Association of left ventricular flow energetics with remodeling after myocardial infarction: New hemodynamic insights for left ventricular remodeling

BACKGROUND

Myocardial infarction leads to complex changes in left ventricular (LV) hemodynamics. It remains unknown how four-dimensional acute changes in LV-cavity blood flow kinetic energy affects LV-remodeling.

METHODS AND RESULTS

In total, 69 revascularised ST-segment elevation myocardial infarction (STEMI) patients were enrolled. All patients underwent cardiovascular magnetic resonance (CMR) examination within 2 days of the index event and at 3-month. CMR examination included cine, late gadolinium enhancement, and whole-heart four-dimensional flow acquisitions. LV volume-function, infarct size (indexed to body surface area), microvascular obstruction, mitral inflow, and blood flow KEi (kinetic energy indexed to end-diastolic volume) characteristics were obtained. Adverse LV-remodeling was defined and categorized according to increase in LV end-diastolic volume of at least 10%, 15%, and 20%. Twenty-four patients (35%) developed at least 10%, 17 patients (25%) at least 15%, 11 patients (16%) at least 20% LV-remodeling. Demographics and clinical history were comparable between patients with/without LV-remodeling. In univariable regression-analysis, A-wave KEi was associated with at least 10%, 15%, and 20% LV-remodeling (p = 0.03, p = 0.02, p = 0.02, respectively), whereas infarct size only with at least 10% LV-remodeling (p = 0.02). In multivariable regression-analysis, A-wave KEi was identified as an independent marker for at least 10%, 15%, and 20% LV-remodeling (p = 0.09, p < 0.01, p < 0.01, respectively), yet infarct size only for at least 10% LV-remodeling (p = 0.03).

CONCLUSION

In patients with STEMI, LV hemodynamic assessment by LV blood flow kinetic energetics demonstrates a significant inverse association with adverse LV-remodeling. Late-diastolic LV blood flow kinetic energetics early after acute MI was independently associated with adverse LV-remodeling.

Ventricular Arrhythmia Substrate Distribution and Its Relation to Sympathetic Innervation in Nonischemic Cardiomyopathy Patients

BACKGROUND

Nonischemic cardiomyopathy patients referred for catheter ablation of ventricular arrhythmias (VAs) typically have either inferolateral (ILS) or anteroseptal (ASS) VA substrate locations, with poorer outcomes for ASS. Sympathetic denervation is an important determinant of arrhythmogenicity. Its relation to nonischemic fibrosis in general and to the different VA substrates is unknown.

OBJECTIVES

This study sought to evaluate the association between VA substrates, myocardial fibrosis, and sympathetic denervation.

METHODS

Thirty-five patients from the Leiden Nonischemic Cardiomyopathy Study, who underwent electroanatomic voltage mapping and iodine-123 metaiodobenzylguanidine imaging between 2011 and 2018 were included. Late gadolinium-enhanced cardiac magnetic resonance data were collected when available. The relation between global cardiac sympathetic innervation and area-weighted unipolar voltage (UV) as a surrogate for diffuse fibrosis was evaluated. For regional analysis, patients were categorized as ASS or ILS. The distribution of low UV, sympathetic denervation, and late gadolinium enhancement (LGE) scar were compared using the 17-segment model.

RESULTS

Median area-weighted UV was 12.3 mV in patients with normal sympathetic innervation and 8.7 mV in patients with sympathetic denervation. Global sympathetic denervation correlated with diffuse myocardial fibrosis (R = 0.53; P = 0.02). ILS (n = 13) matched with low UV, sympathetic denervation, and LGE scar in all patients, whereas ASS (n = 11) matched with low UV in all patients, with LGE scar in 63% (P = 0.20), but with sympathetic denervation in only 27% of patients (P = 0.0002).

CONCLUSIONS

Global cardiac sympathetic denervation is related to fibrosis in nonischemic cardiomyopathy patients with VA. The mismatch between regional fibrosis and preserved innervation for ASS may contribute to a VA substrate difficult to control by catheter ablation.

Validation of Artificial Intelligence Cardiac MRI Measurements: Relationship to Heart Catheterization and Mortality Prediction

Background Cardiac MRI measurements have diagnostic and prognostic value in the evaluation of cardiopulmonary disease. Artificial intelligence approaches to automate cardiac MRI segmentation are emerging but require clinical testing. Purpose To develop and evaluate a deep learning tool for quantitative evaluation of cardiac MRI functional studies and assess its use for prognosis in patients suspected of having pulmonary hypertension. Materials and Methods A retrospective multicenter and multivendor data set was used to develop a deep learning-based cardiac MRI contouring model using a cohort of patients suspected of having cardiopulmonary disease from multiple pathologic causes. Correlation with same-day right heart catheterization (RHC) and scan-rescan repeatability was assessed in prospectively recruited participants. Prognostic impact was assessed using Cox proportional hazard regression analysis of 3487 patients from the ASPIRE (Assessing the Severity of Pulmonary Hypertension In a Pulmonary Hypertension Referral Centre) registry, including a subset of 920 patients with pulmonary arterial hypertension. The generalizability of the automatic assessment was evaluated in 40 multivendor studies from 32 centers. Results The training data set included 539 patients (mean age, 54 years ± 20 [SD]; 315 women). Automatic cardiac MRI measurements were better correlated with RHC parameters than were manual measurements, including left ventricular stroke volume (r = 0.72 vs 0.68; P = .03). Interstudy repeatability of cardiac MRI measurements was high for all automatic measurements (intraclass correlation coefficient range, 0.79-0.99) and similarly repeatable to manual measurements (all paired t test P > .05). Automated right ventricle and left ventricle cardiac MRI measurements were associated with mortality in patients suspected of having pulmonary hypertension. Conclusion An automatic cardiac MRI measurement approach was developed and tested in a large cohort of patients, including a broad spectrum of right ventricular and left ventricular conditions, with internal and external testing. Fully automatic cardiac MRI assessment correlated strongly with invasive hemodynamics, had prognostic value, were highly repeatable, and showed excellent generalizability. Clinical trial registration no. NCT03841344 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Ambale-Venkatesh and Lima in this issue. An earlier incorrect version appeared online. This article was corrected on June 27, 2022.

Non-contrast free-breathing whole-heart 3D cine cardiovascular magnetic resonance with a novel 3D radial leaf trajectory

PURPOSE

To develop and validate a non-contrast free-breathing whole-heart 3D cine steady-state free precession (SSFP) sequence with a novel 3D radial leaf trajectory.

METHODS

We used a respiratory navigator to trigger acquisition of 3D cine data at end-expiration to minimize respiratory motion in our 3D cine SSFP sequence. We developed a novel 3D radial leaf trajectory to reduce gradient jumps and associated eddy-current artifacts. We then reconstructed the 3D cine images with a resolution of 2.0mm³ using an iterative nonlinear optimization algorithm. Prospective validation was performed by comparing ventricular volumetric measurements from a conventional breath-hold 2D cine ventricular short-axis stack against the non-contrast free-breathing whole-heart 3D cine dataset in each patient (n = 13).

RESULTS

All 3D cine SSFP acquisitions were successful and mean scan time was 07:09 ± 01:31 min. End-diastolic ventricular volumes for left ventricle (LV) and right ventricle (RV) measured from the 3D datasets were smaller than those from 2D (LV: 159.99 ± 42.99 vs. 173.16 ± 47.42; RV: 180.35 ± 46.08 vs. 193.13 ± 49.38; p-value≤0.044; bias<8%), whereas ventricular end-systolic volumes were more comparable (LV: 79.12 ± 26.78 vs. 78.46 ± 25.35; RV: 97.18 ± 32.35 vs. 102.42 ± 32.53; p-value≥0.190, bias<6%). The 3D cine data had a lower subjective image quality score.

CONCLUSION

Our non-contrast free-breathing whole-heart 3D cine sequence with novel leaf trajectory was robust and yielded smaller ventricular end-diastolic volumes compared to 2D cine imaging. It has the potential to make examinations easier and more comfortable for patients.

Longitudinal Changes in Left Ventricular Blood Flow Kinetic Energy After Myocardial Infarction: Predictive Relevance for Cardiac Remodeling

BACKGROUND

Four-dimensional (4D) flow cardiac magnetic resonance (cardiac MR) imaging provides quantification of intracavity left ventricular (LV) flow kinetic energy (KE) parameters in three dimensions. ST-elevation myocardial infarction (STEMI) patients have been shown to have altered intracardiac blood flow compared to controls; however, how 4D flow parameters change over time has not been explored previously.

PURPOSE

Measure longitudinal changes in intraventricular flow post-STEMI and ascertain its predictive relevance of long-term cardiac remodeling.

STUDY TYPE

Prospective.

POPULATION

Thirty-five STEMI patients (M:F = 26:9, aged 56 ± 9 years).

FIELD STRENGTH/SEQUENCE

A 3 T/3D EPI-based, fast field echo (FFE) free-breathing 4D-flow sequence with retrospective cardiac gating.

ASSESSMENT

Serial imaging at 3-7 days (V1), 3-months (V2), and 12-months (V3) post-STEMI, including the following protocol: functional imaging for measuring volumes and 4D-flow for calculating parameters including systolic and peakE-wave LVKE, normalized to end-diastolic volume (iEDV) and stroke volume (iSV). Data were analyzed by H.B. (3 years experience). Patients were categorized into two groups: preserved ejection fraction (pEF, if EF > 50%) and reduced EF (rEF, if EF < 50%).

STATISTICAL TESTS

Independent sample t-tests were used to detect the statistical significance between any two cohorts. P < 0.05 was considered statistically significant.

RESULTS

Across the cohort, systolic KEisv was highest at V1 (28.0 ± 4.4 μJ/mL). Patients with rEF retained significantly higher systolic KEisv than patients with pEF at V2 (18.2 ± 3.4 μJ/mL vs. 6.9 ± 0.6 μJ/mL, P < 0.001) and V3 (21.6 ± 5.1 μJ/mL vs. 7.4 ± 0.9 μJ/mL, P < 0.001). Patients with pEF had significantly higher peakE-wave KEiEDV than rEF patients throughout the study (V1: 25.4 ± 11.6 μJ/mL vs. 18.1 ± 9.9 μJ/mL, P < 0.03, V2: 24.0 ± 10.2 μJ/mL vs. 17.2 ± 12.2 μJ/mL, P < 0.05, V3: 27.7 ± 14.8 μJ/mL vs. 15.8 ± 7.6 μJ/mL, P < 0.04).

DATA CONCLUSION

Systolic KE increased acutely following MI; in patients with pEF, this decreased over 12 months, while patients with rEF, this remained raised. Compared to patients with pEF, persistently lower peakE-wave KE in rEF patients is suggestive of early and fixed impairment in diastolic function.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 3.

Revealing Unforeseen Diagnostic Image Features With Deep Learning by Detecting Cardiovascular Diseases From Apical 4-Chamber Ultrasounds

Background

With the increase of highly portable, wireless, and low‐cost ultrasound devices and automatic ultrasound acquisition techniques, an automated interpretation method requiring only a limited set of views as input could make preliminary cardiovascular disease diagnoses more accessible. In this study, we developed a deep learning method for automated detection of impaired left ventricular (LV) function and aortic valve (AV) regurgitation from apical 4‐chamber ultrasound cineloops and investigated which anatomical structures or temporal frames provided the most relevant information for the deep learning model to enable disease classification.

Methods and Results

Apical 4‐chamber ultrasounds were extracted from 3554 echocardiograms of patients with impaired LV function (n=928), AV regurgitation (n=738), or no significant abnormalities (n=1888). Two convolutional neural networks were trained separately to classify the respective disease cases against normal cases. The overall classification accuracy of the impaired LV function detection model was 86%, and that of the AV regurgitation detection model was 83%. Feature importance analyses demonstrated that the LV myocardium and mitral valve were important for detecting impaired LV function, whereas the tip of the mitral valve anterior leaflet, during opening, was considered important for detecting AV regurgitation.

Conclusions

The proposed method demonstrated the feasibility of a 3‐dimensional convolutional neural network approach in detection of impaired LV function and AV regurgitation using apical 4‐chamber ultrasound cineloops. The current study shows that deep learning methods can exploit large training data to detect diseases in a different way than conventionally agreed on methods, and potentially reveal unforeseen diagnostic image features.

Quality of reporting in AI cardiac MRI segmentation studies - A systematic review and recommendations for future studies

Background

There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation.

Methods

MEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains.

Results

209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59-73%). Median compliance was highest for the model description domain (100%, IQR 80-100%) and lower for the study (71%, IQR 63-86%), dataset (63%, IQR 50-67%) and performance (60%, IQR 50-70%) description domains.

Conclusion

This systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing-most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis-that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence to established frameworks for reporting standards and presents recommendations for improving the quality of reporting in this field.

Systematic Review Registration

[www.crd.york.ac.uk/prospero/], identifier [CRD42022279214].

Left Ventricular Structure, Tissue Composition, and Aortic Distensibility in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Intervention and Complications

Alterations in myocardial structure, function, tissue composition (e.g., fibrosis) may be associated with metabolic syndrome (MetS). This study aimed to determine the relation of MetS and its individual components to markers of cardiovascular disease in patients with type 1 Diabetes Mellitus (T1DM). A total of 978 subjects of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications T1DM cohort (age: 49 ± 7 years, 47% female, DM duration 28 ± 5 years) underwent cardiovascular magnetic resonance. In a subset of 200 patients, myocardial tissue composition was measured with cardiovascular magnetic resonance T1 mapping after contrast administration. MetS was defined as T1DM plus 2 other abnormalities based on the American Heart Association/National Cholesterol Education Program criteria. MetS was present in 34.1% of subjects. After adjustment for age, height, scanner, study cohort, gender, smoking, mean glycated hemoglobin levels, history of macroalbuminuria and end-stage renal disease, left ventricle mass was greater by 12.3 g, end-diastolic volume was higher by 5.4 ml, and mass to end-diastolic volume ratio was higher by 5% in patients with MetS versus those without MetS (p <0.001 for all). Myocardial T1 times were lower by 29 ms in patients with MetS than those without (p <0.001). Elevated waist circumference showed the strongest associations with left ventricle mass (+10.1 g), end-diastolic volume (+6.7 ml), and lower myocardial T1 times (+31 ms) in patients with MetS compared with those without (p <0.01). In conclusion, in a large cohort of patients with T1DM, 34.1% of subjects met MetS criteria. MetS was associated with adverse myocardial structural remodeling and change in myocardial tissue composition.

Mitral regurgitation quantified by CMR 4D-flow is associated with microvascular obstruction post reperfused ST-segment elevation myocardial infarction

Objectives

Mitral regurgitation (MR) and microvascular obstruction (MVO) are common complications of myocardial infarction (MI). This study aimed to investigate the association between MR in ST-elevation MI (STEMI) subjects with MVO post-reperfusion. STEMI subjects undergoing primary percutaneous intervention were enrolled. Cardiovascular magnetic resonance (CMR) imaging was performed within 48-hours of initial presentation. 4D flow images of CMR were analysed using a retrospective valve tracking technique to quantify MR volume, and late gadolinium enhancement images of CMR to assess MVO.

Results

Among 69 patients in the study cohort, 41 had MVO (59%). Patients with MVO had lower left ventricular (LV) ejection fraction (EF) (42 ± 10% vs. 52 ± 8%, P < 0.01), higher end-systolic volume (98 ± 49 ml vs. 73 ± 28 ml, P < 0.001) and larger scar volume (26 ± 19% vs. 11 ± 9%, P < 0.001). Extent of MVO was associated with the degree of MR quantified by 4D flow (R = 0.54, P = 0.0003). In uni-variate regression analysis, investigating the association of CMR variables to the degree of acute MR, only the extent of MVO was associated (coefficient = 0.27, P = 0.001). The area under the curve for the presence of MVO was 0.66 (P = 0.01) for MR > 2.5 ml. We conclude that in patients with reperfused STEMI, the degree of acute MR is associated with the degree of MVO.

3D MRI bal pitvari hegtérkép által vezérelt anatómiai pulmonalis véna reizoláció

Összefoglaló. Egy 58 éves hypertoniás nőbetegünk esetét ismertetjük, aki erős szubjektív panaszokat okozó, gyakori, dokumentált pitvarfibrillációs paroxizmusok miatt korábban két alkalommal pulmonalis véna izoláción esett át, de palpitációérzései továbbra sem szűntek. Feltételezve a pulmonalis véna rekonnekciót, a tartós ritmuszavar-mentesség elérését célozva a tervezett harmadik pulmonalis véna izoláció előtt 3D MRI bal pitvari késői kontraszthalmozásos képalkotást végeztünk. A felvételeken először a vékony bal pitvarfal pontos endocardialis és epicardialis feszínét határoztuk meg manuálisan, majd a fali kontraszthalmozás transmuralitasának megfelelő színkódolást végeztünk. Az így nyert bal pitvari színkódolt felszíni rekonstrukció három dimenzióban jelenítette meg a bal pitvarfalban lévő heges területek elhelyezkedését. A felvételeket a tervezett harmadik beavatkozás során beolvasva az elektroanatómiai rendszerbe, a megjelenített antralis hegfolytonossági hiányok területében végeztünk szelektíven ablatiókat, és teljes izolációt értünk el mind a négy vénában. A szövődménymentes beavatkozás után a beteg tartósan panaszmentessé vált. Esetünk az első olyan hazai ismételt pulmonalis véna izoláció, amelynek során a korábbi ablatiós hegek folytonossági hiányait 3D MRI-hegtérkép alkalmazásával láthatóvá tettük, és az innovatív módszerrel feldolgozott képek irányították az ablatiót, ily módon szüntetve meg a hegfolytonossági hiányokat. Orv Hetil. 2022; 163(19): 767–772.

Summary. We present the case of a 58-year-old woman, suffering from high blood pressure, who presented with documented frequently occurring paroxysmal atrial fibrillation attacks. She underwent two prior pulmonary vein isolations, but her palpitations did not cease. We aimed to achieve a long period free of symptoms, and a 3D MRI late enhancement scar map of the left atrium was obtained before the planned third pulmonary vein isolation procedure to visualize the assumed pulmonary vein reconnection sites. First, the endocardial and epicardial contours of the thin left atrial wall were manually determined on the images, then color-coding was added based on the trasmurality of contrast enhancement in the wall. The reconstructed 3D color-coded left atrial surface revealed the localization of left atrial antral wall scars. These images were integrated into the electroanatomical mapping system and ablation was carried out selectively on the spots showing gaps in the antral scar. Isolation was achieved in all four veins without any complications. The patient has become symptom-free for years now. The reconstructed left atrial 3D MRI images gained in an innovative process visualized the gaps in the previous ablation lines and these images were integrated to guide the first gap-closure redo pulmonary vein isolation procedure in Hungary. Orv Hetil. 2022; 163(19): 767–772.

Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension

Aims

Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning.

Methods and results

Seven hundred and twenty-three consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training, and 207 in the validation cohort. A multilinear principal component analysis (MPCA)-based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. The 1-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and four-chamber MPCA-based predictions was statistically significant (hazard ratios: 2.1, 95% CI: 1.3, 3.4, c-index = 0.70, P = 0.002). The MPCA features improved the 1-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (P ≤ 0.001). Abnormalities in the end-systolic interventricular septum and end-diastolic left ventricle indicated the highest risk of mortality.

Conclusion

The MPCA-based machine learning is an explainable time-resolved approach that allows visualization of prognostic cardiac features throughout the cardiac cycle at the population level, making this approach transparent and clinically interpretable. In addition, the added prognostic value over the REVEAL risk score and CMR volumetric measurements allows for a more accurate prediction of 1-year mortality risk in PAH.

Entropy as a Measure of Myocardial Tissue Heterogeneity in Patients With Ventricular Arrhythmias

OBJECTIVES

The authors investigated the incremental prognostic value of entropy, a novel measure of myocardial tissue heterogeneity by cardiac magnetic resonance (CMR) imaging in patients presenting with ventricular arrhythmias (VAs).

BACKGROUND

CMR can characterize myocardial areas serving as arrhythmogenic substrate.

METHODS

Consecutive patients undergoing CMR imaging for VAs were followed for major adverse cardiac events (MACEs) defined by all-cause death, incident VAs requiring therapy, or heart failure hospitalization. Entropy was derived from the probability distribution of pixel signal intensities of the left ventricular (LV) myocardium.

RESULTS

A total of 583 patients (age 54 ± 15 years, female 39%, left ventricular ejection fraction [LVEF] 54 ± 13%) were followed for a median of 4.4 years and experienced 141 MACEs. Entropy showed strong unadjusted association with MACE (HR: 1.88; 95% CI: 1.63-2.17; P < 0.001). In a multivariable model including LVEF, QRS duration, late gadolinium enhancement, and presenting arrhythmia, entropy maintained independent association with MACE (HR: 1.61; 95% CI: 1.32-1.96; P < 0.001). Entropy was further significantly associated with MACE in patients without myocardial scar (HR: 2.43; 95% CI: 1.55-3.82; P < 0.001) and in those presenting with nonsustained VAs (HR: 2.16; 95% CI: 1.43-3.25; P < 0.001). Addition of LV entropy to the baseline multivariable model significantly improved model performance (C-statistic improvement: 0.725 to 0.754; P = 0.003) and risk reclassification.

CONCLUSIONS

In patients with VAs, CMR-assessed LV entropy was independently associated with MACE and provided incremental prognostic value, on top of LVEF and late gadolinium enhancement. LV entropy assessment may help risk stratification in patients with absence of myocardial scar or with nonsustained VAs.

Left ventricular four-dimensional blood flow distribution, energetics, and vorticity in chronic myocardial infarction patients with/without left ventricular thrombus

BACKGROUND

Left ventricular thrombus (LVT) formation is a frequent and serious complication of myocardial infarction (MI). How global LV flow characteristics are related to this phenomenon is yet uncertain. In this study, we investigated LV flow differences using 4D flow cardiovascular magnetic resonance (CMR) between chronic MI patients with LVT [MI-LVT(+)] and without LVT [MI-LVT(-)], and healthy controls.

METHODS

In this prospective cohort study, the 4D flow CMR data were acquired in 19 chronic MI patients (MI-LVT(+), n = 9 and MI-LVT(-), n = 10) and 9 age-matched controls. All included subjects were in sinus rhythm. The following LV flow parameters were obtained: LV flow components (direct, retained, delayed, residual), mean and peak kinetic energy (KE) values (indexed to instantaneous LV volume), mean and peak vorticity values, and diastolic vortex ring properties (position, orientation, shape).

RESULTS

The MI patients demonstrated a significantly larger amount of delayed and residual flow, and a smaller amount of direct flow compared to controls (p = 0.02, p = 0.03, and p < 0.001, respectively). The MI-LVT(+) patients demonstrated numerically increased residual flow and reduced retained and direct flow in comparison to MI-LVT(-) patients. Systolic mean and peak LV blood flow KE values were significantly lower in MI patients compared to controls (p = 0.04, p = 0.03, respectively). Overall, the mean and peak LV vorticity values were significantly lower in MI patients compared to controls. The mean and peak systolic vorticity at the basal level were significantly higher in MI-LVT(+) than in MI-LVT(-) patients (p < 0.01, for both). The vortex ring core during E-wave in MI-LVT(+) group was located in a less tilted orientation to the LV compared to MI-LVT(-) group (p < 0.01).

CONCLUSIONS

Chronic MI patients with LVT express a different distribution of LV flow components, irregular vorticity vector fields, and altered diastolic vortex ring geometric properties as assessed by 4D flow CMR. Larger prospective studies are warranted to further evaluate the significance of these initial observations.

Mitral regurgitation quantification by cardiac magnetic resonance imaging (MRI) remains reproducible between software solutions

Background: The reproducibility of mitral regurgitation (MR) quantification by cardiovascular magnetic resonance (CMR) imaging using different software solutions remains unclear. This research aimed to investigate the reproducibility of MR quantification between two software solutions: MASS (version 2019 EXP, LUMC, Netherlands) and CAAS (version 5.2, Pie Medical Imaging). Methods: CMR data of 35 patients with MR (12 primary MR, 13 mitral valve repair/replacement, and ten secondary MR) was used. Four methods of MR volume quantification were studied, including two 4D-flow CMR methods (MRMVAV and MRJet) and two non-4D-flow techniques (MRStandard and MRLVRV). We conducted within-software and inter-software correlation and agreement analyses. Results: All methods demonstrated significant correlation between the two software solutions: MRStandard (r=0.92, p<0.001), MRLVRV (r=0.95, p<0.001), MRJet (r=0.86, p<0.001), and MRMVAV (r=0.91, p<0.001). Between CAAS and MASS, MRJet and MRMVAV, compared to each of the four methods, were the only methods not to be associated with significant bias. Conclusions: We conclude that 4D-flow CMR methods demonstrate equivalent reproducibility to non-4D-flow methods but greater levels of agreement between software solutions.

Ventricular flow analysis and its association with exertional capacity in repaired tetralogy of Fallot: 4D flow cardiovascular magnetic resonance study

BACKGROUND

Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) allows quantification of biventricular blood flow by flow components and kinetic energy (KE) analyses. However, it remains unclear whether 4D flow parameters can predict cardiopulmonary exercise testing (CPET) as a clinical outcome in repaired tetralogy of Fallot (rTOF). Current study aimed to (1) compare 4D flow CMR parameters in rTOF with age- and gender-matched healthy controls, (2) investigate associations of 4D flow parameters with functional and volumetric right ventricular (RV) remodelling markers, and CPET outcome.

METHODS

Sixty-three rTOF patients (14 paediatric, 49 adult; 30 ± 15 years; 29 M) and 63 age- and gender-matched healthy controls (14 paediatric, 49 adult; 31 ± 15 years) were prospectively recruited at four centers. All underwent cine and 4D flow CMR, and all adults performed standardized CPET same day or within one week of CMR. RV remodelling index was calculated as the ratio of RV to left ventricular (LV) end-diastolic volumes. Four flow components were analyzed: direct flow, retained inflow, delayed ejection flow and residual volume. Additionally, three phasic KE parameters normalized to end-diastolic volume (KEiEDV), were analyzed for both LV and RV: peak systolic, average systolic and peak E-wave.

RESULTS

In comparisons of rTOF vs. healthy controls, median LV retained inflow (18% vs. 16%, P = 0.005) and median peak E-wave KEiEDV (34.9 µJ/ml vs. 29.2 µJ/ml, P = 0.006) were higher in rTOF; median RV direct flow was lower in rTOF (25% vs. 35%, P < 0.001); median RV delayed ejection flow (21% vs. 17%, P < 0.001) and residual volume (39% vs. 31%, P < 0.001) were both greater in rTOF. RV KEiEDV parameters were all higher in rTOF than healthy controls (all P < 0.001). On multivariate analysis, RV direct flow was an independent predictor of RV function and CPET outcome. RV direct flow and RV peak E-wave KEiEDV were independent predictors of RV remodelling index.

CONCLUSIONS

In this multi-scanner multicenter 4D flow CMR study, reduced RV direct flow was independently associated with RV dysfunction, remodelling and, to a lesser extent, exercise intolerance in rTOF patients. This supports its utility as an imaging parameter for monitoring disease progression and therapeutic response in rTOF. Clinical Trial Registration https://www.clinicaltrials.gov . Unique identifier: NCT03217240.

Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning

Introduction

Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA.

Methods

A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort.

Results

Dice similarity coefficients (DSC) for segmented structures were in the range 0.58-0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785-0.801) and 0.520 (0.482-0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar performance in the external test cohort. The failure rates in mixed pulmonary vascular disease were low (<3.9%) indicating good generalisability of the model to different diseases.

Conclusion

Fully automated segmentation of the four cardiac chambers and great vessels has been achieved in CTPA with high accuracy and low rates of failure. DL volumetric biomarkers can potentially improve CTPA cardiac assessment and invasive haemodynamic prediction.

Evaluation of pulse wave velocity for predicting major adverse cardiovascular events in post-infarcted patients; comparison of oscillometric and MRI methods

Increased aortic pulse wave velocity (PWV) has been proved as a strong predictor of major adverse cardiovascular events (MACE) in patients after myocardial infarction (MI). Due to the various technical approaches the level of high PWV values show significant differences. We evaluated the cut-off PWV values for MACE prediction using cardiac magnetic resonance imaging (CMR) and oscillometric methods for validating the prognostic value of high PWV in post-infarcted patients. Phase contrast imaging (PCI) and oscillometric based Arteriograph (AG) were compared in this 6 years follow-up study, including 75 consecutive patients of whom 49 suffered previous ST-elevation myocardial infarction (STEMI). Patients received follow-up for MACE comprising all-cause death, non-fatal MI, ischemic stroke, hospitalization for heart failure and coronary revascularization. An acceptable agreement and significant correlation (rho: 0.332, p < 0.01) was found between AG and CMR derived PWV values. The absolute values, however, were significantly higher for AG (median (IQR): 10.4 (9.2-11.9) vs 6.44 (5.64-7.5) m/s; p < 0.001). Totally 51 MACE events occurred during the 6 years follow-up period in post-infarcted patients. Kaplan-Meier analysis in both methods showed significantly lower event-free survival in case of high PWV (CMR: >6.47 m/s, AG: >9.625 m/s, p < 0.001, respectively). Multivariate Cox regression revealed PWV as a predictor of MACE (PWV CMR hazard ratio (HR): 1.31 (CI: 1.1-1.7), PWV AG HR: 1.24 (CI: 1.0-1.5), p < 0.05, respectively). Increased PWV derived by AG and CMR methods are feasible for MACE prediction in post-infarcted patients. However, adjusted cut-off values of PWV are recommended for different techniques to improve individual risk stratification.

Left atrial appendage size is a marker of atrial fibrillation recurrence after radiofrequency catheter ablation in patients with persistent atrial fibrillation

Introduction

There are no consistently confirmed predictors of atrial fibrillation (AF) recurrence after catheter ablation. Therefore, we aimed to study whether left atrial appendage volume (LAAV) and function influence the long‐term recurrence of AF after catheter ablation, depending on AF type.

Methods

AF patients who underwent point‐by‐point radiofrequency catheter ablation after cardiac computed tomography (CT) were included in this analysis. LAAV and LAA orifice area were measured by CT. Uni‐ and multivariable Cox proportional hazard regression models were performed to determine the predictors of AF recurrence.

Results

In total, 561 AF patients (61.9 ± 10.2 years, 34.9% females) were included in the study. Recurrence of AF was detected in 40.8% of the cases (34.6% in patients with paroxysmal and 53.5% in those with persistent AF) with a median recurrence‐free time of 22.7 (9.3–43.1) months. Patients with persistent AF had significantly higher body surface area‐indexed LAV, LAAV, and LAA orifice area and lower LAA flow velocity, than those with paroxysmal AF. After adjustment left ventricular ejection fraction (LVEF) <50% (HR = 2.17; 95% CI = 1.38–3.43; p < .001) and LAAV (HR = 1.06; 95% CI = 1.01–1.12; p = .029) were independently associated with AF recurrence in persistent AF, while no independent predictors could be identified in paroxysmal AF.

Conclusion

The current study demonstrates that beyond left ventricular systolic dysfunction, LAA enlargement is associated with higher rate of AF recurrence after catheter ablation in persistent AF, but not in patients with paroxysmal AF.

Mitral regurgitation quantification by cardiac magnetic resonance imaging (MRI) remains reproducible between software solutions

Background: The reproducibility of mitral regurgitation (MR) quantification by cardiovascular magnetic resonance (CMR) imaging using different software solutions remains unclear. This research aimed to investigate the reproducibility of MR quantification between two software solutions: MASS (version 2019 EXP, LUMC, Netherlands) and CAAS (version 5.2, Pie Medical Imaging). Methods: CMR data of 35 patients with MR (12 primary MR, 13 mitral valve repair/replacement, and ten secondary MR) was used. Four methods of MR volume quantification were studied, including two 4D-flow CMR methods (MRMVAV and MRJet) and two non-4D-flow techniques (MRStandard and MRLVRV). We conducted within-software and inter-software correlation and agreement analyses. Results: All methods demonstrated significant correlation between the two software solutions: MRStandard (r=0.92, p<0.001), MRLVRV (r=0.95, p<0.001), MRJet (r=0.86, p<0.001), and MRMVAV (r=0.91, p<0.001). Between CAAS and MASS, MRJet and MRMVAV, compared to each of the four methods, were the only methods not to be associated with significant bias. Conclusions: We conclude that 4D-flow CMR methods demonstrate equivalent reproducibility to non-4D-flow methods but greater levels of agreement between software solutions.

Sex and Cardiovascular Function in Relation to Vascular Brain Injury in Patients with Cognitive Complaints

BACKGROUND

Emerging evidence shows sex differences in manifestations of vascular brain injury in memory clinic patients. We hypothesize that this is explained by sex differences in cardiovascular function.

OBJECTIVE

To assess the relation between sex and manifestations of vascular brain injury in patients with cognitive complaints, in interaction with cardiovascular function.

METHODS

160 outpatient clinic patients (68.8±8.5 years, 38% female) with cognitive complaints and vascular brain injury from the Heart-Brain Connection study underwent a standardized work-up, including heart-brain MRI. We calculated sex differences in vascular brain injury (lacunar infarcts, non-lacunar infarcts, white matter hyperintensities [WMHs], and microbleeds) and cardiovascular function (arterial stiffness, cardiac index, left ventricular [LV] mass index, LV mass-to-volume ratio and cerebral blood flow). In separate regression models, we analyzed the interaction effect between sex and cardiovascular function markers on manifestations of vascular brain injury with interaction terms (sex*cardiovascular function marker).

RESULTS

Males had more infarcts, whereas females tended to have larger WMH-volumes. Males had higher LV mass indexes and LV mass-to-volume ratios and lower CBF values compared to females. Yet, we found no interaction effect between sex and individual cardiovascular function markers in relation to the different manifestations of vascular brain injury (p-values interaction terms > 0.05).

CONCLUSION

Manifestations of vascular brain injury in patients with cognitive complaints differed by sex. There was no interaction between sex and cardiovascular function, warranting further studies to explain the observed sex differences in injury patterns.

Reproducibility of left ventricular blood flow kinetic energy measured by four-dimensional flow CMR

OBJECTIVES

Four-dimensional flow CMR allows for a comprehensive assessment of the blood flow kinetic energy of the ventricles of the heart. In comparison to standard two-dimensional image acquisition, 4D flow CMR is felt to offer superior reproducibility, which is important when repeated examinations may be required. The objective was to evaluate the inter-observer and intra-observer reproducibility of blood flow kinetic energy assessment using 4D flow of the left ventricle in 20 healthy volunteers across two centres in the United Kingdom and the Netherlands.

DATA DESCRIPTION

This dataset contains 4D flow CMR blood flow kinetic energy data for 20 healthy volunteers with no known cardiovascular disease. Presented is kinetic energy data for the entire cardiac cycle (global), the systolic and diastolic components, in addition to blood flow kinetic energy for both early and late diastolic filling. This data is available for reuse and would be valuable in supporting other research, such as allowing for larger sample sizes with more statistical power for further analysis of these variables.

Extracellular volume-guided late gadolinium enhancement analysis for non-ischemic cardiomyopathy: The Women's Interagency HIV Study

BACKGROUND

Quantification of non-ischemic myocardial scar remains a challenge due to the patchy diffuse nature of fibrosis. Extracellular volume (ECV) to guide late gadolinium enhancement (LGE) analysis may achieve a robust scar assessment.

METHODS

Three cohorts of 80 non-ischemic-training, 20 non-ischemic-validation, and 10 ischemic-validation were prospectively enrolled and underwent 3.0 Tesla cardiac MRI. An ECV cutoff to differentiate LGE scar from non-scar was identified in the training cohort from the receiver-operating characteristic curve analysis, by comparing the ECV value against the visually-determined presence/absence of the LGE scar at the highest signal intensity (SI) area of the mid-left ventricle (LV) LGE. Based on the ECV cutoff, an LGE semi-automatic threshold of n-times of standard-deviation (n-SD) above the remote-myocardium SI was optimized in the individual cases ensuring correspondence between LGE and ECV images. The inter-method agreement of scar amount in comparison with manual (for non-ischemic) or full-width half-maximum (FWHM, for ischemic) was assessed. Intra- and inter-observer reproducibility were investigated in a randomly chosen subset of 40 non-ischemic and 10 ischemic cases.

RESULTS

The non-ischemic groups were all female with the HIV positive rate of 73.8% (training) and 80% (validation). The ischemic group was all male with reduced LV function. An ECV cutoff of 31.5% achieved optimum performance (sensitivity: 90%, specificity: 86.7% in training; sensitivity: 100%, specificity: 81.8% in validation dataset). The identified n-SD threshold varied widely (range 3 SD-18 SD), and was independent of scar amount (β = -0.01, p = 0.92). In the non-ischemic cohorts, results suggested that the manual LGE assessment overestimated scar (%) in comparison to ECV-guided analysis [training: 4.5 (3.2-6.4) vs. 0.92 (0.1-2.1); validation: 2.5 (1.2-3.7) vs. 0.2 (0-1.6); P < 0.01 for both]. Intra- and inter-observer analyses of global scar (%) showed higher reproducibility in ECV-guided than manual analysis with CCC = 0.94 and 0.78 versus CCC = 0.86 and 0.73, respectively (P < 0.01 for all). In ischemic validation, the ECV-guided LGE analysis showed a comparable scar amount and reproducibility with the FWHM.

CONCLUSIONS

ECV-guided LGE analysis is a robust scar quantification method for a non-ischemic cohort. Trial registration ClinicalTrials.gov; NCT00000797, retrospectively-registered 2 November 1999; NCT02501811, registered 15 July 2015.