Field Correction Research Articles

On the study of electrical transport properties of some liquid metals by pseudopotential method

ABSTRACT Our recently evolved pseudopotential is used to study the electrical transport properties such as electrical resistivity ( ρ ), thermal conductivity ( σ ) and thermoelectric power ( TEP ) of some monovalent (Li, Na, K, Rb, Cs), divalent (Mg, Zn, Ca) and polyvalent (Al, Ga, In, Pb, Sn, Bi, Sb) liquid metals. The model potential theory in conjunction with Ziman, mean free path, t-matrix, and Kubo models are implemented in the above-mentioned work for the first time. The Taylor’s local field correction function (T) is taken for observing the exchange and correlation effect with one component plasma (OCP) structure factor method of liquid metals with Hartree dielectric function (H). The presently computed findings are extremely comparable with available data, either theoretical or experimental results that exist in the literature.

Propagation properties and stability of dark solitons in weakly interacting Bose–Bose droplets

We investigate dark solitons in two-component Bose systems with competing interactions in one dimension. Such a system hosts a liquid phase stabilized by the beyond-mean field corrections. Using the generalized Gross–Pitaevskii equation, we reveal the presence of two families of solitonic solutions. The solitons in both of them can be engineered to be arbitrarily wide. One family of solutions, however, has an anomalous dispersion relation, and our analyses show one of its branches is unstable. We find a critical velocity that demarcates the stable from unstable solutions. Nonetheless, gray anomalous solitons can exist inside quantum droplets and can be treated as solitonic excitations thereof.

Ab Initio Path Integral Monte Carlo Simulations of the Uniform Electron Gas on Large Length Scales.

The accurate description of non-ideal quantum many-body systems is of prime importance for a host of applications within physics, quantum chemistry, materials science, and related disciplines. At finite temperatures, the gold standard is given by ab initio path integral Monte Carlo (PIMC) simulations, which do not require any empirical input but exhibit an exponential increase in the required computation time for Fermionic systems with an increase in system size N. Very recently, computing Fermionic properties without this bottleneck based on PIMC simulations of fictitious identical particles has been suggested. In our work, we use this technique to perform very large (N ≤ 1000) PIMC simulations of the warm dense electron gas and demonstrate that it is capable of providing a highly accurate description of the investigated properties, i.e., the static structure factor, the static density response function, and the local field correction, over the entire range of length scales.

Scalarization of the Reissner-Nordström black hole with higher derivative gauge field corrections

Scalarization of the Reissner-Nordström black hole with higher derivative gauge field corrections

Neural Network Corrections to Intermolecular Interaction Terms of a Molecular Force Field Capture Nuclear Quantum Effects in Calculations of Liquid Thermodynamic Properties.

We incorporate nuclear quantum effects (NQE) in condensed matter simulations by introducing short-range neural network (NN) corrections to the ab initio fitted molecular force field ARROW. Force field NN corrections are fitted to average interaction energies and forces of molecular dimers, which are simulated using the Path Integral Molecular Dynamics (PIMD) technique with restrained centroid positions. The NN-corrected force field allows reproduction of the NQE for computed liquid water and methane properties such as density, radial distribution function (RDF), heat of evaporation (HVAP), and solvation free energy. Accounting for NQE through molecular force field corrections circumvents the need for explicit computationally expensive PIMD simulations in accurate calculations of the properties of chemical and biological systems. The accuracy and locality of pairwise NN NQE corrections indicate that this approach could be applicable to complex heterogeneous systems, such as proteins.

Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization.

This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability. Radiomic features, compliant with image biomarker standardization initiative standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wild type). Four approaches were compared: Anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The University of California San Francisco (UCSF)-glioma dataset (n = 409) was used for external validation. Naïve-Bayes algorithms yielded overall the best performance on the internal test set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 (P = .011) for the IDH-wild-type subgroup, but not for the other 2 glioma subgroups (P > .05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wild-type subgroup (P ≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4), and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (P < .012 each). ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wild-type glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data.

A Global Coastal Sea Surface Wind Field Correction Method for HY-2C Scatterometer

A Global Coastal Sea Surface Wind Field Correction Method for HY-2C Scatterometer

Experimental characterization of four ionization chamber types in magnetic fields including intra-type variation

Background and purposeFor dosimetry in magnetic resonance (MR) guided radiotherapy, assessing the magnetic field correction factors of air-vented ionization chambers is crucial. Novel MR-optimized chambers reduce MR-imaging artefacts, enhancing their quality assurance utility. This study aimed to characterize two new MR-optimized ionization chambers with sensitive volumes of 0.07 and 0.016cm3 regarding magnetic field correction factors and intra-type variation and compare them to their conventional counterparts. Material and methodsFive chambers of each type were evaluated in a water phantom, using a clinical linear accelerator and an electromagnet, as well as a 1.5 T MR-linac system. The magnetic field correction factor kB→,Q, addressing the change of response caused by a magnetic field, was assessed together with its intra-type variation. MR-optimized and conventional chambers were compared using a Mann-Whitney U-Test. ResultsConsidering 1.5 T and a perpendicular chamber orientation, we observed significant differences in the magnetic field-induced change in chamber reading between the two 0.016cm3 chamber versions (p = 0.03). For a 7MV beam, MR-optimized chambers (0.016/0.07 cm3) showed kB→,Q values of 1.0426(66) and 1.0463(44), compared to 1.0319(53) and 1.0480(41) of their conventional counterparts. In anti-parallel orientation, kB→,Q was 1.0012(69) and 0.9863(49) for the MR-optimized chambers. The average intra-type variation of kB→,Q over all chamber types was 0.3%. ConclusionMagnetic field correction factors were successfully determined for four ionization chamber types, including two new MR-optimized versions, allowing their use in MR-linac absolute dosimetry. Evaluation of the intra-type variation enabled the assessment of their contribution to the uncertainty of tabulated kB→,Q.

Oil Spill Timely Backtracking Oriented by Wind Field Correction With Self-Attention Temporal Convolutional Networks

Oil Spill Timely Backtracking Oriented by Wind Field Correction With Self-Attention Temporal Convolutional Networks

Сравнительный анализ проекционного и диффузионного методов обеспечения соленоидальности расчетного магнитного поля

Numerical modeling of current-carrying plasma dynamics is based, as a rule, on multiphysics models, which include equations describing MHD waves, transport and and dissipative processes accompanying the exchange of momentum and energy with the electromagnetic field. To solve the equations describing the evolution of the magnetic field, including as a result of magnetic diffusion, the grid system of equations of the discrete MHD model is not always constructed in such a way that the magnetic field divergence constraint is satisfied “automatically”. As a result, numerical errors can accumulate, creating the effect of the appearance of non-physical “magnetic charges” and plasma flows caused by these charges that do not correspond to the true physical situation. To maintain the solenoidal condition, calibration of the calculated magnetic field is used. In this work, in computational experiments with the MHD MARPLE code (M.V. Keldysh Institute of Applied Mathematics RAS), the practical accuracy of calibration methods such as projection and diffusion is assessed. The magnetic field correction was calculated using a stabilized explicit scheme for solving parabolic equations. It has been shown via numerical experiments that the diffusion method is more accurate and is not inferior to the projection method in terms of efficiency.

Brain tumor MRI images classification based on machine learning

Recent research has shown machine learnings outstanding performance on image classifying tasks, including applications on Magnetic Resonance Images. While the former models are overly complicated, this paper proposes a simplified model, which is proven to be both accurate and much less time-consuming. Our proposed method is learned from former research and combines Bias Field Correction, DenseNet, and SE-Net to form a concise structure. With small datasets of T1-weighted and T2-weighted labeled MR brain tumor images, our model spent a short training time of 2 hours and showed excellent performance on classifying pituitary, meningioma, glioma or no tumor with an accuracy of 91.32%. After evaluation, our model is proven to be accurate in distinguishing between 3 of the tumor types with an f1-score of 0.96.

Nonlinear mechanical behavior and stress amplification effect correction of thin plate welded structure considering initial deformation

With the continuous development of light weight of ships, thin plate welded structures are widely used in superstructure of large hull structures and high-speed boats. However, the thin-plate welded structure has a special nonlinear mechanical behavior because of its small stiffness, more complicated welding deformation. The nonlinear mechanical behavior of thin plate welded structure is mainly caused by large slenderness ratio and complex initial buckling deformation, which can be characterized and quantified by defining global deformation angle αG and local deformation angle αL. Because of the welding angle deformation and welding dislocation, there is obvious stress amplification effect at the welding corner, but the traditional stress amplification correction formula is only suitable for thick plate welding structure. Aiming at the nonlinear mechanical behavior and secondary bending moment effect of thin plate welded structure, an analytical model of thin plate welded structure with initial deformation is established by combining theoretical derivation and numerical method. The results show that the additional bending moment effect caused by local welding deformation angle cannot be ignored. Finally, combined with a series of fatigue tests, the additional bending moment caused by bending deformation and angular deformation of typical welded joints is analyzed, and the notch stress field and fatigue strength correction of thin plate welded structures with initial defects are realized.

Field load testing of wind turbines based on the relational model of strain vs load

Compared to theoretical calculations, field blade load testing can more accurately obtain actual load characteristics. The main challenge is the field relationship correction of strain vs load. The initial state of the strain sensing installation can be divided into two scenarios. One scenario is to install before the blade is lifted and another is to install online during the service of wind turbines. The key analysis is conducted on the field correction of the relationship of strain vs load, and corresponding correction strategies are constructed. A decoupled calculation model for strains in the edgewise direction induced by the aerodynamic moment and the gravity moment is proposed. Ground and online experiments for blade strain testing have been carried out. Through the combination of strain testing data and SCADA data, some new insights have been gained on strain characteristics. For example, the mapping coefficient of strain vs load induced by the aerodynamic moment varies under different operating conditions, while the mapping coefficient of strain vs load induced by gravitational moment is relatively stable. When the power of the wind turbine is 552 kW, 853 kW, and 1379 kW, the coefficients induced by the aerodynamic moment are 2270 N m/μɛ, 2480 N m/μɛ, and 3224 N m/μɛ, respectively.

Improved postprocessing of dynamic glucose-enhanced CEST MRI for imaging brain metastases at 3 T

BackgroundDynamic glucose-enhanced (DGE) chemical exchange saturation transfer (CEST) has the potential to characterize glucose metabolism in brain metastases. Since the effect size of DGE CEST is small at 3 T (< 1%), measurements of signal-to-noise ratios are challenging. To improve DGE detection, we developed an acquisition pipeline and extended image analysis for DGE CEST on a hybrid 3-T positron emission tomography/magnetic resonance imaging system.MethodsThis cross-sectional study was conducted after local ethical approval. Static Z-spectra (from -100 to 100 ppm) were acquired to compare the use of 1.2 versus 2 ppm to calculate static glucose-enhanced (glucoCEST) maps in 10 healthy volunteers before and after glucose infusion. Dynamic CEST images were acquired during glucose infusion. Image analysis was optimized using motion correction, dynamic B0 correction, and principal component analysis (PCA) to improve the detection of DGE CEST in the sagittal sinus, cerebrospinal fluid, and grey and white matter. The developed DGE CEST pipeline was applied to four patients diagnosed with brain metastases.ResultsGlucoCEST was strongest in healthy tissues at 2 ppm. Correcting for motion, B0, and use of PCA locally improved DGE maps. A larger contrast between healthy tissues and enhancing regions in brain metastases was found when dynamic B0 correction and PCA denoising were applied.ConclusionWe demonstrated the feasibility of DGE CEST with our developed acquisition and analysis pipeline at 3 T in patients with brain metastases. This work enables a direct comparison of DGE CEST to 18F-fluoro-deoxy-D-glucose positron emission tomography of glucose metabolism in patients with brain metastases.Relevance statementContrast between brain metastasis and healthy brain tissue in DGE CEST MR images is improved by including principle component analysis and dynamic magnetic field correction during postprocessing. This approach enables the detection of increased DGE CEST signal in brain metastasis, if present.Key points• Despite the low signal-to-noise ratio, dynamic glucose-enhanced CEST MRI is feasible at 3 T.• Principal component analyses and dynamic magnetic field correction improve DGE CEST MRI.• DGE CEST MRI does not consequently show changes in brain metastases compared to healthy brain tissue.• Increased DGE CEST MRI in brain metastases, if present, shows overlap with contrast enhancement on T1-weighted images.Graphical

Polarizable AMOEBA Model for Simulating Mg2+·Protein·Nucleotide Complexes.

Molecular mechanics (MM) simulations have the potential to provide detailed insights into the mechanisms of enzymes that utilize nucleotides as cofactors. In most cases, the activities of these enzymes also require the binding of divalent cations to catalytic sites. However, modeling divalent cations in MM simulations has been challenging. The inclusion of explicit polarization was considered promising, but despite improvements over nonpolarizable force fields and despite the inclusion of "Nonbonded-fix (NB-fix)" corrections, errors in interaction energies of divalent cations with proteins remain large. Importantly, the application of these models fails to reproduce the experimental structural data on Mg2+·Protein·ATP complexes. Focusing on these complexes, here we provide a systematic assessment of the polarizable AMOEBA model and recommend critical changes that substantially improve its predictive performance. Our key results are as follows. We first show that our recent revision of the AMOEBA protein model (AMOEBABIO18-HFC), which contains high field corrections (HFCs) to induced dipoles, dramatically improves Mg2+-protein interaction energies, reducing the mean absolute error (MAE) from 17 to 10 kcal/mol. This further supports the general applicability of AMOEBABIO18-HFC. The inclusion of many-body NB-fix corrections further reduces MAE to 6 kcal/mol, which amounts to less than 2% error. The errors are estimated with respect to vdW-inclusive density functional theory that we benchmark against CCSD(T) calculations and experiments. We also present a new model of ATP with revised polarization parameters to better capture its high field response, as well as new vdW and dihedral parameters. The ATP model accurately predicts experimental Mg2+-ATP binding free energy in the aqueous phase and provides new insights into how Mg2+ associates with ATP. Finally, we show that molecular dynamics (MD) simulations of Mg2+·Kinase·ATP complexes carried out with these improvements lead to a better agreement in global and local catalytic site structures between MD and X-ray crystallography.

Multi‐channel segmentation allows simultaneous estimation of classic tissue compartments, WMH and enlarged perivascular spaces

AbstractBackgroundLarge neuroimaging studies like ADNI‐4 now include T1‐weighted, T2‐weighted and FLAIR images at isotropic1mm resolution for investigation of both neurodegenerative and cerebrovascular disease. These three input types allow segmentation of GM, WM, DeepGM, WMH, and CSF compartments. Additionally, this supports automated assessment of enlarged perivascular spaces (PVS). Our objective was to propose a unified framework for segmentation of intracranial classes and PVS while reducing human effort.MethodFLAIR and T2‐weighted images are aligned to the T1‐weighted image.. Standard SPM12 unified segmentation using all three input images establishes deformations between subject and template space and bias field corrections. Extended tissue priors including deep GM are deformed into subject space. Adding mean intensities from high probability deep GM voxels as seeds and using the extended prior set, segmentation is re‐started, refining model parameters, deformations and bias‐field estimates. The procedure repeats, adding a WMH channel, resulting in a segmentation including GM, WM, CSF, deep GM, WMH and nuisance classes. Frangi filters are applied to the T1‐ and T2‐ weighted images. Filter response images are smoothed, averaged, masked and inserted in place of the CSF prior. Probability maps of vessel‐like shapes with CSF‐like contrast are estimated to identify PVS. Visual grading was also performed for comparison.ResultThe method was applied to 300 participants. Visual quality control found two instances of poor WMH over‐segmentation. Perivascular space maps were all visually reasonable. Standard segmentation classes were visibly superior to single T1‐based segmentation. Figures 1,2 present examples. Pearson correlations of PVS fractional volume with multiple human raters’ PVS counts in CSO/CR was over 0.7 in a subset of 125 participants (Figure 3).ConclusionModern dementia protocols include high‐resolution T1‐weighted, T2‐weighted, and FLAIR images that can be used together as inputs to extend probabilistic segmentation adding deep grey and WMH classes with automated PVS assessment.

Multi‐channel segmentation allows simultaneous estimation of classic tissue compartments, WMH and enlarged perivascular spaces

AbstractBackgroundLarge neuroimaging studies like ADNI‐4 now include T1‐weighted, T2‐weighted and FLAIR images at isotropic1mm resolution for investigation of both neurodegenerative and cerebrovascular disease. These three input types allow segmentation of GM, WM, DeepGM, WMH, and CSF compartments. Additionally, this supports automated assessment of enlarged perivascular spaces (PVS). Our objective was to propose a unified framework for segmentation of intracranial classes and PVS while reducing human effort.MethodsFLAIR and T2‐weighted images are aligned to the T1‐weighted image.. Standard SPM12 unified segmentation using all three input images establishes deformations between subject and template space and bias field corrections. Extended tissue priors including deep GM are deformed into subject space. Adding mean intensities from high probability deep GM voxels as seeds and using the extended prior set, segmentation is re‐started, refining model parameters, deformations and bias‐field estimates. The procedure repeats, adding a WMH channel, resulting in a segmentation including GM, WM, CSF, deep GM, WMH and nuisance classes. Frangi filters are applied to the T1‐ and T2‐ weighted images. Filter response images are smoothed, averaged, masked and inserted in place of the CSF prior. Probability maps of vessel‐like shapes with CSF‐like contrast are estimated to identify PVS. Visual grading was also performed for comparison.ResultsThe method was applied to 300 participants. Visual quality control found two instances of poor WMH over‐segmentation. Perivascular space maps were all visually reasonable. Standard segmentation classes were visibly superior to single T1‐based segmentation. Figures 1,2 present examples. Pearson correlations of PVS fractional volume with multiple human raters’ PVS counts in CSO/CR was over 0.7 in a subset of 125 participants (Figure 3).ConclusionModern dementia protocols include high‐resolution T1‐weighted, T2‐weighted, and FLAIR images that can be used together as inputs to extend probabilistic segmentation adding deep grey and WMH classes with automated PVS assessment.

Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort

AbstractBackgroundNeuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aβ)42/40 ratio (Aβ42 concentration to total Aβ concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aβ42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI).MethodFWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aβ42, Aβ40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aβ42/40 threshold of 0.068.ResultFWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p&lt;0.0001), RcGM: R = ‐0.25, p&lt;0.0001). The nonAD group did not show this relationship. FWD did not predict NFL in the AD and nonAD group.ConclusionIn patients with Aβ42/40&lt;0.068, suggestive of AD, FWD in cGM was more strongly related to GFAP than NFL, and predicted cognition, a pattern that was not observed in nonAD patients. Our results suggest distinct patterns of NI in AD compared with nonAD that can be detected with FWD and with a multi‐modal approach could further understanding of differences in pathophysiology across NDs.

Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort

AbstractBackgroundNeuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aß)42/40 ratio (Aß42 concentration to total Aß concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aß42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI).MethodFWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aß42, Aß40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aß42/40 threshold of 0.068.ResultFWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p&lt;0.0001), RcGM: R = ‐0.25, p&lt;0.0001). The nonAD group did not show this relationship. FWD did not predict NFL in the AD and nonAD group.ConclusionIn patients with Aß42/40&lt;0.068, suggestive of AD, FWD in cGM was more strongly related to GFAP than NFL, and predicted cognition, a pattern that was not observed in nonAD patients. Our results suggest distinct patterns of NI in AD compared with nonAD that can be detected with FWD and with a multi‐modal approach could further understanding of differences in pathophysiology across NDs.

Cerebral hypometabolism mediates the effect of stroke volume on cognitive impairment in heart failure patients.

The present study aimed to phenotype the cerebral structural and glucose metabolic alterations in patients with heart failure (HF) using simultaneous positron emission tomography (PET)/magnetic resonance (MR) and to investigate their relationship to cardiac biomarkers and cognitive performance. Forty-two HF patients caused by ischaemic heart disease (mean age 67.2±10.4, 32 males) and 32 age- and sex-matched healthy volunteers (mean age 61.3±4.8, 18 males) were included in this study. Participants underwent simultaneous cerebral fluorine-18 (18 F) fluorodeoxyglucose PET/MR followed by cardiac MR scan, and neuropsychological scores were obtained to assess cognitive performance. The grey matter volume (GMV) and standardized uptake value ratio (SUVR) were calculated to examine cerebral structural and metabolic alterations. Cardiac biomarkers included cardiac MR parameters and cardiac serum laboratory tests. Mediation analysis was performed to explore the associations among cerebral alterations, cardiac biomarkers, and cognitive performance. HF patients demonstrated notable cognitive impairment compared with normal controls (P<0.001). Furthermore, HF patients exhibited regional brain hypometabolism in the bilateral calcarine cortex, caudate nucleus, thalamus, hippocampus, precuneus, posterior cingulate cortex, lingual and olfactory cortex, and GMV reduction in bilateral thalamus and hippocampus (cluster level at P<0.05, Gaussian random field correction). The SUVR of the hypometabolic brain regions was correlated with the Montreal Cognitive Assessment (MoCA) scores (r=0.55, P = 0.038) and cardiac stroke volume (r=0.49, P=0.002). Cerebral hypometabolism played a key role in the relationship between the decreased stroke volume and MoCA scores, with a mediation effect of 33.2%. HF patients suffered cerebral metabolic and structural alterations in regions associated with cognition. The observed correlation between cardiac stroke volume and cognitive impairment underscored the potential influence of cerebral hypometabolism, suggesting that cerebral hypometabolism due to chronic systemic hypoperfusion may significantly contribute to cognitive impairment in HF patients.