The left atrium (LA) plays a pivotal role in modulating left ventricular filling, yet its hemodynamics remain poorly understood due to the limitations of conventional ultrasound analysis. Four-dimensional flow magnetic resonance imaging (4D Flow MRI) holds promise for enhancing our understanding of atrial hemodynamics, but its analysis is hindered by the inherently low velocities within the chamber and the modest spatial resolution of 4D Flow MRI. Heterogeneity in acquisition protocols and MRI vendors, and the lack of standardized computational frameworks further complicates the creation of large, comparable datasets needed to assess the prognostic value of hemodynamic markers provided by 4D Flow MRI. To address these challenges, we introduce a computational framework tailored to the analysis of 4D Flow MRI in the LA, enabling the qualitative and quantitative analysis of advanced hemodynamic parameters (e.g., kinetic energy, vorticity, and pressure). We applied this framework to a diverse cohort spanning different degrees of left ventricular diastolic dysfunction to investigate the prognostic potential of these metrics. Our framework proved robustness across multicenter data of varying quality, producing high-accuracy automated segmentations. Notably, our findings show that 4D Flow MRI-derived parameters provide superior differentiation between healthy and pathological states than those available to conventional hemodynamic analysis tools.

Hypertension (HTN) and hypertrophic cardiomyopathy(HCM) induce structural and functional remodelling of the left atrium (LA), contributing to atrial dysfunction and altered flow dynamics. However, the specific impact of these conditions on intra-atrial blood dynamics has not yet been well characterised. In this study, we used a semi-automated Lagrangian-tracking pipeline to quantify distinct intra-atrial flow patterns from 4D flow magnetic resonance imaging (MRI) data. This represents the first application of the approach to compare LA hemodynamics in HTN, HCM, and controls. This pipeline enables the identification and quantification of distinct intra-atrial blood flow patterns, unlike conventional qualitative assessments or global flow quantification indices. The findings demonstrate significant, common changes in LA function in both diseased groups as a result of chronic pressure overload. Both patient groups showed significantly reduced conduit flow (p < 0.001) and increased retained and residual volumes (p < 0.01), indicating impaired LA emptying and a shift toward stasis-prone flow. Particle trajectories were less curved in disease, reflecting a loss of organised vortical structures. Pulmonary venous (PV) systolic and diastolic velocities were attenuated (both p < 0.01), and PV backflow decreased (p < 0.05), consistent with reduced atrial compliance. HTN and HCM groups showed significantly altered kinetic energy (KE) curves, revealing disruption of LA energetics. Collectively, these quantitative 4D flow MRI biomarkers provide novel insight into shared patterns of atrial dysfunction in pressure-overload cardiomyopathies and may serve as non-invasive markers for disease severity.

The left atrium (LA) plays a pivotal role in modulating left ventricular filling, but our comprehension of its hemodynamics is significantly limited by the constraints of conventional ultrasound analysis. 4D flow magnetic resonance imaging (4D Flow MRI) holds promise for enhancing our understanding of atrial hemodynamics. However, the low velocities within the LA and the limited spatial resolution of 4D Flow MRI make analyzing this chamber challenging. Furthermore, the absence of dedicated computational frameworks, combined with diverse acquisition protocols and vendors, complicates gathering large cohorts for studying the prognostic value of hemodynamic parameters provided by 4D Flow MRI. In this study, we introduce the first open-source computational framework tailored for the analysis of 4D Flow MRI in the LA, enabling comprehensive qualitative and quantitative analysis of advanced hemodynamic parameters. Our framework proves robust to data from different centers of varying quality, producing high-accuracy automated segmentations (Dice > 0.9 and Hausdorff 95 < 3 mm), even with limited training data. Additionally, we conducted the first comprehensive assessment of energy, vorticity, and pressure parameters in the LA across a spectrum of disorders to investigate their potential as prognostic biomarkers.

Clinical translation of personalised computational physiology workflows and digital twins can revolutionise healthcare by providing a better understanding of an individual's physiological processes and any changes that could lead to serious health consequences. However, the lack of common infrastructure for developing these workflows and digital twins has hampered the realisation of this vision. The Auckland Bioengineering Institute’s 12 LABOURS project aims to address these challenges by developing a Digital Twin Platform to enable researchers to develop and personalise computational physiology models to an individual’s health data in clinical workflows. This will allow clinical trials to be more efficiently conducted to demonstrate the efficacy of these personalised clinical workflows. We present a demonstration of the platform's capabilities using publicly available data and an existing automated computational physiology workflow developed to assist clinicians with diagnosing and treating breast cancer. We also demonstrate how the platform facilitates the discovery and exploration of data and the presentation of workflow results as part of clinical reports through a web portal. Future developments will involve integrating the platform with health systems and remote-monitoring devices such as wearables and implantables to support home-based healthcare. Integrating outputs from multiple workflows that are applied to the same individual's health data will also enable the generation of their personalised digital twin.Clinical Relevance— The proposed 12 LABOURS Digital Twin Platform will enable researchers to 1) more efficiently conduct clinical trials to assess the efficacy of their computational physiology workflows and support the clinical translation of their research; 2) reuse primary and derived data from these workflows to generate novel workflows; and 3) generate personalised digital twins by integrating the outputs of different computational physiology workflows.

Abstract Introduction Changes in left atrial (LA) hemodynamics have been suggested reflective of left ventricular (LV) disease progression. Kinetic energy (KE), viscous energy loss, and vorticity could be promising indices for the assessment of left atrial dynamics by time resolved four-dimensional cardiac magnetic resonance (4D flow CMR). Hypertrophic cardiomyopathy (HCM) patients are at risk of developing mitral regurgitation and left ventricular outflow tract obstruction, with poor outcome (1). Hypertensive myocardial remodelling has been suggested to be due to complex flow and tissue interactions leading to hypertrophy and diastolic dysfunction (2). Early detection of altered LA hemodynamic abnormalities may help non-invasive assessment, improving their long-term outcome (3). Purpose Quantitative and qualitative evaluation of left atrial hemodynamic metrics by 4D flow CMR in a cohort of HCM patients, hypertensive patients, and healthy controls with no known cardiovascular disease. Methods Eighteen HCM patients (50.0 [±18.2] years, 8 females), twenty-one hypertensive patients (55.2 [±6.2] years, 11 females), and seventeen controls (36.7 [±12.8] years, 1 female) underwent 4D flow CMR. Both patient groups had significantly higher LV-indexed mass, end-diastolic volume, and end-systolic volumes (P < 0.05). A semi-automated pipeline was used to segment the LA including pulmonary inflow and mitral outflow tracts using phase contrast magnetic resonance images derived from the 4D flow acquisition's phase and magnitude images. LA KE, viscous energy loss and vorticity were computed throughout the cardiac cycle. Results The HCM and hypertensive groups demonstrated significantly higher average KE integrated across the cardiac cycle (1.58 ± 0.79 and 1.32 ± 0.50 mJ respectively) compared to controls (1.20 ± 0.47 mJ) (P < 0.05), with the systolic curve in HCM showing the highest peak KE. Qualitatively, KE in the diastolic portion of the cardiac cycle appeared less variable between groups (Figure 1). Vorticity in HCM showed different configurations with loss of the central atrial vortex core (Figure 2). Viscous energy loss was also significantly higher in HCM patients confirming the increase in energy loss (Figure 2). Average KE across the cardiac cycle highly correlated with LA volume and pulmonary capillary wedge pressure calculated by a previously published regression method (4) (Pearson correlation: r= 0.76 and 0.71 respectively P < 0.05). There were also significant correlations with cardiac metrics such as indexed end-diastolic and indexed end-systolic volumes (Pearson correlation: r= 0.54 and 0.52 respectively P < 0.05) as well as indexed LV mass (r= 0.59 P < 0.05). Conclusion Hemodynamic metrics derived from 4D flow CMR may be valuable in establishing valuable predictive metrics of ventricular function. Integration of 4D flow MRI in clinical workflows could provide information that may be indicative of pathological onset before abnormal remodelling.Kinetic energy and viscous energy lossParticle pathlines and vorticity

Four-dimensional (4D) flow cardiovascular magnetic resonance (4D flow CMR) imaging has evolved as a versatile imaging technique for the assessment of cardiovascular three-dimensional time resolved haemodynamics. This study aimed to examine the utility and metrics derived from 4D flow CMR in different patient cohorts.

Our study methodology is motivated from three disparate needs: one, imaging studies have existed in silo and study organs but not across organ systems; two, there are gaps in our understanding of paediatric structure and function; three, lack of representative data in New Zealand. Our research aims to address these issues in part, through the combination of magnetic resonance imaging, advanced image processing algorithms and computational modelling. Our study demonstrated the need to take an organ-system approach and scan multiple organs on the same child. We have pilot tested an imaging protocol to be minimally disruptive to the children and demonstrated state-of-the-art image processing and personalized computational models using the imaging data. Our imaging protocol spans brain, lungs, heart, muscle, bones, abdominal and vascular systems. Our initial set of results demonstrated child-specific measurements on one dataset. This work is novel and interesting as we have run multiple computational physiology workflows to generate personalized computational models. Our proposed work is the first step towards achieving the integration of imaging and modelling improving our understanding of the human body in paediatric health and disease.

Elevated left ventricular filling pressure (LVFP) (≥15 mmHg) is a haemodynamic marker of diastolic dysfunction measured invasively during cardiac catheterisation. Attention has recently been drawn to several studies using multivariate regression approaches to identify elevated LVFP using non-invasive imaging. This study investigated the utility of a regression model to estimate LVFP using routine two-dimensional echocardiography (2DE) parameters.

Left atrial haemodynamics by time-resolved four-dimensional cardiac magnetic resonance (4D flow CMR) were suggested to yield noninvasive markers that provide insight into ventricular diastolic dysfunction. It is suggested that 4D flow parameters may be superior to transthoracic echocardiography. This study examined various flow metrics in the pulmonary veins and the mitral flow for the early detection of left diastolic dysfunction. Controls (n=10) and left ventricular hypertrophy (LVH) (n=8) patients were scanned prospectively at the university of Auckland Centre of Advanced MRI. The four pulmonary veins and mitral flow were examined. End systolic and end diastolic volumes in LVH participants were not significantly different from controls but ventricular mass over end diastolic volume ratios were significantly different (control 0.71±0.08 and LVH 0.84±0.11; p<0.05). Pulmonary temporal curves demonstrated features that differed from controls, for example: the pulmonary peak velocity showed the atrial reversal flow (AR-wave) was significantly different between groups (p<0.05). Average pulmonary vein derived pressure was significantly higher in LVH (control 0.51±0.40 and LVH 2.52±0.81 mmHg; p<0.05). Mitral flow pressure demonstrated the E/A differences between groups with the appearance of the L-wave in mid-diastole, but averaged mitral pressure was not different between groups. The ICC between modalities was 0.64 with the mitral E/A measurement. 4D flow CMR showed differences in flow between study groups, with the potential of more accurate evaluation of diastolic function through more precision of flow-derived indices.

Remodeling in adults with repaired tetralogy of Fallot (rToF) may occur due to chronic pulmonary regurgitation, but may also be related to altered flow patterns, including vortices. We aimed to correlate and quantify relationships between vorticity and ventricular shape derived from atlas-based analysis of biventricular shape. Adult rToF (n = 12) patients underwent 4D flow and cine MRI imaging. Vorticity in the RV was computed after noise reduction using a neural network. A biventricular shape atlas built from 95 rToF patients was used to derive principal component modes, which were associated with vorticity and pulmonary regurgitant volume (PRV) using univariate and multivariate linear regression. Univariate analysis showed that indexed PRV correlated with 3 modes (r = -0.55,-0.50, and 0.6, all p < 0.05) associated with RV dilatation and an increase in basal bulging, apical bulging and tricuspid annulus tilting with more severe regurgitation, as well as a smaller LV and paradoxical movement of the septum. RV outflow and inflow vorticity were also correlated with these modes. However, total vorticity over the whole RV was correlated with two different modes (r = -0.62,-0.69, both p < 0.05). Higher vorticity was associated with both RV and LV shape changes including longer ventricular length, a larger bulge beside the tricuspid valve, and distinct tricuspid tilting. RV flow vorticity was associated with changes in biventricular geometry, distinct from associations with PRV. Flow vorticity may provide additional mechanistic information in rToF remodeling. Both LV and RV shapes are important in rToF RV flow patterns.

The lack of standardized pipelines for image processing has prevented the application of deep learning (DL) techniques for the segmentation of the aorta in phase-contrast enhanced magnetic resonance angiography (PC-MRA). Furthermore, large, well-curated and annotated datasets, which are needed to create DL-based models able to generalize, are rare. We present the adaptation of the popular nnU-net DL framework to automatically segment the aorta in 4D flow MRI-derived angiograms. The resulting segmentations in a large database ( $$> 300$$ cases) with normal cases and examples of different pathologies of the aorta provided from a single centre were excellent after post-processing (Dice score of 0.944). Subsequently, we explored the generalisation of the trained network in a small dataset of images (around 20 cases) acquired in a different hospital with another scanner. Without domain adaptation, only with a model trained with the large dataset, the obtained results were substantially worst than with adding a few cases of the small dataset (Dice scores of 0.61 vs 0.86, respectively). The obtained results created good quality segmentations of the aorta in 4D flow MRI, which can later be post-processed to assess blood flow patterns, similarly than with manual annotations. However, advanced domain adaptation schemes are very important in 4D flow MRI due to the large differences in image characteristics between different vendor scanners available in multiple centers.

BackgroundPatients with repaired Tetralogy of Fallot (rTOF) often develop cardiovascular dysfunction and require regular imaging to evaluate deterioration and time interventions such as pulmonary valve replacement. Four-dimensional flow cardiovascular magnetic resonance (4D flow CMR) enables detailed assessment of flow characteristics in all chambers and great vessels. We performed a systematic review of intra-cardiac 4D flow applications in rTOF patients, to examine clinical utility and highlight optimal methods for evaluating rTOF patients.MethodsA comprehensive literature search was undertaken in March 2020 on Google Scholar and Scopus. A modified version of the Critical Appraisal Skills Programme (CASP) tool was used to assess and score the applicability of each study. Important clinical outcomes were assessed including similarities and differences.ResultsOf the 635 articles identified, 26 studies met eligibility for systematic review. None of these were below 59% applicability on the modified CASP score. Studies could be broadly classified into four groups: (i) pilot studies, (ii) development of new acquisition methods, (iii) validation and (vi) identification of novel flow features. Quantitative comparison with other modalities included 2D phase contrast CMR (13 studies) and echocardiography (4 studies). The 4D flow study applications included stroke volume (18/26;69%), regurgitant fraction (16/26;62%), relative branch pulmonary artery flow(4/26;15%), systolic peak velocity (9/26;35%), systemic/pulmonary total flow ratio (6/26;23%), end diastolic and end systolic volume (5/26;19%), kinetic energy (5/26;19%) and vorticity (2/26;8%).Conclusions4D flow CMR shows potential in rTOF assessment, particularly in retrospective valve tracking for flow evaluation, velocity profiling, intra-cardiac kinetic energy quantification, and vortex visualization. Protocols should be targeted to pathology. Prospective, randomized, multi-centered studies are required to validate these new characteristics and establish their clinical use.

Abstract Background Repaired tetralogy of Fallot adults (rToF) undergo right ventricular (RV) remodeling, in part due to volume overload of residual pulmonary regurgitation volume (PRV). Time-resolved phase-contrast cardiac magnetic resonance imaging (4D Flow MRI) enables the qualitative and quantitative measurement of altered blood flow patterns, including vorticity. Cardiac atlases allow for complex three-dimensional heart shapes to be expressed as morphometric scores. Those scores show the extent of geometrical shift and can help explore uncharted relationships between vorticity and architecture. Purpose We aimed to quantify vorticity, incorporating deep learning to enhance 4D Flow data, and correlate this with global cardiac parameters and morphometric scores. Methods 12 Adult rToF patients and 10 age-matched controls underwent 4D flow MRI and cine imaging. RV interventricular vorticity was calculated for outflow and inflow tracts. EDV, ESV and SV were computed from cines which were also used to build three-dimensional shape models. The biventricular models were projected onto an atlas generated from 95 rToF patients, and twenty-one principal component analysis shape modes were correlated with cardiac metrics and vorticity to identify global shape variations. Association between biventricular shape and vorticity was further analysed using multivariate multiple regression models. Results Strong correlation was found between PRV and the right ventricular outflow tract (RVOT) vorticity. PRV and RVOT vorticity both correlated with the same 3 shape modes (r=−0.55, −0.50 and 0.6 (p<0.05) respectively for PR and r=0.63, −0.82 and 0.60 (p<0.05) respectively for vorticity) i.e., the RV dilates with an increase in basal bulging, apical bulging and tricuspid annulus tilting with more severe regurgitation, as well as a smaller LV, and a paradoxical movement of the septum (Figure 1). However, RV vorticity correlated with 2 modes that did not correlate with PRV, (r=−0.62, −0.69, p<0.05). With higher vorticity the RV was longer, increased tilting of the tricuspid annulus and an increased basal bulge around the tricuspid area. The multivariate analysis model demonstrated that higher vorticity was associated with displacement of the pulmonary valve and change in the RVOT length and direction. A septal displacement towards the left ventricle was observed and increased apical flatness of the RV (Figure 1). Qualitatively, vorticity in rToF group was more heterogeneous than controls (Figure 2). Conclusions Vorticity is a novel marker based on the influence of blood motion providing new insight into early diagnosis and prognosis of cardiac disease. This is the first study to examine the relationships between vorticity and regional RV shape changes in rToF. Mode associations with vorticity were different to associations with PRV. More longitudinal studies are required for standardization of change in vorticity with the disease process. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): The New Zealand heart foundation Mode variations and morphometric modelVorticity visualization and analysis

Introduction: Alterations in flow patterns are believed to be a cause of some pathological consequences of irreversible cardiac modelling and have recently gained interest. 4D flow MRI is a faster, single acquisition, with no breath holds and is recently moving towards clinical settings. Objective: Visualisation and qualitative assessment of vortices in the cardiac chambers and large vessels with 4D flow MRI Methodology and Results: Twenty subjects were imaged with a 1.5T MRI at a Velocity encoding (VENC) of 150 and 50 cm/s and visualised using 4D Flow Demonstrator V2.3 (Siemens AG, Erlangen, Germany). Flow through particle traces from emitter planes in the pulmonary arteries (main, right and left), the aorta and big veins was observed. Right atrium showed a clockwise vortex during systole in rapid filling phase from the superior and inferior vena cava (A), flow went directly up the outflow tract towards the pulmonary artery (B) with minimal flow in the apex of the right ventricle. Organised flow was observed in most cases up the aorta and pulmonary arteries. Vortices in the left atrium were more complex with a dominating anteroposterior clockwise vortex (C). Filling through the mitral valve (D) was with a sub-mitral clockwise vortex that reached the ventricular apex (E). VENC using a single value of 150 cm/s is acceptable in flow visualisation in the large arteries. Lower VENC is suggested for visualisation of intraventricular flow vortices. Conclusion: Visualisation of intracardiac vortices is feasible with a high potential of qualitative assessment via the current software.

Introduction: Current 2D MRI flow quantification is acquired during different breath holds, resulting in variations in cardiac output. 4D flow MRI is faster but in need of clinical validation and improved software to achieve widespread clinical application. Objective: Comparison of velocity and volume measurements acquired from 2D MRI and echocardiography, with 4D flow MRI. Methodology and Results: Twenty participants were imaged on a 1.5 T MRI, and SC2000 ultrasound. Stroke volume was measured using standard methods and compared with 4D flow. The two methods showed correlation for the left ventricular (ICC 0.864) and right ventricular outputs (ICC 0.904). The mean forward flow through the pulmonary valve was 91.55 (±19.17) ml/cycle measured with 4D and 99.32 (±22.9) ml/cycle measured with 2D (p = 0.0006, r = 0.838). The aortic valve flow was 91.82 (±19.90) ml/cycle with 4D and 87.77 (±23.5) ml/cycle measured with 2D (p = 0.001, r = 0.771). Lower agreement was seen when comparing 4D flow with echocardiography regarding peak velocity in the ascending aorta (r = 0.335). To test the accuracy of the acquisition, correlation between 4D flow volume in the main pulmonary, and the sum of the left and right branches was 0.965; the pulmonary to the aortic flow volumes showed excellent correlation of 0.927. Another test was the average regurgitant volume for 4D flow across all the planes in all vessels, which was 1.36 ml/cycle. Conclusion: Stroke volume was accurately quantified using 4D flow MRI, it also enabled measuring peak velocities and regurgitant fractions.