due to hepatic inflexibilities. [ABSTRACT FROM AUTHOR]

Background: Myocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan without contrast-associated problems.Methods: Virtual native enhancement (VNE) is a novel technology that can produce virtual LGE-like images without the need for contrast. VNE combines cine imaging and native T1 maps to produce LGE-like images using artificial intelligence. VNE was developed for patients with previous myocardial infarction from 4271 data sets (912 patients); each data set comprises slice position-matched cine, T1 maps, and LGE images. After quality control, 3002 data sets (775 patients) were used for development and 291 data sets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, using 2 adversarial discriminators to improve the image quality. The left ventricle was contoured semiautomatically. Myocardial scar volume was quantified using the full width at half maximum method. Scar transmurality was measured using the centerline chord method and visualized on bull's-eye plots. Lesion quantification by VNE and LGE was compared using linear regression, Pearson correlation (R), and intraclass correlation coefficients. Proof-of-principle histopathologic comparison of VNE in a porcine model of myocardial infarction also was performed.Results: VNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 data sets (all P<0.001). VNE correlated strongly with LGE in quantifying scar size (R, 0.89; intraclass correlation coefficient, 0.94) and transmurality (R, 0.84; intraclass correlation coefficient, 0.90) in 66 patients (277 test data sets). Two cardiovascular magnetic resonance experts reviewed all test image slices and reported an overall accuracy of 84% for VNE in detecting scars when compared with LGE, with specificity of 100% and sensitivity of 77%. VNE also showed excellent visuospatial agreement with histopathology in 2 cases of a porcine model of myocardial infarction.Conclusions: VNE demonstrated high agreement with LGE cardiovascular magnetic resonance for myocardial scar assessment in patients with previous myocardial infarction in visuospatial distribution and lesion quantification with superior image quality. VNE is a potentially transformative artificial intelligence-based technology with promise in reducing scan times and costs, increasing clinical throughput, and improving the accessibility of cardiovascular magnetic resonance in the near future. [ABSTRACT FROM AUTHOR]

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation. However, accurate segmentation of the left ventricular (LV) myocardium remains a challenge due to limited training data and lack of quality control. This study addresses these issues by leveraging generative adversarial networks (GAN)-generated virtual native enhancement (VNE) images to expand the training set and incorporating an automated quality control-driven (QCD) framework to improve segmentation reliability., Methods: A dataset comprising 4,716 LGE images (from 1,363 patients with hypertrophic cardiomyopathy and myocardial infarction) was used for development. To generate additional clinically validated data, LGE data were augmented with a GAN-based generator to produce VNE images. LV was contoured on these images manually by clinical observers. To create diverse candidate segmentations, the QCD framework involved multiple U-Nets, which were combined using statistical rank filters. The framework predicted the Dice Similarity Coefficient (DSC) for each candidate segmentation, with the highest predicted DSC indicating the most accurate and reliable result. The performance of the QCD ensemble framework was evaluated on both LGE and VNE test datasets (309 LGE/VNE images from 103 patients), assessing segmentation accuracy (DSC) and quality prediction (mean absolute error (MAE) and binary classification accuracy)., Results: The QCD framework effectively and rapidly segmented the LV myocardium (<1 s per image) on both LGE and VNE images, demonstrating robust performance on both test datasets with similar mean DSC (LGE: 0.845 ± 0.075 ; VNE: 0.845 ± 0.071 ; p = n s ). Incorporating GAN-generated VNE data into the training process consistently led to enhanced performance for both individual models and the overall framework. The quality control mechanism yielded a high performance ( MAE = 0.043 , accuracy = 0.951 ) emphasising the accuracy of the quality control-driven strategy in predicting segmentation quality in clinical settings. Overall, no statistical difference ( p = n s ) was found when comparing the LGE and VNE test sets across all experiments., Conclusions: The QCD ensemble framework, leveraging GAN-generated VNE data and an automated quality control mechanism, significantly improved the accuracy and reliability of LGE segmentation, paving the way for enhanced and accountable diagnostic imaging in routine clinical use., Competing Interests: VF, QZ, and SP have authorship rights for patent WO2021/044153 (“Enhancement of Medical Images”; granted March 11, 2021). EH, IP, VF, QZ and SP have authorship rights for patent WO/2020/161481 (“Method and Apparatus for Quality Prediction”; granted August 13, 2020). SP has patent authorship rights for US patent US20120078084A1 (“Systems and Methods for Shortened Look Locker Inversion Recovery [Sh-MOLLI] Cardiac Gated Mapping of T1”; granted March 15, 2016). DI was employed by Artificio Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Gonzales, Ibáñez, Hann, Popescu, Burrage, Lee, Altun, Weintraub, Kwong, Kramer, Neubauer, Ferreira, Zhang and Piechnik.)

Purpose: Acetylcarnitine can be assessed in vivo using proton MRS ( 1 H-MRS) with long TEs and this has been previously applied successfully in muscle. The aim of this study was to evaluate a 1 H-MRS technique for liver acetylcarnitine quantification in healthy humans before and after l-carnitine supplementation., Method: Baseline acetylcarnitine levels were quantified using a STEAM sequence with prolonged TE in 15 healthy adults. Using STEAM with four different TEs was evaluated in phantoms. To assess reproducibility of the measurements, five of the participants had repeated 1 H-MRS without receiving l-carnitine supplementation. To determine if liver acetylcarnitine could be changed after l-carnitine supplementation, acetylcarnitine was quantified 2 h after intravenous l-carnitine supplementation (50 mg/kg body weight) in the other 10 participants. Hepatic lipids were also quantified from the 1 H-MRS spectra., Results: There was good separation between the acetylcarnitine and fat in the phantoms using TE = 100 ms. Hepatic acetylcarnitine levels were reproducible (coefficient of reproducibility = 0.049%) and there was a significant (p < 0.001) increase in the relative abundance after a single supplementation of l-carnitine. Hepatic allylic, methyl, and methylene peaks were not altered by l-carnitine supplementation in healthy volunteers., Conclusion: Our results demonstrate that our 1 H-MRS technique could be used to measure acetylcarnitine in the liver and detect changes following intravenous supplementation in healthy adults despite the presence of lipids. Our techniques should be explored further in the study of fatty liver disease, where acetylcarnitine is suggested to be altered due to hepatic inflexibilities., (© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Background: Patients with a history of COVID-19 infection are reported to have cardiac abnormalities on cardiovascular magnetic resonance (CMR) during convalescence. However, it is unclear whether these abnormalities were present during the acute COVID-19 illness and how they may evolve over time., Methods: We prospectively recruited unvaccinated patients hospitalized with acute COVID-19 ( n  = 23), and compared them with matched outpatient controls without COVID-19 ( n  = 19) between May 2020 and May 2021. Only those without a past history of cardiac disease were recruited. We performed in-hospital CMR at a median of 3 days (IQR 1-7 days) after admission, and assessed cardiac function, edema and necrosis/fibrosis, using left and right ventricular ejection fraction (LVEF, RVEF), T1-mapping, T2 signal intensity ratio (T2SI), late gadolinium enhancement (LGE) and extracellular volume (ECV). Acute COVID-19 patients were invited for follow-up CMR and blood tests at 6 months., Results: The two cohorts were well matched in baseline clinical characteristics. Both had normal LVEF (62 ± 7 vs. 65 ± 6%), RVEF (60 ± 6 vs. 58 ± 6%), ECV (31 ± 3 vs. 31 ± 4%), and similar frequency of LGE abnormalities (16 vs. 14%; all p  > 0.05). However, measures of acute myocardial edema (T1 and T2SI) were significantly higher in patients with acute COVID-19 when compared to controls (T1 = 1,217 ± 41 ms vs. 1,183 ± 22 ms; p  = 0.002; T2SI = 1.48 ± 0.36 vs. 1.13 ± 0.09; p  < 0.001). All COVID-19 patients who returned for follow up ( n  = 12) at 6 months had normal biventricular function, T1 and T2SI., Conclusion: Unvaccinated patients hospitalized for acute COVID-19 demonstrated CMR imaging evidence of acute myocardial edema, which normalized at 6 months, while biventricular function and scar burden were similar when compared to controls. Acute COVID-19 appears to induce acute myocardial edema in some patients, which resolves in convalescence, without significant impact on biventricular structure and function in the acute and short-term. Further studies with larger numbers are needed to confirm these findings., Competing Interests: SP has patent authorship rights for U.S. patent 9285446 B2 [systems and methods for Shortened Look Locker Inversion Recovery (Sh-MOLLI) cardiac gated mapping of T1], granted March 15, 2016; IPs are owned and managed by Oxford University Innovations. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Shanmuganathan, Kotronias, Burrage, Ng, Banerjee, Xie, Fletcher, Manley, Borlotti, Emfietzoglou, Mentzer, Marin, Raman, Oxford Acute Myocardial Infarction (OxAMI) investigators, Tunnicliffe, Neubauer, Piechnik, Channon and Ferreira.)