Purpose The main objective of this study was to develop two-dimensional (2D) phase contrast (PC) methods to quantify the helicity and vorticity of blood flow in the aortic root.Methods This proof... Show morePurpose The main objective of this study was to develop two-dimensional (2D) phase contrast (PC) methods to quantify the helicity and vorticity of blood flow in the aortic root.Methods This proof-of-concept study used four-dimensional (4D) flow cardiovascular MR (4D flow CMR) data of five healthy controls, five patients with heart failure with preserved ejection fraction and five patients with aortic stenosis (AS). A PC through-plane generated by 4D flow data was treated as a 2D PC plane and compared with the original 4D flow. Visual assessment of flow vectors was used to assess helicity and vorticity. We quantified flow displacement (FD), systolic flow reversal ratio (sFRR) and rotational angle (RA) using 2D PC.Results For visual vortex flow presence near the inner curvature of the ascending aortic root on 4D flow CMR, sFRR demonstrated an area under the curve (AUC) of 0.955, p<0.001. A threshold of >8% for sFRR had a sensitivity of 82% and specificity of 100% for visual vortex presence. In addition, the average late systolic FD, a marker of flow eccentricity, also demonstrated an AUC of 0.909, p<0.001 for visual vortex flow. Manual systolic rotational flow angle change (ΔsRA) demonstrated excellent association with semiautomated ΔsRA (r=0.99, 95% CI 0.9907 to 0.999, p<0.001). In reproducibility testing, average systolic FD (FDsavg) showed a minimal bias at 1.28% with a high intraclass correlation coefficient (ICC=0.92). Similarly, sFRR had a minimal bias of 1.14% with an ICC of 0.96. ΔsRA demonstrated an acceptable bias of 5.72°—and an ICC of 0.99.Conclusion 2D PC flow imaging can possibly quantify blood flow helicity (ΔRA) and vorticity (FRR). These imaging biomarkers of flow helicity and vorticity demonstrate high reproducibility for clinical adoption. Show less
Rooijakkers, M.J.P.; Messaoudi, S. el; Stens, N.A.; Wely, M.H. van; Habets, J.; Brink, M.; ... ; Nijveldt, R. 2024
AimsTo compare the novel 2D multi-velocity encoding (venc) and 4D flow acquisitions with the standard 2D flow acquisition for the assessment of paravalvular regurgitation (PVR) after transcatheter... Show moreAimsTo compare the novel 2D multi-velocity encoding (venc) and 4D flow acquisitions with the standard 2D flow acquisition for the assessment of paravalvular regurgitation (PVR) after transcatheter aortic valve replacement (TAVR) using cardiac magnetic resonance (CMR)-derived regurgitant fraction (RF).Methods and resultsIn this prospective study, patients underwent CMR 1 month after TAVR for the assessment of PVR, for which 2D multi-venc and 4D flow were used, in addition to standard 2D flow. Scatterplots and Bland–Altman plots were used to assess correlation and visualize agreement between techniques. Reproducibility of measurements was assessed with intraclass correlation coefficients. The study included 21 patients (mean age ± SD 80 ± 5 years, 9 men). The mean RF was 11.7 ± 10.0% when standard 2D flow was used, 10.6 ± 7.0% when 2D multi-venc flow was used, and 9.6 ± 7.3% when 4D flow was used. There was a very strong correlation between the RFs assessed with 2D multi-venc and standard 2D flow (r = 0.88, P < 0.001), and a strong correlation between the RFs assessed with 4D flow and standard 2D flow (r = 0.74, P < 0.001). Bland–Altman plots revealed no substantial bias between the RFs (2D multi-venc: 1.3%; 4D flow: 0.3%). Intra-observer and inter-observer reproducibility for 2D multi-venc flow were 0.98 and 0.97, respectively, and 0.92 and 0.90 for 4D flow, respectively.ConclusionTwo-dimensional multi-venc and 4D flow produce an accurate quantification of PVR after TAVR. The fast acquisition of the 2D multi-venc sequence and the free-breathing acquisition with retrospective plane selection of the 4D flow sequence provide useful advantages in clinical practice, especially in the frail TAVR population. Show less
Sun, X.W.; Cheng, L.H.; Plein, S.; Garg, P.; Geest, R.J. van der 2024
Background: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and... Show moreBackground: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segmentation derived from a registered cine MRI acquisition. This requires an additional acquisition and is prone to imperfect spatial and temporal inter-scan alignment. Therefore, in this work we developed and evaluated deep learning-based methods to segment the left ventricle (LV) from 4D flow MRI directly. Methods: We compared five deep learning-based approaches with different network structures, data pre-processing and feature fusion methods. For the data pre-processing, the 4D flow MRI data was reformatted into a stack of short-axis view slices. Two feature fusion approaches were proposed to integrate the features from magnitude and velocity images. The networks were trained and evaluated on an in-house dataset of 101 subjects with 67,567 2D images and 3030 3D volumes. The performance was evaluated using various metrics including Dice, average surface distance (ASD), end-diastolic volume (EDV), end-systolic volume (ESV), LV ejection fraction (LVEF), LV blood flow kinetic energy (KE) and LV flow components. The Monte Carlo dropout method was used to assess the confidence and to describe the uncertainty area in the segmentation results. Results: Among the five models, the model combining 2D U-Net with late fusion method operating on short-axis reformatted 4D flow volumes achieved the best results with Dice of 84.52% and ASD of 3.14 mm. The best averaged absolute and relative error between manual and automated segmentation for EDV, ESV, LVEF and KE was 19.93 ml (10.39%), 17.38 ml (22.22%), 7.37% (13.93%) and 0.07 mJ (5.61%), respectively. Flow component results derived from automated segmentation showed high correlation and small average error compared to results derived from manual segmentation. Conclusions: Deep learning-based methods can achieve accurate automated LV segmentation and subsequent quantification of volumetric and hemodynamic LV parameters from 4D flow MRI without requiring an additional cine MRI acquisition. Show less
Driest, F.Y. van; Broersen, A.; Geest, R.J. van der; Jukema, J.W.; Scholte, A.J.H.A.; Dijkstra, J. 2023
Introduction: The use of serial coronary computed tomography angiography (CCTA) allows for the early assessment of coronary plaque progression, a crucial factor in averting major adverse cardiac... Show moreIntroduction: The use of serial coronary computed tomography angiography (CCTA) allows for the early assessment of coronary plaque progression, a crucial factor in averting major adverse cardiac events (MACEs). Traditionally, serial CCTA is assessed using anatomical landmarks to match baseline and follow-up scans. Recently, a tool has been developed that allows for the automatic quantification of local plaque thickness differences in serial CCTA utilizing plaque contour delineation.The aim of this study was to determine thresholds of plaque thickness differences that define whether there is plaque progression and/or regression. These thresholds depend on the contrast-to-noise ratio (CNR). Methods: Plaque thickness differences between two scans acquired at the same moment in time should always be zero. The negative and positive differences in plaque contour delineation in these scans were used along with the CNR in order to create calibration graphs on which a linear regression analysis was performed. This analysis was conducted on a cohort of 50 patients referred for a CCTA due to chest complaints. A total of 300 coronary vessels were analyzed. First, plaque contours were semi-automatically determined for all major epicardial coronary vessels. Second, manual drawings of seven regions of interest (ROIs) per scan were used to quantify the scan quality based on the CNR for each vessel. Results: A linear regression analysis was performed on the CNR and negative and positive plaque contour delineation differences. Accounting for the standard error of the estimate, the linear regression analysis revealed that above 1.009 - 0.002 9 CNR there is an increase in plaque thickness (progression), and below - 1.638 ? 0.012 9 CNR there is a decrease in plaque thickness (regression). Conclusion: This study demonstrates the feasibility of developing vessel-specific, qualitybased thresholds for visualizing local plaque thickness differences evaluated by serial CCTA. These thresholds have the potential to facilitate the early detection of atherosclerosis progression. Show less
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac... Show moreIn recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms. Show less
BackgroundMRI relaxometry mapping and proton density fat fraction (PDFF) have been proposed for the evaluation of hepatic fibrosis. However, sex-specific relationships of age and body fat with... Show moreBackgroundMRI relaxometry mapping and proton density fat fraction (PDFF) have been proposed for the evaluation of hepatic fibrosis. However, sex-specific relationships of age and body fat with these MRI parameters have not been studied in detail among adults without clinically manifest hepatic disease. We aimed to determine the sex-specific correlation of multiparametric MRI parameters with age and body fat and to evaluate their interplay associations.Methods147 study participants (84 women, mean age 48±14 years, range 19-85 years) were prospectively enrolled. 3 T MRI including T1, T2 and T1ρ mapping and PDFF and R2* map were acquired. Visceral and subcutaneous fat were measured on the fat images from Dixon water-fat separation sequence.ResultsAll MRI parameters demonstrated sex difference except for T1ρ. PDFF was more related to visceral than subcutaneous fat. Per 100 ml gain of visceral or subcutaneous fat is associated with 1 or 0.4% accretion of liver fat, respectively. PDFF and R2* were higher in men (both P = 0.01) while T1 and T2 were higher in women (both P < 0.01). R2* was positively but T1 and T2 were negatively associated with age in women (all P < 0.01), while T1ρ was positively related to age in men (P < 0.05). In all studies, R2* was positively and T1ρ was negatively associated with PDFF (both P <0.0001).ConclusionVisceral fat plays an essential role in the elevated liver fat. When using MRI parametric measures for liver disease evaluation, the interplay between these parameters should be considered. Show less
Objectives:Renal sympathetic denervation (RDN) reduces blood pressure (BP). However, one out of three patients does not exhibit a significant BP response to the therapy. This study investigates the... Show moreObjectives:Renal sympathetic denervation (RDN) reduces blood pressure (BP). However, one out of three patients does not exhibit a significant BP response to the therapy. This study investigates the association between noninvasive vascular stiffness indices and RDN-mediated BP reduction.Methods:In this prospective, single-arm pilot study, patients with systolic office BP at least 140 mmHg, mean 24-h systolic ambulatory blood pressure (ABP) at least 130 mmHg and at least three prescribed antihypertensive drugs underwent radiofrequency RDN. The primary efficacy endpoint was temporal evolution of mean 24-h systolic ABP throughout 1-year post RDN (measured at baseline and 3-6-12 months). Effect modification was studied for baseline ultrasound carotid-femoral and magnetic resonance (MR) pulse wave velocity (PWV), MR aortic distensibility, cardiac MR left ventricular parameters and clinical variables. Statistical analyses were performed using linear mixed-effects models, and effect modification was assessed using interaction terms.Results:Thirty patients (mean age 62.5 +/- 10.7 years, 50% women) with mean 24-h ABP 146.7/80.8 +/- 13.7/12.0 mmHg were enrolled. Following RDN, mean 24-h systolic ABP changed with -8.4 (95% CI: -14.5 to -2.3) mmHg/year (P = 0.007). Independent effect modifiers were CF-PWV [+2.7 (0.3 to 5.1) mmHg/year change in outcome for every m/s increase in CF-PWV; P = 0.03], daytime diastolic ABP [-0.4 (-0.8 to 0.0) mmHg/year per mmHg; P = 0.03], age [+0.6 (0.2 to 1.0) mmHg/year per year of age; P = 0.006], female sex [-14.0 (-23.1 to -5.0) mmHg/year as compared with men; P = 0.003] and BMI [+1.2 (0.1 to 2.2) mmHg/year per kg/m(2); P = 0.04].Conclusion:Higher CF-PWV at baseline was associated with a smaller reduction in systolic ABP following RDN. These findings could contribute to improve identification of RDN responders. Show less
Sun, X.W.; Cheng, L.H.; Plein, S.; Garg, P.; Moghari, M.H.; Geest, R.J. van der 2023
Purpose: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI,... Show morePurpose: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow. Methods: A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. The method was evaluated using velocity vector end-point error (EPE) and angle error. Additionally, the E/A ratio and diastolic function classification derived from the predicted velocities were compared to those derived from 4D flow. Results: For intra-cardiac pixels with a velocity > 5 cm/s, our method achieved an EPE of 8.65 cm/s and angle error of 41.27 degrees. For pixels with a velocity > 25 cm/s, the angle error significantly degraded to 19.26 degrees. Although the averaged blood flow velocity prediction was under-estimated by 26.69%, the high correlation (PCC = 0.95) of global time-varying velocity and the visual evaluation demonstrate a good agreement between our prediction and 4D flow data. The E/A ratio was derived with minimal bias, but with considerable mean absolute error of 0.39 and wide limits of agreement. The diastolic function classification showed a high accuracy of 86.9%. Conclusion: Using a deep learning-based algorithm, intra-cardiac blood flow velocities can be predicted from long-axis cine MRI with high correlation with 4D flow derived velocities. Visualization of the derived velocities provides adjunct functional information and may potentially be used to derive the E/A ratio from conventional CMR exams. Show less
BackgroundMeasurement of peak velocities is important in the evaluation of heart failure. This study compared the performance of automated 4D flow cardiac MRI (CMR) with traditional transthoracic... Show moreBackgroundMeasurement of peak velocities is important in the evaluation of heart failure. This study compared the performance of automated 4D flow cardiac MRI (CMR) with traditional transthoracic Doppler echocardiography (TTE) for the measurement of mitral inflow peak diastolic velocities.MethodsPatients with Doppler echocardiography and 4D flow cardiac magnetic resonance data were included retrospectively. An established automated technique was used to segment the left ventricular transvalvular flow using short-axis cine stack of images. Peak mitral E-wave and peak mitral A-wave velocities were automatically derived using in-plane velocity maps of transvalvular flow. Additionally, we checked the agreement between peak mitral E-wave velocity derived by 4D flow CMR and Doppler echocardiography in patients with sinus rhythm and atrial fibrillation (AF) separately.ResultsForty-eight patients were included (median age 69 years, IQR 63 to 76; 46% female). Data were split into three groups according to heart rhythm. The median peak E-wave mitral inflow velocity by automated 4D flow CMR was comparable with Doppler echocardiography in all patients (0.90 +/- 0.43 m/s vs 0.94 +/- 0.48 m/s, P = 0.132), sinus rhythm-only group (0.88 +/- 0.35 m/s vs 0.86 +/- 0.38 m/s, P = 0.54) and in AF-only group (1.33 +/- 0.56 m/s vs 1.18 +/- 0.47 m/s, P = 0.06). Peak A-wave mitral inflow velocity results had no significant difference between Doppler TTE and automated 4D flow CMR (0.81 +/- 0.44 m/s vs 0.81 +/- 0.53 m/s, P = 0.09) in all patients and sinus rhythm-only groups. Automated 4D flow CMR showed a significant correlation with TTE for measurement of peak E-wave in all patients group (r = 0.73, P < 0.001) and peak A-wave velocities (r = 0.88, P < 0.001). Moreover, there was a significant correlation between automated 4D flow CMR and TTE for peak-E wave velocity in sinus rhythm-only patients (r = 0.68, P < 0.001) and AF-only patients (r = 0.81, P = 0.014). Excellent intra-and inter-observer variability was demonstrated for both parameters.ConclusionAutomated dynamic peak mitral inflow diastolic velocity tracing using 4D flow CMR is comparable to Doppler echocardiography and has excellent repeatability for clinical use. However, 4D flow CMR can potentially underestimate peak velocity in patients with AF. Show less
Venlet, J.; Piers, S.R.; Hoogendoorn, J.; Androulakis, A.F.A.; Riva, M. de; Geest, R.J. van der; Zeppenfeld, K. 2022
Aims In right ventricular cardiomyopathy (RVCM), intramural scar may prevent rapid transmural activation, which may facilitate subepicardial ventricular tachycardia (VT) circuits. A critical... Show moreAims In right ventricular cardiomyopathy (RVCM), intramural scar may prevent rapid transmural activation, which may facilitate subepicardial ventricular tachycardia (VT) circuits. A critical transmural activation delay determined during sinus rhythm (SR) may identify VT substrates in RVCM. Methods and results Consecutive patients with RVCM who underwent detailed endocardial-epicardial mapping and ablation for scar-related VT were enrolled. The transmural activation interval (TAI, first endocardial to first epicardial activation) and maximal activation interval (MAI, first endocardial to last epicardial activation) were determined in endocardial-epicardial point pairs located <10 mm apart. VT-related sites were determined by conventional substrate mapping and limited activation mapping when possible. Nineteen patients (46 +/- 16 years, 84% male, 63% arrhythmogenic right ventricular cardiomyopathy, 37% exercise-induced arrhythmogenic remodelling) were inducible for 44 VT [CL 283 (interquartile range, IQR 240-325)ms]. A total of 2569 endocardial-epicardial coupled point pairs were analysed, including 98 (4%) epicardial VT-related sites. The TAI and MAI were significantly longer at VT-related sites compared with other electroanatomical scar sites [TAI median 31 (IQR 11-50) vs. 2 (-7-11)ms, P < 0.001; MAI median 65 (IQR 45-87) vs. 23 (13-39)ms, P < 0.001]. TAI and MAI allowed highly accurate identification of epicardial VT-related sites (optimal cut-off TAI 17 ms and MAI 45 ms, both AUC 0.81). Both TAI and MAI had a better predictive accuracy for VT-related sites than endocardial and epicardial voltage and electrogram (EGM) duration (AUC 0.51-0.73). Conclusion The transmural activation delay in SR can be used to identify VT substrates in patients with RVCM and predominantly hemodynamically non-tolerated VT, and may be an important new mapping tool for substrate-based ablation. Show less
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... Show morePurpose: 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. Show less
A. das; Kelly, C.; Ben-Arzi, H.; Geest, R.J. van der; Plein, S.; Dall'Armellina, E. 2022
Background: Despite advancements in percutaneous coronary intervention, a significant proportion of ST-elevation myocardial infarction (STEMI) survivors develop long-term adverse left ventricular ... Show moreBackground: Despite advancements in percutaneous coronary intervention, a significant proportion of ST-elevation myocardial infarction (STEMI) survivors develop long-term adverse left ventricular (LV) remodelling, which is associated with poor prognosis. Adverse remodelling is difficult to predict, however four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) can measure various aspects of LV intra-cavity flow beyond LV ejection fraction and is well equipped for exploring the underlying mechanical processes driving remodelling. The aim for this study was to compare acute 4D flow CMR parameters between patients who develop adverse remodelling with patients who do not. Methods: Fifty prospective 'first-event' STEMI patients underwent CMR 5 days post-reperfusion, which included cine-imaging, and 4D flow for assessing in-plane kinetic energy (KE), residual volume, peak-E and peak-A wave KE (indexed for LV end-diastolic volume [LVEDV]). All subjects underwent follow-up cine CMR imaging at 12 months to identify adverse remodelling (defined as 20% increase in LVEDV from baseline). Quantitative variables were compared using unpaired student's t-test. Tests were deemed statistically significant when p < 0.05. Results: Patients who developed adverse LV remodelling by 12 months had significantly higher in-plane KE (54 +/- 12 vs 42 +/- 10%, p = 0.02), decreased proportion of direct flow (27 +/- 9% vs 11 +/- 4%, p < 0.01), increased proportion of delayed ejection flow (22 +/- 9% vs 12 +/- 2, p < 0.01) and increased proportion of residual volume after 2 consecutive cardiac cycles (64 +/- 14 vs 34 +/- 14%, p < 0.01), in their acute scan. Conclusion: Following STEMI, increased in-plane KE, reduced direct flow and increased residual volume in the acute scan were all associated with adverse LV remodelling at 12 months. Our results highlight the clinical utility of acute 4D flow in prognostic stratification in patients following myocardial infarction. Show less
Demirkiran, A.; Geest, R.J. van der; Hopman, L.H.G.A.; Robbers, L.F.H.J.; Handoko, M.L.; Nijveldt, R.; ... ; Garg, P. 2022
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... Show moreBackground: 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), microvas-cular obstruction, mitral inflow, and blood flow KEi (kinetic energy indexed to end-diastolic volume) charac-teristics 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 pa-tients (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 demon-strates a significant inverse association with adverse LV-remodeling. Late-diastolic LV blood flow kinetic ener-getics early after acute MI was independently associated with adverse LV-remodeling. Show less
Dong, X.; Strudwick, M.; Wang, W.Y.S.; Borlaug, B.A.; Geest, R.J. van der; Ng, A.C.C.; ... ; Ng, A.C.T. 2022
Purpose: We hypothesize that both increased myocardial steatosis and interstitial fibrosis contributes to subclinical myocardial dysfunction in patients with increased body mass index and diabetes... Show morePurpose: We hypothesize that both increased myocardial steatosis and interstitial fibrosis contributes to subclinical myocardial dysfunction in patients with increased body mass index and diabetes mellitus. Background: Increased body weight and diabetes mellitus are both individually associated with a higher incidence of heart failure with preserved ejection fraction. However, it is unclear how increased myocardial steatosis and interstitial fibrosis interact to influence myocardial composition and function. Methods: A total of 100 subjects (27 healthy lean volunteers, 21 healthy but overweight volunteers, and 52 asymptomatic overweight patients with diabetes) were prospectively recruited to measure left ventricular (LV) myocardial steatosis (LV-myoFat) and interstitial fibrosis (by extracellular volume [ECV]) using magnetic resonance imaging, and then used to determine their combined impact on LV global longitudinal strain (GLS) analysis by 2-dimensional (2D) speckle tracking echocardiography on the same day. Results: On multivariable analysis, both increased body mass index and diabetes were independently associated with increased LV-myoFat. In turn, increased LV-myoFat was independently associated with increased LV ECV. Both increased LV-myoFat and LV ECV were independently associated with impaired 2D LV GLS. Conclusion: Patients with increased body weight and patients with diabetes display excessive myocardial steatosis, which is related to a greater burden of myocardial interstitial fibrosis. LV myocardial contractile function was determined by both the extent of myocardial steatosis and interstitial fibrosis, and was independent of increasing age. Further study is warranted to determine how weight loss and improved diabetes management can improve myocardial composition and function. Show less
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.... Show moreIntroduction: 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. Show less
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... Show moreBackground 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. Show less
Background: Pulmonary vasodilator therapy in Fontan patients can improve exercise tolerance. We aimed to assess the potential for non-invasive testing of acute vasodilator response using four... Show moreBackground: Pulmonary vasodilator therapy in Fontan patients can improve exercise tolerance. We aimed to assess the potential for non-invasive testing of acute vasodilator response using four-dimensional (D) flow MRI during oxygen inhalation. Materials and Methods: Six patients with well-functioning Fontan circulations were prospectively recruited and underwent cardiac MRI. Ventricular anatomical imaging and 4D Flow MRI were acquired at baseline and during inhalation of oxygen. Data were compared with six age-matched healthy volunteers with 4D Flow MRI scans acquired at baseline. Results: All six patients tolerated the MRI scan well. The dominant ventricle had a left ventricular morphology in all cases. On 4D Flow MRI assessment, two patients (Patients 2 and 6) showed improved cardiac filling with improved preload during oxygen administration, increased mitral inflow, increased maximum E-wave kinetic energy, and decreased systolic peak kinetic energy. Patient 1 showed improved preload only. Patient 5 showed no change, and patient 3 had equivocal results. Patient 4, however, showed a decrease in preload and cardiac filling/function with oxygen. Discussion: Using oxygen as a pulmonary vasodilator to assess increased pulmonary venous return as a marker for positive acute vasodilator response would provide pre-treatment assessment in a more physiological state - the awake patient. This proof-of-concept study showed that it is well tolerated and has shown changes in some stable patients with a Fontan circulation. Show less
Background and Objectives: Interest in artificial intelligence (AI) for outcome prediction has grown substantially in recent years. However, the prognostic role of AI using advanced cardiac... Show moreBackground and Objectives: Interest in artificial intelligence (AI) for outcome prediction has grown substantially in recent years. However, the prognostic role of AI using advanced cardiac magnetic resonance imaging (CMR) remains unclear. This systematic review assesses the existing literature on AI in CMR to predict outcomes in patients with cardiovascular disease. Materials and Methods: Medline and Embase were searched for studies published up to November 2021. Any study assessing outcome prediction using AI in CMR in patients with cardiovascular disease was eligible for inclusion. All studies were assessed for compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: A total of 5 studies were included, with a total of 3679 patients, with 225 deaths and 265 major adverse cardiovascular events. Three methods demonstrated high prognostic accuracy: (1) three-dimensional motion assessment model in pulmonary hypertension (hazard ratio (HR) 2.74, 95%CI 1.73-4.34, p < 0.001), (2) automated perfusion quantification in patients with coronary artery disease (HR 2.14, 95%CI 1.58-2.90, p < 0.001), and (3) automated volumetric, functional, and area assessment in patients with myocardial infarction (HR 0.94, 95%CI 0.92-0.96, p < 0.001). Conclusion: There is emerging evidence of the prognostic role of AI in predicting outcomes for three-dimensional motion assessment in pulmonary hypertension, ischaemia assessment by automated perfusion quantification, and automated functional assessment in myocardial infarction. Show less
