Native myocardial T1 and right ventricular size by CMR predict outcome in systemic sclerosis-associated pulmonary hypertension.

OBJECTIVES: Measures of right heart size and function are prognostic in systemic sclerosis-associated pulmonary hypertension (SSc-PH), but the importance of myocardial tissue characterisation remains unclear. We aimed to investigate the predictive potential and interaction of cardiovascular magnetic resonance (CMR) myocardial tissue characterisation and right heart size and function in SSc-PH. METHODS: A retrospective, single-centre, observational study of 148 SSc-PH patients confirmed by right heart catheterization who underwent clinically-indicated CMR including native myocardial T1 and T2 mapping from 2016 to 2023 was performed. RESULTS: Sixty-six (45%) patients died during follow-up (median 3.5 years, range 0.1-7.3). Patients who died were older (65 vs 60 years, p= 0.035) with more dilated (RVEDVi and RVESVi, p< 0.001), hypertrophied (RVMi, p= 0.013) and impaired (RVEF, p< 0.001) right ventricles, more dilated right atria (RAi, p= 0.043) and higher native myocardial T1 (p< 0.001).After adjustment for age, RVESVi (p = 0.0023) and native T1 (p = 0.0024) were independent predictors of all-cause mortality. Both RVESVi and native T1 remained independently predictive after adjusting for age and PH subtype (RVESVi p < 0.001, T1 p = 0.0056). Optimal prognostic thresholds for RVESVi and native T1 were ≤38 mL/m2 and ≤1119 ms, respectively (p < 0.001). Patients with RVESVi ≤ 38 mL/m2 and native T1 ≤ 1119 ms had significantly better outcomes than all other combinations (p < 0.001). Furthermore, patients with RVESVi > 38mL/m2 and native T1 ≤ 1119 ms had significantly better survival than patients with RVESVi > 38mL/m2 and native T1 > 1119ms (p = 0.017). CONCLUSION: We identified prognostically relevant CMR metrics and thresholds for patients with SSc-PH. Assessing myocardial tissue characterisation alongside RV function confers added value in SSc-PH and may represent an additional treatment target.

Training deep learning based dynamic MR image reconstruction using open-source natural videos.

To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian, radial, spiral) either from true multi-coil cardiac MR data (N = 692) or from synthetic MR data simulated from Inter4K natural videos (N = 588). Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS). Differences were assessed in simulations (N = 104 datasets) in terms of MSE, PSNR, and SSIM and prospectively for cardiac cine (short axis, four chambers, N = 20) and speech cine (N = 10) data in terms of subjective image quality ranking, SNR and Edge sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were performed to assess statistical significance. In simulated data, DL networks trained with cardiac data outperformed DL networks trained with natural videos, both of which outperformed CS (p < 0.05). However, in prospective experiments DL reconstructions using both training datasets were ranked similarly (and higher than CS) and presented no statistical differences in SNR and Edge Sharpness for most conditions.The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). The natural video dataset, code and pre-trained networks are made readily available on github.

Rapid 2D 23Na MRI of the calf using a denoising convolutional neural network.

PURPOSE: 23Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the inherently low 23Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction algorithms such as compressed sensing (CS) have been proposed to mitigate low signal-to-noise ratio (SNR); although, these can result in unnatural images, suboptimal denoising and long processing times. Recently, machine learning has been increasingly used to denoise 1H MRI acquisitions; however, this approach typically requires large volumes of high-quality training data, which is not readily available for 23Na MRI. Here, we propose using 1H data to train a denoising convolutional neural network (CNN), which we subsequently demonstrate on prospective 23Na images of the calf. METHODS: 1893 1H fat-saturated transverse slices of the knee from the open-source fastMRI dataset were used to train denoising CNNs for different levels of noise. Synthetic low SNR images were generated by adding gaussian noise to the high-quality 1H k-space data before reconstruction to create paired training data. For prospective testing, 23Na images of the calf were acquired in 10 healthy volunteers with a total of 150 averages over ten minutes, which were used as a reference throughout the study. From this data, images with fewer averages were retrospectively reconstructed using a non-uniform fast Fourier transform (NUFFT) as well as CS, with the NUFFT images subsequently denoised using the trained CNN. RESULTS: CNNs were successfully applied to 23Na images reconstructed with 50, 40 and 30 averages. Muscle and skin apparent TSC quantification from CNN-denoised images were equivalent to those from CS images, with <0.9 mM bias compared to reference values. Estimated SNR was significantly higher in CNN-denoised images compared to NUFFT, CS and reference images. Quantitative edge sharpness was equivalent for all images. For subjective image quality ranking, CNN-denoised images ranked equally best with reference images and significantly better than NUFFT and CS images. CONCLUSION: Denoising CNNs trained on 1H data can be successfully applied to 23Na images of the calf; thus, allowing scan time to be reduced from ten minutes to two minutes with little impact on image quality or apparent TSC quantification accuracy.

Design and implementation of multicenter pediatric and congenital studies with cardiovascular magnetic resonance: Big data in smaller bodies.

Cardiovascular magnetic resonance (CMR) has become the reference standard for quantitative and qualitative assessment of ventricular function, blood flow, and myocardial tissue characterization. There is a preponderance of large CMR studies and registries in adults; However, similarly powered studies are lacking for the pediatric and congenital heart disease (PCHD) population. To date, most CMR studies in children are limited to small single or multicenter studies, thereby limiting the conclusions that can be drawn. Within the PCHD CMR community, a collaborative effort has been successfully employed to recognize knowledge gaps with the aim to embolden the development and initiation of high-quality, large-scale multicenter research. In this publication, we highlight the underlying challenges and provide a practical guide toward the development of larger, multicenter initiatives focusing on PCHD populations, which can serve as a model for future multicenter efforts.

Cardiopulmonary Exercise Testing in Evaluating Transthyretin Amyloidosis.

Importance: Cardiopulmonary exercise testing (CPET) has an established role in the assessment of patients with heart failure. However, data are lacking in patients with transthyretin (ATTR) amyloidosis. Objective: To use CPET to characterize the spectrum of functional phenotypes in patients with ATTR amyloidosis and assess their association with the cardiac amyloid burden as well as the association between CPET parameters and prognosis. Design, Setting and Participants: This single-center study evaluated patients diagnosed with ATTR amyloidosis from May 2019 to September 2022 who underwent CPET at the National Amyloidosis Centre. Of 1045 patients approached, 506 were included and completed the study. Patients were excluded if they had an absolute contraindication to CPET or declined participation. The mean (SD) follow-up period was 22.4 (11.6) months. Main Outcomes and Measures: Comparison of CPET parameters across disease phenotypes (ATTR with cardiomyopathy [ATTR-CM], polyneuropathy, or both [ATTR-mixed]), differences in CPET parameters based on degree of amyloid infiltration (as measured by cardiovascular magnetic resonance [CMR] with extracellular volume mapping), and association between CPET parameters and prognosis. Results: Among the 506 patients with ATTR amyloidosis included in this study, the mean (SD) age was 73.5 (10.2) years, and 457 participants (90.3%) were male. Impairment in functional capacity was highly prevalent. Functional impairment in ATTR-CM and ATTR-mixed phenotypes (peak mean [SD] oxygen consumption [VO2], 14.5 [4.3] mL/kg/min and 15.7 [6.2] mL/kg/min, respectively) was observed alongside impairment in the oxygen pulse, with ventilatory efficiency highest in ATTR-CM (mean [SD] ventilatory efficiency/volume of carbon dioxide expired slope, 38.1 [8.6]). Chronotropic incompetence and exercise oscillatory ventilation (EOV) were highly prevalent across all phenotypes, with both the prevalence and severity being higher than in heart failure from different etiologies. Worsening of amyloid burden on CMR was associated with decline in multiple CPET parameters, although chronotropic response and EOV remained abnormal irrespective of amyloid burden. On multivariable Cox regression analysis, peak VO2 and peak systolic blood pressure (SBP) were independently associated with prognosis (peak VO2: hazard ratio, 0.89 [95% CI, 0.81-0.99; P = .03]; peak SBP: hazard ratio, 0.98 [95% CI, 0.97-0.99; P < .001]). Conclusions and Relevance: In this study, ATTR amyloidosis was characterized by distinct patterns of functional impairment between all disease phenotypes. A high prevalence of chronotropic incompetence, EOV, and ventilatory inefficiency were characteristic of this population. CPET parameters were associated with amyloid burden by CMR and with peak VO2, and SBP, which have been shown to be independent predictors of mortality. These findings suggest that CPET may be useful in characterizing distinct patterns of functional impairment across the spectrum of amyloid infiltration and predicting outcomes, and potentially offers a more comprehensive method of evaluating functional capacity for future prospective studies.

Predicting Peri-Operative Cardiorespiratory Adverse Events in Children with Idiopathic Pulmonary Arterial Hypertension Undergoing Cardiac Catheterization Using Echocardiography: A Cohort Study.

General anesthesia in children with idiopathic pulmonary arterial hypertension (PAH) carries an increased risk of peri-operative cardiorespiratory complications though risk stratifying individual children pre-operatively remains difficult. We report the incidence and echocardiographic risk factors for adverse events in children with PAH undergoing general anesthesia for cardiac catheterization. Echocardiographic, hemodynamic, and adverse event data from consecutive PAH patients are reported. A multivariable predictive model was developed from echocardiographic variables identified by Bayesian univariable logistic regression. Model performance was reported by area under the curve for receiver operating characteristics (AUCroc) and precision/recall (AUCpr) and a pre-operative scoring system derived (0-100). Ninety-three children underwent 158 cardiac catheterizations with mean age 8.8 ± 4.6 years. Adverse events (n = 42) occurred in 15 patients (16%) during 16 catheterizations (10%) including cardiopulmonary resuscitation (n = 5, 3%), electrocardiographic changes (n = 3, 2%), significant hypotension (n = 2, 1%), stridor (n = 1, 1%), and death (n = 2, 1%). A multivariable model (age, right ventricular dysfunction, and dilatation, pulmonary and tricuspid regurgitation severity, and maximal velocity) was highly predictive of adverse events (AUCroc 0.86, 95% CI 0.75 to 1.00; AUCpr 0.68, 95% CI 0.50 to 0.91; baseline AUCpr 0.10). Pre-operative risk scores were higher in those who had a subsequent adverse event (median 47, IQR 43 to 53) than in those who did not (median 23, IQR 15 to 33). Pre-operative echocardiography informs the risk of peri-operative adverse events and may therefore be useful both for consent and multi-disciplinary care planning.

A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology.

Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of patients with Fontan circulation (Fontan Outcomes Registry Using CMR Examinations [FORCE]). Materials and Methods This retrospective study used 250 cardiac MRI examinations (November 2007-December 2022) from 13 institutions for training, validation, and testing. The pipeline contained three DL models: a classifier to identify short-axis cine stacks and two U-Net 3+ models for image cropping and segmentation. The automated segmentations were evaluated on the test set (n = 50) by using the Dice score. Volumetric and functional metrics derived from DL and ground truth manual segmentations were compared using Bland-Altman and intraclass correlation analysis. The pipeline was further qualitatively evaluated on 475 unseen examinations. Results There were acceptable limits of agreement (LOA) and minimal biases between the ground truth and DL end-diastolic volume (EDV) (bias: -0.6 mL/m2, LOA: -20.6 to 19.5 mL/m2) and end-systolic volume (ESV) (bias: -1.1 mL/m2, LOA: -18.1 to 15.9 mL/m2), with high intraclass correlation coefficients (ICCs > 0.97) and Dice scores (EDV, 0.91 and ESV, 0.86). There was moderate agreement for ventricular mass (bias: -1.9 g/m2, LOA: -17.3 to 13.5 g/m2) and an ICC of 0.94. There was also acceptable agreement for stroke volume (bias: 0.6 mL/m2, LOA: -17.2 to 18.3 mL/m2) and ejection fraction (bias: 0.6%, LOA: -12.2% to 13.4%), with high ICCs (>0.81). The pipeline achieved satisfactory segmentation in 68% of the 475 unseen examinations, while 26% needed minor adjustments, 5% needed major adjustments, and in 0.4%, the cropping model failed. Conclusion The DL pipeline can provide fast standardized segmentation for patients with single ventricle physiology across multiple centers. This pipeline can be applied to all cardiac MRI examinations in the FORCE registry. Keywords: Cardiac, Adults and Pediatrics, MR Imaging, Congenital, Volume Analysis, Segmentation, Quantification Supplemental material is available for this article. © RSNA, 2023.

Rapid desensitization through immunoadsorption during cardiopulmonary bypass. A novel method to facilitate human leukocyte antigen incompatible heart transplantation.

BACKGROUND: Anti-human leukocyte antigen (HLA)-antibody production represents a major barrier to heart transplantation, limiting recipient compatibility with potential donors and increasing the risk of complications with poor waiting-list outcomes. Currently there is no consensus to when desensitization should take place, and through what mechanism, meaning that sensitized patients must wait for a compatible donor for many months, if not years. We aimed to determine if intraoperative immunoadsorption could provide a potential desensitization methodology. METHODS: Anti-HLA antibody-containing whole blood was added to a Cardiopulmonary bypass (CPB) circuit set up to mimic a 20 kg patient undergoing heart transplantation. Plasma was separated and diverted to a standalone, secondary immunoadsorption system, with antibody-depleted plasma returned to the CPB circuit. Samples for anti-HLA antibody definition were taken at baseline, when combined with the CPB prime (on bypass), and then every 20 min for the duration of treatment (total 180 min). RESULTS: A reduction in individual allele median fluorescence intensity (MFI) to below clinically relevant levels (<1000 MFI), and in the majority of cases below the lower positive detection limit (<500 MFI), even in alleles with a baseline MFI >4000 was demonstrated. Reduction occurred in all cases within 120 min, demonstrating efficacy in a time period usual for heart transplantation. Flowcytometric crossmatching of suitable pseudo-donor lymphocytes demonstrated a change from T cell and B cell positive channel shifts to negative, demonstrating a reduction in binding capacity. CONCLUSIONS: Intraoperative immunoadsorption in an ex vivo setting demonstrates clinically relevant reductions in anti-HLA antibodies within the normal timeframe for heart transplantation. This method represents a potential desensitization technique that could enable sensitized children to accept a donor organ earlier, even in the presence of donor-specific anti-HLA antibodies.

2D sodium MRI of the human calf using half-sinc excitation pulses and compressed sensing.

PURPOSE: Sodium MRI can be used to quantify tissue sodium concentration (TSC) in vivo; however, UTE sequences are required to capture the rapidly decaying signal. 2D MRI enables high in-plane resolution but typically has long TEs. Half-sinc excitation may enable UTE; however, twice as many readouts are necessary. Scan time can be minimized by reducing the number of signal averages (NSAs), but at a cost to SNR. We propose using compressed sensing (CS) to accelerate 2D half-sinc acquisitions while maintaining SNR and TSC. METHODS: Ex vivo and in vivo TSC were compared between 2D spiral sequences with full-sinc (TE = 0.73 ms, scan time ≈ 5 min) and half-sinc excitation (TE = 0.23 ms, scan time ≈ 10 min), with 150 NSAs. Ex vivo, these were compared to a reference 3D sequence (TE = 0.22 ms, scan time ≈ 24 min). To investigate shortening 2D scan times, half-sinc data was retrospectively reconstructed with fewer NSAs, comparing a nonuniform fast Fourier transform to CS. Resultant TSC and image quality were compared to reference 150 NSAs nonuniform fast Fourier transform images. RESULTS: TSC was significantly higher from half-sinc than from full-sinc acquisitions, ex vivo and in vivo. Ex vivo, half-sinc data more closely matched the reference 3D sequence, indicating improved accuracy. In silico modeling confirmed this was due to shorter TEs minimizing bias caused by relaxation differences between phantoms and tissue. CS was successfully applied to in vivo, half-sinc data, maintaining TSC and image quality (estimated SNR, edge sharpness, and qualitative metrics) with ≥50 NSAs. CONCLUSION: 2D sodium MRI with half-sinc excitation and CS was validated, enabling TSC quantification with 2.25 × 2.25 mm2 resolution and scan times of ≤5 mins.

HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination.

PURPOSE: Interactive cardiac MRI is used for fast scan planning and MR-guided interventions. However, the requirement for real-time acquisition and near-real-time visualization constrains the achievable spatio-temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low-latency reconstruction (deep artifact suppression). METHODS: A variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath-held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework-HyperSLICE- was compared with conventional Cartesian real-time and breath-hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi-squared tests with post hoc Nemenyi test (p < 0.05). RESULTS: In simulations the normalized RMS error, peak SNR, structural similarity, and Laplacian energy were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (structural similarity: 0.71 vs. 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real-time imaging. The pipeline was demonstrated in patients during catheter pull back, showing sufficiently fast reconstruction for interactive imaging. CONCLUSION: HyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared with radial and uniform spiral trajectories.

Toward Autonomous Pulmonary Artery Catheterization: A Learning-based Robotic Navigation System.

Providing imaging during interventional treatments of cardiovascular diseases is challenging. Magnetic Resonance Imaging (MRI) has gained popularity as it is radiation-free and returns high resolution of soft tissue. However, the clinician has limited access to the patient, e.g., to their femoral artery, within the MRI scanner to accurately guide and manipulate an MR-compatible catheter. At the same time, communication will need to be maintained with a clinician, located in a separate control room, to provide the most appropriate image to the screen inside the MRI room. Hence, there is scope to explore the feasibility of how autonomous catheterization robots could support the steering of catheters along trajectories inside complex vessel anatomies.In this paper, we present a Learning from Demonstration based Gaussian Mixture Model for a robot trajectory optimisation during pulmonary artery catheterization. The optimisation algorithm is integrated into a 2 Degree-of-Freedom MR-compatible interventional robot allowing for continuous and simultaneous translation and rotation. Our methodology achieves autonomous navigation of the catheter tip from the inferior vena cava, through the right atrium and the right ventricle into the pulmonary artery where an interventions is performed. Our results show that our MR-compatible robot can follow an advancement trajectory generated by our Learning from Demonstration algorithm. Looking at the overall duration of the intervention, it can be concluded that procedures performed by the robot (teleoperated or autonomously) required significantly less time compared to manual hand-held procedures.

Predictors of outcomes in mild pulmonary hypertension according to 2022 ESC/ERS Guidelines: the EVIDENCE-PAH UK study.

BACKGROUND AND AIMS: Interventional studies in pulmonary arterial hypertension completed to date have shown to be effective in symptomatic patients with significantly elevated mean pulmonary artery pressure (mPAP) (≥25 mmHg) and pulmonary vascular resistance (PVR) > 3 Wood Unit (WU). However, in health the mPAP does not exceed 20 mmHg and PVR is 2 WU or lower, at rest. The ESC/ERS guidelines have recently been updated to reflect this. There is limited published data on the nature of these newly defined populations (mPAP 21-24 mmHg and PVR >2-≤3 WU) and the role of comorbidity in determining their natural history. With the change in guidelines, there is a need to understand this population and the impact of the ESC/ERS guidelines in greater detail. METHODS: A retrospective nationwide evaluation of the role of pulmonary haemodynamics and comorbidity in predicting survival among patients referred to the UK pulmonary hypertension (PH) centres between 2009 and 2017. In total, 2929 patients were included in the study. Patients were stratified by mPAP (<21 mmHg, 21-24 mmHg, and ≥25 mmHg) and PVR (≤2 WU, > 2-≤3 WU, and >3 WU), with 968 (33.0%) in the mPAP <21 mmHg group, 689 (23.5%) in the mPAP 21-24 mmHg group, and 1272 (43.4%) in the mPAP ≥25 mmHg group. RESULTS: Survival was negatively correlated with mPAP and PVR in the population as a whole. Survival in patients with mildly elevated mPAP (21-24 mmHg) or PVR (>2-≤3WU) was lower than among those with normal pressures (mPAP <21 mmHg) and normal PVR (PVR ≤ 2WU) independent of comorbid lung and heart disease [hazard ratio (HR) 1.36, 95% confidence interval (CI) 1.14-1.61, P = .0004 for mPAP vs. HR 1.28, 95% CI 1.10-1.49, P = .0012 for PVR]. Among patients with mildly elevated mPAP, a mildly elevated PVR remained an independent predictor of survival when adjusted for comorbid lung and heart disease (HR 1.33, 95% CI 1.01-1.75, P = .042 vs. HR 1.4, 95% CI 1.06-1.86, P = .019). 68.2% of patients with a mPAP 21-24 mmHg had evidence of underlying heart or lung disease. Patients with mildly abnormal haemodynamics were not more symptomatic than patients with normal haemodynamics. Excluding patients with heart and lung disease, connective tissue disease was associated with a poorer survival among those with PH. In this subpopulation evaluating those with a mPAP of 21-24 mmHg, survival curves only diverged after 5 years. CONCLUSIONS: This study supports the change in diagnostic category of the ESC/ERS guidelines in a PH population. The newly included patients have an increased mortality independent of significant lung or heart disease. The majority of patients in this new category have underlying heart or lung disease rather than an isolated pulmonary vasculopathy. Mortality is higher if comorbidity is present. Rigorous phenotyping will be pivotal to determine which patients are at risk of progressive vasculopathic disease and in whom surveillance and recruitment to studies may be of benefit. This study provides an insight into the population defined by the new guidelines.

Whole-Body Magnetic Resonance Imaging Assessment of the Contributions of Adipose and Nonadipose Tissues to Cardiovascular Remodeling in Adolescents.

Background Greater body mass index is associated with cardiovascular remodeling in adolescents. However, body mass index cannot differentiate between adipose and nonadipose tissues. We examined how visceral and subcutaneous adipose tissue are linked with markers of early cardiovascular remodeling, independently from nonadipose tissue. Methods and Results Whole-body magnetic resonance imaging was done in 82 adolescents (39 overweight/obese; 36 female; median age, 16.3 [interquartile range, 14.4-18.1] years) to measure body composition and cardiovascular remodeling markers. Left ventricular diastolic function was assessed by echocardiography. Waist, waist:height ratio, and body mass index z scores were calculated. Residualized nonadipose tissue, subcutaneous adipose tissue, and visceral adipose tissue variables, uncorrelated with each other, were constructed using partial regression modeling to allow comparison of their individual contributions in a 3-compartment body composition model. Cardiovascular variables mostly related to nonadipose rather than adipose tissue. Nonadipose tissue was correlated positively with left ventricular mass (r=0.81), end-diastolic volume (r=0.70), stroke volume (r=0.64), left ventricular mass:end-diastolic volume (r=0.37), and systolic blood pressure (r=0.35), and negatively with heart rate (r=-0.33) (all P<0.01). Subcutaneous adipose tissue was associated with worse left ventricular diastolic function (r=-0.42 to -0.48, P=0.0007-0.02) and higher heart rates (r=0.34, P=0.007) but linked with better systemic vascular resistance (r=-0.35, P=0.006). There were no significant relationships with visceral adipose tissue and no associations of any compartment with pulse wave velocity. Conclusions Simple anthropometry does not reflect independent effects of nonadipose tissue and subcutaneous adipose tissue on the adolescent cardiovascular system. This could result in normal cardiovascular adaptations to growth being misinterpreted as pathological sequelae of excess adiposity in studies reliant on such measures.

Pulse wave velocity during re-feeding and with weight gain in underweight female adolescents with anorexia nervosa.

Anorexia Nervosa (AN) causes harmful underweight and important cardiovascular acute complications however less is known about longer-term cardiovascular risk. We measured carotid femoral pulse wave velocity (PWV) in a group of underweight young women with AN at baseline and weekly as they were refed and gained weight. PWV decreased over time and was negatively associated with increasing BMI and calorific meal content suggesting potential positive cardiovascular benefits for refeeding and weight gain in AN and supports current consensus for the importance of weight gain in underweight young women with AN.

Multicenter research priorities in pediatric CMR: results of a collaborative wiki survey.

Multicenter studies in pediatric cardiovascular magnetic resonance (CMR) improve statistical power and generalizability. However, a structured process for identifying important research topics has not been developed. We aimed to (1) develop a list of high priority knowledge gaps, and (2) pilot the use of a wiki survey to collect a large group of responses. Knowledge gaps were defined as areas that have been either unexplored or under-explored in the research literature. High priority goals were: (1) feasible and answerable from a multicenter research study, and (2) had potential for high impact on the field of pediatric CMR. Seed ideas were contributed by a working group and imported into a pairwise wiki survey format which allows for new ideas to be uploaded and voted upon ( https://allourideas.org ). Knowledge gaps were classified into 2 categories: 'Clinical CMR Practice' (16 ideas) and 'Disease Specific Research' (22 ideas). Over a 2-month period, 3,658 votes were cast by 96 users, and 2 new ideas were introduced. The 3 highest scoring sub-topics were myocardial disorders (9 ideas), translating new technology & techniques into clinical practice (7 ideas), and normal reference values (5 ideas). The highest priority gaps reflected strengths of CMR (e.g., myocardial tissue characterization; implementation of technologic advances into clinical practice), and deficiencies in pediatrics (e.g., data on normal reference values). The wiki survey format was effective and easy to implement, and could be used for future surveys.

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy.

Distinct cardiovascular phenotypes are associated with prognosis in systemic sclerosis: a cardiovascular magnetic resonance study.

AIMS: Cardiovascular involvement in systemic sclerosis (SSc) is heterogeneous and ill-defined. This study aimed to: (i) discover cardiac phenotypes in SSc by cardiovascular magnetic resonance (CMR); (ii) provide a CMR-based algorithm for phenotypic classification; and (iii) examine for associations between phenotypes and mortality. METHODS AND RESULTS: A retrospective, single-centre, observational study of 260 SSc patients who underwent clinically indicated CMR including native myocardial T1 and T2 mapping from 2016 to 2019 was performed. Agglomerative hierarchical clustering using only CMR variables revealed five clusters of SSc patients with shared CMR characteristics: dilated right hearts with right ventricular failure (RVF); biventricular failure dilatation and dysfunction (BVF); and normal function with average cavity (NF-AC), normal function with small cavity (NF-SC), and normal function with large cavity (NF-LC) sizes. Phenotypes did not co-segregate with clinical or antibody classifications. A CMR-based decision tree for phenotype classification was created. Sixty-three (24%) patients died during a median follow-up period of 3.4 years. After adjustment for age and presence of pulmonary hypertension (PH), independent CMR predictors of all-cause mortality were native T1 (P < 0.001) and right ventricular ejection fraction (RVEF) (P = 0.0032). NF-SC and NF-AC groups had more favourable prognoses (P≤0.036) than the other three groups which had no differences in prognoses between them (P > 0.14). Hazard ratios (HR) were statistically significant for RVF (HR = 8.9, P < 0.001), BVF (HR = 5.2, P = 0.006), and NF-LC (HR = 4.9, P = 0.002) groups. The NF-LC group remained significantly predictive of mortality after adjusting for RVEF, native T1, and PH diagnosis (P = 0.0046). CONCLUSION: We identified five CMR-defined cardiac SSc phenotypes that did not co-segregate with clinical data and had distinct outcomes, offering opportunities for a more precision-medicine based management approach.

Automatic segmentation of the great arteries for computational hemodynamic assessment.

BACKGROUND: Computational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, typically obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. However, segmentation is time-consuming and requires expert input. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries for CFD studies. METHODS: 90 CHD patients were retrospectively selected for this study. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and pulmonary artery labels. These were used to train and optimize a U-Net model, using a 70-10-10 train-validation-test split. Segmentation performance was primarily evaluated using Dice score. CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Mean pressure and velocity fields across 99 planes along the vessel centrelines were extracted, and a mean average percentage error (MAPE) was calculated for each vessel pair (ML vs GT). A second observer (SO) segmented the test dataset for assessment of inter-observer variability. Friedman tests were used to compare ML vs GT, SO vs GT and ML vs SO metrics, and pressure/velocity field errors. RESULTS: The network's Dice score (ML vs GT) was 0.945 (interquartile range: 0.929-0.955) for the aorta and 0.885 (0.851-0.899) for the pulmonary arteries. Differences with the inter-observer Dice score (SO vs GT) and ML vs SO Dice scores were not statistically significant for either aorta or pulmonary arteries (p = 0.741, p = 0.061). The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% (8.5-15.7%) and 4.1% (3.1-6.9%), respectively, and for the pulmonary arteries 14.6% (11.5-23.2%) and 6.3% (4.3-7.9%), respectively. Inter-observer (SO vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to ML vs GT (p > 0.2). CONCLUSIONS: ML can successfully segment the great vessels for CFD, with errors similar to inter-observer variability. This fast, automatic method reduces the time and effort needed for CFD analysis, making it more attractive for routine clinical use.