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IntroductionCardiac magnetic resonance (CMR) is of diagnostic and prognostic value in a range of cardiopulmonary conditions. Current methods for evaluating CMR studies are laborious and time-consuming, contributing to delays for patients. As the demand for CMR increases, there is a growing need to automate this process. The application of artificial intelligence (AI) to CMR is promising, but the evaluation of these tools in clinical practice has been limited. This study assessed the clinical viability of an automatic tool for measuring cardiac volumes on CMR.MethodsConsecutive patients who underwent CMR for any indication between January 2022 and October 2022 at a single tertiary centre were included prospectively. For each case, short-axis CMR images were segmented by the AI tool and manually to yield volume, mass and ejection fraction measurements for both ventricles. Automated and manual measurements were compared for agreement and the quality of the automated contours was assessed visually by cardiac radiologists.Results462 CMR studies were included. No statistically significant difference was demonstrated between any automated and manual measurements (p > 0.05; independent T-test). Intraclass correlation coefficient and Bland-Altman analysis showed excellent agreement across all metrics (ICC > 0.85). The automated contours were evaluated visually in 251 cases, with agreement or minor disagreement in 229 cases (91.2%) and failed segmentation in only a single case (0.4%). The AI tool was able to provide automated contours in under 90 s.ConclusionsAutomated segmentation of both ventricles on CMR by an automatic tool shows excellent agreement with manual segmentation performed by CMR experts in a retrospective real-world clinical cohort. Implementation of the tool could improve the efficiency of CMR reporting and reduce delays between imaging and diagnosis.

Abstract Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 ‘4D Flow CMR Consensus Statement’. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.

Abstract Background Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value. Methods A deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018). Results All deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 ± 3.5 cm2, 91.2 ± 4.5 cm2 and 93.2 ± 3.2 cm2, respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 ± 3.9 cm2, 87.0 ± 5.8 cm2 and 91.8 ± 4.8 cm2. Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p

Abstract Cardiac magnetic resonance (CMR) is emerging as an important tool in the assessment of heart failure with preserved ejection fraction (HFpEF). This study sought to investigate the prognostic value of multiparametric CMR, including left and right heart volumetric assessment, native T1-mapping and LGE in HFpEF. In this retrospective study, we identified patients with HFpEF who have undergone CMR. CMR protocol included: cines, native T1-mapping and late gadolinium enhancement (LGE). The mean follow-up period was 3.2 ± 2.4 years. We identified 86 patients with HFpEF who had CMR. Of the 86 patients (85% hypertensive; 61% males; 14% cardiac amyloidosis), 27 (31%) patients died during the follow up period. From all the CMR metrics, LV mass (area under curve [AUC] 0.66, SE 0.07, 95% CI 0.54–0.76, p = 0.02), LGE fibrosis (AUC 0.59, SE 0.15, 95% CI 0.41–0.75, p = 0.03) and native T1-values (AUC 0.76, SE 0.09, 95% CI 0.58–0.88, p 133.24 g (hazard ratio [HR] 1.58, 95% CI 1.1–2.2, p 34.86% (HR 1.77, 95% CI 1.1–2.8, p = 0.01) and native T1 > 1056.42 ms (HR 2.36, 95% CI 0.9–6.4, p = 0.07). In multivariate cox regression, CMR score model comprising these three variables independently predicted mortality in HFpEF when compared to NTproBNP (HR 4 vs HR 1.65). In non-amyloid HFpEF cases, only native T1 > 1056.42 ms demonstrated higher mortality (AUC 0.833, p

Background: Four-dimensional flow cardiac magnetic resonance (4D flow CMR) is an emerging non-invasive imaging technology that can be used to quantify mitral regurgitation (MR) volume. Current methods of quantification have demonstrated limitations in accurate analysis, particularly in difficult cases such as complex congenital heart disease. 4D flow CMR methods aim to circumvent these limitations and allow accurate quantification of MR volume even in complex cases. This systematic review aims to summarize the available literature on 4D flow CMR MR quantification methods and examine their ability to accurately classify MR severity.Methods: Structured searches were carried out on Medline and EMBASE in December 2018 to identify suitable research outcome studies. The titles and abstracts were screened for relevance, with a third adjudicator utilized when study suitability was uncertain.Results: Seven studies met the eligibility criteria and were included in the systematic review. The most widely used 4D flow MRI method was retrospective valve tracking (RVT) which was examined in five papers. The key finding of these papers was that RVT is a reliable and accurate method of regurgitant volume quantification.Conclusions: MR quantification through 4D flow MRI is both feasible and accurate. The evidence gathered suggests that for MR assessment, 4D flow MRI is potentially as accurate and reliable to echocardiography and may be complementary to this technique. Further work on MR quantification 4D flow image analysis is needed to determine the most accurate analysis technique and to demonstrate 4D flow MRI as a predictor of clinical outcome.PROSPERO Registration Number: CRD42019122837, http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42019122837

Background: There is an emerging body of evidence that supports the potential clinical value of left ventricular (LV) intracavity blood flow kinetic energy (KE) assessment using four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR). The aim of this systematic review is to summarize studies evaluating LV intracavity blood flow KE quantification methods and its potential clinical significance. Methods: A systematic review search was carried out on Medline, Pubmed, EMBASE and CINAHL. Results: Of the 677 articles screened, 16 studies met eligibility. These included six (37%) studies on LV diastolic function, another six (37%) studies on heart failure or cardiomyopathies, three (19%) studies on ischemic heart disease or myocardial infarction and finally, one (6%) study on valvular heart disease, namely, mitral regurgitation. One of the main strengths identified by these studies is high reproducibility of LV blood flow KE hemodynamic assessment (mean coefficient of variability = 6 ± 2%) for the evaluation of LV diastolic function. Conclusions: The evidence gathered in this systematic review suggests that LV blood flow KE has great promise for LV hemodynamic assessment. Studies showed increased diagnostic confidence at no cost of additional time. Results were highly reproducible with low intraobserver variability.