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Left ventricular assist devices (LVADs) are gaining increasing importance as therapeutic strategy in advanced heart failure (HF), not only as bridge to recovery or to transplant but also as destination therapy. Even though long-term LVADs are considered a precious resource to expand the treatment options and improve clinical outcome of these patients, these are limited by peri-operative and post-operative complications, such as device-related infections, haemocompatibility-related events, device mis-positioning, and right ventricular failure. For this reason, a precise pre-operative, peri-operative, and post-operative evaluation of these patients is crucial for the selection of LVAD candidates and the management LVAD recipients. The use of different imaging modalities offers important information to complete the study of patients with LVADs in each phase of their assessment, with peculiar advantages/disadvantages, ideal application, and reference parameters for each modality. This clinical consensus statement sought to guide the use of multimodality imaging for the evaluation of patients with advanced HF undergoing LVAD implantation. [ABSTRACT FROM AUTHOR]

Quantification of myocardial scar from late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) images can be facilitated by automated artificial intelligence (AI)-based analysis. However, AI models are susceptible to domain shifts in which the model performance is degraded when applied to data with different characteristics than the original training data. In this study, CycleGAN models were trained to translate local hospital data to the appearance of a public LGE CMR dataset. After domain adaptation, an AI scar quantification pipeline including myocardium segmentation, scar segmentation, and computation of scar burden, previously developed on the public dataset, was evaluated on an external test set including 44 patients clinically assessed for ischemic scar. The mean ± standard deviation Dice similarity coefficients between the manual and AI-predicted segmentations in all patients were similar to those previously reported: 0.76 ± 0.05 for myocardium and 0.75 ± 0.32 for scar, 0.41 ± 0.12 for scar in scans with pathological findings. Bland-Altman analysis showed a mean bias in scar burden percentage of -0.62% with limits of agreement from -8.4% to 7.17%. These results show the feasibility of deploying AI models, trained with public data, for LGE CMR quantification on local clinical data using unsupervised CycleGAN-based domain adaptation. Relevance statement: Our study demonstrated the possibility of using AI models trained from public databases to be applied to patient data acquired at a specific institution with different acquisition settings, without additional manual labor to obtain further training labels. [ABSTRACT FROM AUTHOR]

Background: Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine. Methods: A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects. Results: The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91−0.98 and 0.89−0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99−1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001). Conclusion: Fully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment. Trials registration: ClinicalTrials.gov: NCT05114785. Relevance statement: Integrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes. Key points: • Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination. • This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution. • LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation. • Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias. [ABSTRACT FROM AUTHOR]

Background: Pulmonary transit time (PTT) can be measured automatically from arterial input function (AIF) images of dual sequence first-pass perfusion imaging. PTT has been validated against invasive cardiac catheterisation correlating with both cardiac output and left ventricular filling pressure (both important prognostic markers in heart failure). We hypothesized that prolonged PTT is associated with clinical outcomes in patients with heart failure. Methods: We recruited outpatients with a recent diagnosis of non-ischaemic heart failure with left ventricular ejection fraction (LVEF) < 50% on referral echocardiogram. Patients were followed up by a review of medical records for major adverse cardiovascular events (MACE) defined as all-cause mortality, heart failure hospitalization, ventricular arrhythmia, stroke or myocardial infarction. PTT was measured automatically from low-resolution AIF dynamic series of both the LV and RV during rest perfusion imaging, and the PTT was measured as the time (in seconds) between the centroid of the left (LV) and right ventricle (RV) indicator dilution curves. Results: Patients (N = 294) were followed-up for median 2.0 years during which 37 patients (12.6%) had at least one MACE event. On univariate Cox regression analysis there was a significant association between PTT and MACE (Hazard ratio (HR) 1.16, 95% confidence interval (CI) 1.08–1.25, P = 0.0001). There was also significant association between PTT and heart failure hospitalisation (HR 1.15, 95% CI 1.02–1.29, P = 0.02) and moderate correlation between PTT and N-terminal pro B-type natriuretic peptide (NT-proBNP, r = 0.51, P < 0.001). PTT remained predictive of MACE after adjustment for clinical and imaging factors but was no longer significant once adjusted for NT-proBNP. Conclusions: PTT measured automatically during CMR perfusion imaging in patients with recent onset non-ischaemic heart failure is predictive of MACE and in particular heart failure hospitalisation. PTT derived in this way may be a non-invasive marker of haemodynamic congestion in heart failure and future studies are required to establish if prolonged PTT identifies those who may warrant closer follow-up or medicine optimisation to reduce the risk of future adverse events. [ABSTRACT FROM AUTHOR]

Purpose: Guidelines recommend measuring myocardial extracellular volume (ECV) using T1‐mapping before and 10–30 min after contrast agent administration. Data are then analyzed using a linear model (LM), which assumes fast water exchange (WX) between the ECV and cardiomyocytes. We investigated whether limited WX influences ECV measurements in patients with severe aortic stenosis (AS). Methods: Twenty‐five patients with severe AS and 5 healthy controls were recruited. T1 measurements were made on a 3 T Siemens system using a multiparametric saturation‐recovery single‐shot acquisition (a) before contrast; (b) 4 min post 0.05 mmol/kg gadobutrol; and (c) 4 min, (d) 10 min, and (e) 30 min after an additional gadobutrol dose (0.1 mmol/kg). Three LM‐based ECV estimates, made using paired T1 measurements (a and b), (a and d), and (a and e), were compared to ECV estimates made using all 5 T1 measurements and a two‐site exchange model (2SXM) accounting for WX. Results: Median (range) ECV estimated using the 2SXM model was 25% (21%–39%) for patients and 26% (22%–29%) for controls. ECV estimated in patients using the LM at 10 min following a cumulative contrast dose of 0.15 mmol/kg was 21% (17%–32%) and increased significantly to 22% (19%–35%) at 30 min (p = 0.0001). ECV estimated using the LM was highest following low dose gadobutrol, 25% (19%–38%). Conclusion: Current guidelines on contrast agent dose for ECV measurements may lead to underestimated ECV in patients with severe AS because of limited WX. Use of a lower contrast agent dose may mitigate this effect. [ABSTRACT FROM AUTHOR]

Background: Determination of myocardial blood flow (MBF) with MRI is usually performed with dynamic contrast enhanced imaging (MBFDCE). MBF can also be determined from coronary sinus blood flow (MBFCS), which has the advantage of being a noncontrast technique. However, comparative studies of MBFDCE and MBFCS in large cohorts are lacking. Purpose: To compare MBFCS and MBFDCE in a large cohort. Study Type: Prospective, sequence‐comparison study. Population: 147 patients with type 2 diabetes mellitus (age: 56+/−12 years; 106 male; diabetes duration: 12.9+/−8.1 years), and 25 age‐matched controls. Field Strength/Sequences: 1.5 Tesla scanner. Saturation recovery sequence for MBFDCE vs. phase‐contrast gradient‐echo pulse sequence (free‐breathing) for MBFCS. Assessment: MBFDCE and MBFCS were determined at rest and during coronary dilatation achieved by administration of adenosine at 140 μg/kg/min. Myocardial perfusion reserve (MPR) was calculated as the stress/rest ratio of MBF values. Coronary sinus flow was determined twice in the same imaging session for repeatability assessment. Statistical Tests: Agreement between MBFDCE and MBFCS was assessed with Bland and Altman's technique. Repeatability was determined from single‐rater random intraclass and repeatability coefficients. Results: Rest and stress flows, including both MBFDCE and MBFCS values, ranged from 33 to 146 mL/min/100 g and 92 to 501 mL/min/100 g, respectively. Intraclass and repeatability coefficients for MBFCS were 0.95 (CI 0.90; 0.95) and 5 mL/min/100 g. In Bland–Altman analysis, mean bias at rest was –1.1 mL/min/100 g (CI −3.1; 0.9) with limits of agreement of −27 and 24.8 mL/min/100 g. Mean bias at stress was 6.3 mL/min/100 g (CI −1.1; 14.1) with limits of agreement of −86.9 and 99.9. Mean bias of MPR was 0.11 (CI: −0.02; 0.23) with limits of agreement of −1.43 and 1.64. Conclusion: MBF may be determined from coronary sinus blood flow, with acceptable bias, but relatively large limits of agreement, against the reference of MBFDCE. Level of Evidence: 1 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Purpose: DTI characterizes tissue microstructure and provides proxy measures of nerve health. Echo‐planar imaging is a popular method of acquiring DTI but is susceptible to various artifacts (e.g., susceptibility, motion, and eddy currents), which may be ameliorated via preprocessing. There are many pipelines available but limited data comparing their performance, which provides the rationale for this study. Methods: DTI was acquired from the upper limb of heathy volunteers at 3T in blip‐up and blip‐down directions. Data were independently corrected using (i) FSL's TOPUP & eddy, (ii) FSL's TOPUP, (iii) DSI Studio, and (iv) TORTOISE. DTI metrics were extracted from the median, radial, and ulnar nerves and compared (between pipelines) using mixed‐effects linear regression. The geometric similarity of corrected b = 0 images and the slice matched T1‐weighted (T1w) images were computed using the Sörenson‐Dice coefficient. Results: Without preprocessing, the similarity coefficient of the blip‐up and blip‐down datasets to the T1w was 0·80 and 0·79, respectively. Preprocessing improved the geometric similarity by 1% with no difference between pipelines. Compared to TOPUP & eddy, DSI Studio and TORTOISE generated 2% and 6% lower estimates of fractional anisotropy, and 6% and 13% higher estimates of radial diffusivity, respectively. Estimates of anisotropy from TOPUP & eddy versus TOPUP were not different but TOPUP reduced radial diffusivity by 3%. The agreement of DTI metrics between pipelines was poor. Conclusions: Preprocessing DTI from the upper limb improves geometric similarity but the choice of the pipeline introduces clinically important variability in diffusion parameter estimates from peripheral nerves. [ABSTRACT FROM AUTHOR]