Background: Voltage mapping to detect ventricular scar is important for guiding catheter ablation, but the field-of-view of unipolar, bipolar, conventional, and microelectrodes as it relates to the extent of viable myocardium (VM) is not well defined. Objectives: The purpose of this study was to evaluate electroanatomic voltage-mapping (EAVM) with different-size electrodes for identifying VM, validated against high-resolution ex-vivo cardiac magnetic resonance (HR-LGE-CMR). Methods: A total of 9 swine with early-reperfusion myocardial infarction were mapped with the QDOT microcatheter. HR-LGE-CMR (0.3-mm slices) were merged with EAVM. At each EAVM point, the underlying VM in multisize transmural cylinders and spheres was quantified from ex vivo CMR and related to unipolar and bipolar voltages recorded from conventional and microelectrodes. Results: In each swine, 220 mapping points (Q1, Q3: 216, 260 mapping points) were collected. Infarcts were heterogeneous and nontransmural. Unipolar and bipolar voltage increased with VM volumes from >175 mm³ up to >525 mm³ (equivalent to a 5-mm radius cylinder with height >6.69 mm). VM volumes in subendocardial cylinders with 1- or 3-mm depth correlated poorly with all voltages. Unipolar voltages recorded with conventional and microelectrodes were similar (difference 0.17 ± 2.66 mV) and correlated best to VM within a sphere of radius 10 and 8 mm, respectively. Distance-weighting did not improve the correlation. Conclusions: Voltage increases with transmural volume of VM but correlates poorly with small amounts of VM, which limits EAVM in defining heterogeneous scar. Microelectrodes cannot distinguish thin from thick areas of subendocardial VM. The field-of-view for unipolar recordings for microelectrodes and conventional electrodes appears to be 8 to 10 mm, respectively, and unexpectedly similar.

Objectives: This study sought to investigate the extent of hypertensive exposure as assessed by cardiovascular magnetic resonance imaging (MRI) in relation to cerebral small vessel disease (CSVD) and cognitive impairment, with the aim of understanding the role of hypertension in the early stages of deteriorating brain health. Background: Preserving brain health into advanced age is one of the great challenges of modern medicine. Hypertension is thought to induce vascular brain injury through exposure of the cerebral microcirculation to increased pressure/pulsatility. Cardiovascular MRI provides markers of (subclinical) hypertensive exposure, such as aortic stiffness by pulse wave velocity (PWV), left ventricular (LV) mass index (LVMi), and concentricity by mass-to-volume ratio. Methods: A total of 559 participants from the Heart-Brain Connection Study (431 patients with manifest cardiovascular disease and 128 control participants), age 67.8 ± 8.8 years, underwent 3.0-T heart-brain MRI and extensive neuropsychological testing. Aortic PWV, LVMi, and LV mass-to-volume ratio were evaluated in relation to presence of CSVD and cognitive impairment. Effect modification by patient group was investigated by interaction terms; results are reported pooled or stratified accordingly. Results: Aortic PWV (odds ratio [OR]: 1.17; 95% confidence interval [CI]: 1.05 to 1.30 in patient groups only), LVMi (in carotid occlusive disease, OR: 5.69; 95% CI: 1.63 to 19.87; in other groups, OR: 1.30; 95% CI: 1.05 to 1.62]) and LV mass-to-volume ratio (OR: 1.81; 95% CI: 1.46 to 2.24) were associated with CSVD. Aortic PWV (OR: 1.07; 95% CI: 1.02 to 1.13) and LV mass-to-volume ratio (OR: 1.27; 95% CI: 1.07 to 1.51) were also associated with cognitive impairment. Relations were independent of sociodemographic and cardiac index and mostly persisted after correction for systolic blood pressure or medical history of hypertension. Causal mediation analysis showed significant mediation by presence of CSVD in the relation between hypertensive exposure markers and cognitive impairment. Conclusions: The extent of hypertensive exposure is associated with CSVD and cognitive impairment beyond clinical blood pressure or medical history. The mediating role of CSVD suggests that hypertension may lead to cognitive impairment through the occurrence of CSVD.

We present a workflow for processing real-time cardiac MR (RT-CMR) scans for segmenting the left ventricle (LV) on short-axis slices (SAX). Our method is based on image registration, where the LV endocardium and epicardium are segmented by propagating a reference contour over all the frames of the RT-CMR SAX scans. Our method was evaluated on 19 subjects, the accuracy of the automatic LV endocardium and epicardium segmentation was compared to those defined manually. The proposed method obtained a dice similarity coefficient (DSC) of 0.94 and a mean surface-to-surface distance (MSD) measure of 0.89 ± 0.53 mm. Additionally, a number of automatically obtained clinical measures were compared to ground truth values. On average we obtained a Pearson’s correlation coefficient (R) of 0.94 (0.99–0.74).

Background: Hemodynamic balance in the heart-brain axis is increasingly recognized as a crucial factor in maintaining functional and structural integrity of the brain and thereby cognitive functioning. Patients with heart failure (HF), carotid occlusive disease (COD), and vascular cognitive impairment (VCI) present themselves with complaints attributed to specific parts of the heart-brain axis, but hemodynamic changes often go beyond the part of the axis for which they primarily seek medical advice. The Heart-Brain Study hypothesizes that the hemodynamic status of the heart and the brain is an important but underestimated cause of VCI. We investigate this by studying to what extent hemodynamic changes contribute to VCI and what the mechanisms involved are. Here, we provide an overview of the design and protocol. Methods: The Heart-Brain Study is a multicenter cohort study with a follow-up measurement after 2 years among 645 participants (175 VCI, 175 COD, 175 HF, and 120 controls). Enrollment criteria are the following: 1 of the 3 diseases diagnosed according to current guidelines, age ≥50 years, no magnetic resonance contraindications, ability to undergo cognitive testing, and independence in daily life. A core clinical dataset is collected including sociodemographic factors, cardiovascular risk factors, detailed neurologic, cardiac, and medical history, medication, and a physical examination. In addition, we perform standardized neuropsychological testing, cardiac, vascular and brain MRI, and blood sampling. In subsets of participants we assess Alz­heimer biomarkers in cerebrospinal fluid, and assess echocardiography and 24-hour blood pressure monitoring. Follow-up measurements after 2 years include neuropsychological testing, brain MRI, and blood samples for all participants. We use centralized state-of-the-art storage platforms for clinical and imaging data. Imaging data are processed centrally with automated standardized pipelines. Results and Conclusions: The Heart-Brain Study investigates relationships between (cardio-)vascular factors, the hemodynamic status of the heart and the brain, and cognitive impairment. By studying the complete heart-brain axis in patient groups that represent components of this axis, we have the opportunity to assess a combination of clinical and subclinical manifestations of disorders of the heart, vascular system and brain, with hemodynamic status as a possible binding factor.

With an increasing number of large-scale population-based cardiac magnetic resonance (CMR) imaging studies being conducted nowadays, there comes the mammoth task of image annotation and image analysis. Such population-based studies would greatly benefit from automated pipelines, with an efficient CMR image analysis workflow. The purpose of this work is to investigate the feasibility of using a fully-automatic pipeline to segment the left ventricular endocardium and epicardium simultaneously on two orthogonal (vertical and horizontal) long-axis cardiac cine MRI scans. The pipeline is based on a multi-atlas-based segmentation approach and a spatio-temporal registration approach. The performance of the method was assessed by: (i) comparing the automatic segmentations to those obtained manually at both the end-diastolic and end-systolic phase, (ii) comparing the automatically obtained clinical parameters, including end-diastolic volume, end-systolic volume, stroke volume and ejection fraction, with those defined manually and (iii) by the accuracy of classifying subjects to the appropriate risk category based on the estimated ejection fraction. Automatic segmentation of the left ventricular endocardium was achieved with a Dice similarity coefficient (DSC) of 0.93 on the end-diastolic phase for both the vertical and horizontal long-axis scan; on the end-systolic phase the DSC was 0.88 and 0.85, respectively. For the epicardium, a DSC of 0.94 and 0.95 was obtained on the end-diastolic vertical and horizontal long-axis scans; on the end-systolic phase the DSC was 0.90 and 0.88, respectively. With respect to the clinical volumetric parameters, Pearson correlation coefficient (R) of 0.97 was obtained for the end-diastolic volume, 0.95 for end-systolic volume, 0.87 for stroke volume and 0.84 for ejection fraction. Risk category classification based on ejection fraction showed that 80% of the subjects were assigned to the correct risk category and only one subject (< 1%) was more than one risk category off. We conclude that the proposed automatic pipeline presents a viable and cost-effective alternative for manual annotation.