Background Electrocardiographic imaging (ECGI) generates electrophysiological (EP) biomarkers while cardiovascular magnetic resonance (CMR) imaging provides data about myocardial structure, function and tissue substrate. Combining this information in one examination is desirable but requires an affordable, reusable, and high-throughput solution. We therefore developed the CMR-ECGI vest and carried out this technical development study to assess its feasibility and repeatability in vivo. Methods CMR was prospectively performed at 3T on participants after collecting surface potentials using the locally designed and fabricated 256-lead ECGI vest. Epicardial maps were reconstructed to generate local EP parameters such as activation time (AT), repolarization time (RT) and activation recovery intervals (ARI). 20 intra- and inter-observer and 8 scan re-scan repeatability tests. Results 77 participants were recruited: 27 young healthy volunteers (HV, 38.9 ± 8.5 years, 35% male) and 50 older per

Background The 2019 arrhythmogenic right ventricular cardiomyopathy (ARVC) risk model has proved insufficient in the capability of predicting ventricular arrhythmia (VA) risk in non-classical arrhythmogenic cardiomyopathy (ACM). Furthermore, the prognostic value of ringlike late gadolinium enhancement (LGE) of the left ventricle in non-classical ACM remains unknown. We aimed to assess the incremental value of ringlike LGE over the 2019 ARVC risk model in predicting sustained VA in patients with non-classical ACM. Methods In this retrospective study, consecutive patients with non-classical ACM who underwent CMR from January 2011 to January 2022 were included. The pattern of LGE was categorized as no, non-ringlike, and ringlike LGE. The primary outcome was defined as the occurrence of sustained VA. Univariable and multivariable Cox regression analysis was used to evaluate the impact of LGE patterns on sustained VA and area under curve (AUC) was calculated for the incremental value of rin

Background Pulmonary capillary wedge pressure (PCWP) assessment is fundamental for managing dilated cardiomyopathy (DCM) patients. Although cardiovascular magnetic resonance (CMR) has become the gold-standard imaging technique for evaluating cardiac chamber volume and function, PCWP is not routinely assessed with CMR. Therefore, this study aimed to validate the left atrial expansion index (LAEI), a LA reservoir function parameter able to estimate filling pressure with echocardiography, as a novel CMR-measured parameter for non-invasive PCWP estimation in DCM patients. Methods We performed a retrospective, single-center, cross-sectional study. We included electively admitted DCM patients referred to our tertiary center for further diagnostic evaluation that underwent a clinically indicated right heart catheterization (RHC) and CMR within 24 h. PCWP invasively measured during RHC was used as the reference. LAEI was calculated from CMR-measured LA maximal and minimal volumes as LAEI =  (

Purpose This study aimed to evaluate the early morphology and function of the left heart in hypertrophic obstructive cardiomyopathy (HOCM) after transapical beating-heart septal myectomy (TA-BSM) using cardiovascular magnetic resonance (CMR). Materials and methods Between April 2022 and January 2023, HOCM patients who underwent CMR before and 3 months after TA-BSM were prospectively and consecutively enrolled in the study. Preoperative and postoperative cardiac morphological and functional parameters, including those for the left atrium (LA) and left ventricle (LV), were compared. The left ventricular remodeling index (LVRI) was defined as the ratio between left ventricular mass (LVM) and left ventricular end-diastolic volume (LVEDV). Healthy participants with a similar age and sex distribution were enrolled for comparison. Pearson or Spearman correlation analysis was used to investigate the relationships between the parameters and LVRI. Last, univariate and multivariate linear regress

Introduction Research utilising artificial intelligence (AI) and cardiovascular magnetic resonance (CMR) is rapidly evolving with various objectives, however AI model development, generalisation and performance may be hindered by availability of robust training datasets including contrast enhanced images. Methods NotIs CMR is a large UK, prospective, multicentre, observational cohort study to guide the development of a biventricular AI scar model. Patients with ischaemic heart disease undergoing clinically indicated contrast-enhanced cardiac magnetic resonance imaging will be recruited at Nottingham University Hospitals NHS Trust and Mid-Yorkshire Hospital NHS Trust. Baseline assessment will include cardiac magnetic resonance imaging, demographic data, medical history, electrocardiographic and serum biomarkers. Participants will undergo monitoring for a minimum of 5 years to document any major cardiovascular adverse events. The main objectives include (1) AI training, validation and te

Background The clinical application of coronary MR angiography (MRA) combining diastole and systole imaging has never been described comprehensively in coronary artery disease (CAD) patients. We aimed to design an optimal non-contrast coronary MRA scan protocol combining diastolic and systolic imaging and to (1) evaluate its diagnostic performance for detecting significant coronary stenosis; (2) evaluate the feasibility of this protocol to noninvasively measure the coronary distensibility index (CDI). Methods From June 2021 to May 2022, 33 healthy volunteers and 91 suspected CAD patients scheduled for X-ray coronary angiography (CAG) were prospectively enrolled. 3T non-contrast water-fat coronary MRA was carried out twice at diastole and systole. Significant coronary stenosis was defined as a luminal diameter reduction of ≥ 50% using CAG as the reference and was evaluated as follows: (1) by coronary MRA in diastole alone; (2) by coronary MRA in systole alone; (3) by combined coronary M

Purpose To develop a partially interpretable neural network for joint suppression of banding and flow artifacts in non-phase-cycled bSSFP cine imaging. Methods A dual-stage neural network consisting of a voxel-identification (VI) sub-network and artifact-suppression (AS) sub-network is proposed. The VI sub-network provides identification of artifacts, which guides artifact suppression and improves interpretability. The AS sub-network reduces banding and flow artifacts. Short-axis cine images of 12 frequency offsets from 28 healthy subjects were used to train and test the dual-stage network. An additional 77 patients were retrospectively enrolled to evaluate its clinical generalizability. For healthy subjects, artifact suppression performance was analyzed by comparison with traditional phase cycling. The partial interpretability provided by the VI sub-network was analyzed via correlation analysis. Generalizability was evaluated for cine obtained with different sequence parameters and sc