The Enduring Impact of Shape Following Perfect Coarctation of the Aorta Repair.

Objectives: Aortic arch appearances can be associated with worse cardiac function and chronic hypertension late after coarctation of the aorta (CoA) repair, even without residual obstruction. Statistical shape modeling (SSM) has identified specific 3D arch shapes linked to poorer cardiovascular outcomes. We sought a mechanistic explanation. Methods: From 53 asymptomatic patients late after CoA repair with no residual obstruction (age: 22.3 ± 5.6 years; 12-38 years after operation), eight aortic arch shapes associated with the four best and four worst cardiovascular parameters were obtained from 3D SSM. Four favorable shapes were affiliated with left ventricular (LV) ejection fraction +2 standard deviation (SD) values from the mean, and indexed LV end diastolic volume/indexed LV mass/resting systolic blood pressure that were -2SD. Four unfavorable shapes were defined by the reverse. Computational Fluid Dynamics modeling was carried out to assess differences in pressure gradient across the aortic arch and viscous energy loss (VEL) between favorable and unfavorable aortic arches. Results: In all aortic arches, the pressure gradients were clinically insignificant (<8 mm Hg). However, in the four unfavorable aortic arches, VEL were uniformly higher than those in the favorable shapes (VEL difference: 15%-32%). There was increased turbulence and more complex propagation of VEL along with the unfavorable aortic arches. Conclusions: This study reveals the variable flow dynamics that underpin the association of aortic arch shapes with worse cardiovascular outcomes late after successful CoA repair. Higher VEL persists in the unfavorable aortic arch shapes. Further understanding of the mechanism of viscous energy loss in cardiovascular maladaptation may afford mitigating strategies to monitor and modify this unrelenting liability.

Machine Learning and Statistical Shape Modelling Methodologies to Assess Vascular Morphology before and after Aortic Valve Replacement.

Introduction: Statistical shape modelling (SSM) is used to analyse morphology, discover qualitatively and quantitatively unique shape features within a population, and generate mean shapes and shape modes that show morphological variability. Hierarchical agglomerative clustering is a machine learning analysis used to identify subgroups within a given population in relation to shape features. We tested the application of both methods in the clinically relevant scenario of patients undergoing aortic valve repair (AVR). Every year, around 5000 patients undergo surgical AVR in the UK. Aims: Evaluate aortic morphology and identify subgroups amongst patients who had undergone AVR, including Ozaki, Ross, and valve-sparing procedures using SSM and unsupervised hierarchical clustering analysis. This methodological framework can evaluate both pre- and post-surgical variability across subgroups undergoing different surgeries. Methods: Pre- (n = 47) and post- (n = 35) operative three-dimensional (3D) aortic models were reconstructed from computed tomography (CT) and cardiac magnetic resonance (CMR) images. Computational analyses for SSM and hierarchical clustering were run separately for the two subgroups, assessing (a) ascending aorta only and (b) the whole aorta. This allows for exploring possible variations in morphological classification related to the input shape. Results: Most patients in the Ross procedure subgroup exhibited differences in aortic morphology from other subgroups, including an elongated ascending and wide aortic arch pre-operatively, and an elongated ascending aorta with a slightly enlarged sinus post-operatively. In hierarchical clustering, the Ross aortas also appeared to cluster together compared to the other surgical procedures, both pre-operatively and post-operatively. There were significant differences between clusters in terms of clustering distance in the pre-operative analyses (p = 0.003 for ascending aortas, p = 0.016 for whole aortas). There were no significant differences between the clusters in post-operative analyses (p = 0.47 for ascending, p = 0.19 for whole aorta). Conclusions: We demonstrated the feasibility of evaluating aortic morphology before and after different aortic valve surgeries using SSM and hierarchical clustering. This framework could be used to further explore shape features associated with surgical decision-making pre-operatively and, importantly, to identify subgroups whose morphology is associated with poorer clinical outcomes post-operatively. Statistical shape modelling (SSM) and unsupervised hierarchical clustering are two statistical methods that can be used to assess morphology, show morphological variations, with the latter being able to identify subgroups within a population. These methods have been applied to the population of aortic valve replacement (AVR) patients since there are different surgical procedures (traditional AVR, Ozaki, Ross, and valve-sparing). The aim is to evaluate aortic morphology and identify subgroups within this population before and after surgery. Computed tomography and cardiac magnetic resonance images were reconstructed into 3D models of the ascending aorta and whole aorta, which were then input into SSM and hierarchical clustering. The results show that the Ross aortic morphology is quite different from the other aortas. The clustering did not classify the aortas based on the surgical procedures; however, most of the Ross group did cluster together, indicating low variability within this surgical group.

Aortic geometry and long-term outcome in patients with a repaired coarctation.

OBJECTIVE: This study aims to compare aortic morphology between repaired coarctation patients and controls, and to identify aortic morphological risk factors for hypertension and cardiovascular events (CVEs) in coarctation patients. METHODS: Repaired coarctation patients with computed tomography angiography (CTA) or magnetic resonance angiography (MRA) were included, followed-up and compared with sex-matched and age-matched controls. Three-dimensional aortic shape was reconstructed using patients' CTA or MRA, or four-dimensional flow cardiovascular magnetic resonance in controls, and advanced geometrical characteristics were calculated and visualised using statistical shape modelling. In patients, we examined the association of geometrical characteristics with (1) baseline hypertension, using multivariable logistic regression; and (2) cardiovascular events (CVE, composite of aortic complications, coronary artery disease, ventricular arrhythmias, heart failure hospitalisation, stroke, transient ischaemic attacks and cardiovascular death), using multivariable Cox regression. The least absolute shrinkage and selection operator (LASSO) method selected the most informative multivariable model. RESULTS: Sixty-five repaired coarctation patients (23 years (IQR 19-38)) were included, of which 44 (68%) patients were hypertensive at baseline. After a median follow-up of 8.7 years (IQR 4.8-15.4), 27 CVEs occurred in 20 patients. Aortic arch dimensions were smaller in patients compared with controls (diameter p<0.001, wall surface area p=0.026, volume p=0.007). Patients had more aortic arch torsion (p<0.001) and a higher curvature (p<0.001). No geometrical characteristics were associated with hypertension. LASSO selected left ventricular mass, male sex, tortuosity and age for the multivariable model. Left ventricular mass (p=0.014) was independently associated with CVE, and aortic tortuosity showed a trend towards significance (p=0.070). CONCLUSION: Repaired coarctation patients have a smaller aortic arch and a more tortuous course of the aorta compared with controls. Besides left ventricular mass index, geometrical features might be of importance in long-term risk assessment in coarctation patients.

Aortic Dissection is Determined by Specific Shape and Hemodynamic Interactions.

The aim of this study was to determine whether specific three-dimensional aortic shape features, extracted via statistical shape analysis (SSA), correlate with the development of thoracic ascending aortic dissection (TAAD) risk and associated aortic hemodynamics. Thirty-one patients followed prospectively with ascending thoracic aortic aneurysm (ATAA), who either did (12 patients) or did not (19 patients) develop TAAD, were included in the study, with aortic arch geometries extracted from computed tomographic angiography (CTA) imaging. Arch geometries were analyzed with SSA, and unsupervised and supervised (linked to dissection outcome) shape features were extracted with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively. We determined PLS-DA to be effective at separating dissection and no-dissection patients ([Formula: see text]), with decreased tortuosity and more equal ascending and descending aortic diameters associated with higher dissection risk. In contrast, neither PCA nor traditional morphometric parameters (maximum diameter, tortuosity, or arch volume) were effective at separating dissection and no-dissection patients. The arch shapes associated with higher dissection probability were supported with hemodynamic insight. Computational fluid dynamics (CFD) simulations revealed a correlation between the PLS-DA shape features and wall shear stress (WSS), with higher maximum WSS in the ascending aorta associated with increased risk of dissection occurrence. Our work highlights the potential importance of incorporating higher dimensional geometric assessment of aortic arch anatomy in TAAD risk assessment, and in considering the interdependent influences of arch shape and hemodynamics as mechanistic contributors to TAAD occurrence.

Feasibility of a longitudinal statistical atlas model to study aortic growth in congenital heart disease.

Studying anatomical shape progression over time is of utmost importance to refine our understanding of clinically relevant processes. These include vascular remodeling, such as aortic dilation, which is particularly important in some congenital heart defects (CHD). A novel methodological framework for three-dimensional shape analysis has been applied for the first time in a CHD scenario, i.e., bicuspid aortic valve (BAV) disease, the most common CHD. Three-dimensional aortic shapes (n = 94) reconstructed from cardiovascular magnetic resonance imaging (MRI) data as surface meshes represented the input for a longitudinal atlas model, using multiple scans over time (n = 2-4 per patient). This model relies on diffeomorphism transformations in the absence of point-to-point correspondence, and on the right combination of initialization, estimation and registration parameters. We computed the shape trajectory of an average disease progression in our cohort, as well as time-dependent parameters, geometric variations and the average shape of the population. Results cover a spatiotemporal spectrum of visual and numerical information that can be further used to run clinical associations. This proof-of-concept study demonstrates the feasibility of applying advanced statistical shape models to track disease progression and stratify patients with CHD.

Visual Analytics Platform for Centralized Covid-19 Digital Contact Tracing.

The Covid-19 pandemic and its dramatic worldwide impact have required global multidisciplinary actions to mitigate its effects. Mobile phone activity-based digital contact tracing (DCT) via Bluetooth Low Energy (BLE) technology has been considered a powerful pandemic monitoring tool, yet it sparked a controversial debate about privacy risks for people. In order to explore the potential benefits of a DCT system in the context of Occupational Risk Prevention, this paper presents the potential of Visual Analytics methods to summarize and extract relevant information from complex DCT data collected during a long-term experiment at our research centre. Visual tools were combined with quantitative metrics to provide insights into contact patterns among volunteers. Results showed that crucial actors such as participants acting as bridges between groups could be easily identified - ultimately allowing for making more informed management decisions aimed at containing the potential spread of a disease.

ArchABM: An agent-based simulator of human interaction with the built environment. CO2 and viral load analysis for indoor air quality.

Recent evidence suggests that SARS-CoV-2, which is the virus causing a global pandemic in 2020, is predominantly transmitted via airborne aerosols in indoor environments. This calls for novel strategies when assessing and controlling a building's indoor air quality (IAQ). IAQ can generally be controlled by ventilation and/or policies to regulate human-building-interaction. However, in a building, occupants use rooms in different ways, and it may not be obvious which measure or combination of measures leads to a cost- and energy-effective solution ensuring good IAQ across the entire building. Therefore, in this article, we introduce a novel agent-based simulator, ArchABM, designed to assist in creating new or adapt existing buildings by estimating adequate room sizes, ventilation parameters and testing the effect of policies while taking into account IAQ as a result of complex human-building interaction patterns. A recently published aerosol model was adapted to calculate time-dependent carbon dioxide (CO2) and virus quanta concentrations in each room and inhaled CO2 and virus quanta for each occupant over a day as a measure of physiological response. ArchABM is flexible regarding the aerosol model and the building layout due to its modular architecture, which allows implementing further models, any number and size of rooms, agents, and actions reflecting human-building interaction patterns. We present a use case based on a real floor plan and working schedules adopted in our research center. This study demonstrates how advanced simulation tools can contribute to improving IAQ across a building, thereby ensuring a healthy indoor environment.

Three-Dimensional Calvarial Growth in Spring-Assisted Cranioplasty for Correction of Sagittal Synostosis.

Spring-assisted cranioplasty (SAC) is a minimally invasive technique for treating sagittal synostosis in young infants. Yet, follow-up data on cranial growth in patients who have undergone SAC are lacking. This project aimed to understand how the cranial shape develops during the postoperative period, from spring insertion to removal. 3D head scans of 30 consecutive infants undergoing SAC for sagittal synostosis were acquired using a handheld scanner pre-operatively, immediately postoperatively, at follow-up and at spring removal; 3D scans of 41 age-matched control subjects were also acquired. Measurements of head length, width, height, circumference, and volume were taken for all subjects; cephalic index (CI) was calculated. Statistical shape modeling was used to compute 3D average head models of sagittal patients at the different time points. SAC was performed at a mean age of 5.2 months (range 3.3-8.0) and springs were removed 4.3 months later. CI increased significantly (P < 0.001) from pre-op (69.5% ±â€Š2.8%) to spring removal (74.4% ±â€Š3.9%), mainly due to the widening of head width, which became as wide as for age-matched controls; however, the CI of controls was not reached (82.3% ±â€Š6.8%). The springs did not constrain volume changes and allowed for natural growth. Population mean shapes showed that the bony prominences seen at the sites of spring engagement settle over time, and that springs affect the overall 3D head shape of the skull. In conclusion, results reaffirmed the effectiveness of SAC as a treatment method for nonsyndromic single suture sagittal synostosis.

Aortic morphological variability in patients with bicuspid aortic valve and aortic coarctation.

OBJECTIVES: This study aimed to explore aortic morphology and the associations between morphological features and cardiovascular function in a population of patients with bicuspid aortic valve, while further assessing differences between patients with repaired coarctation, patients with unrepaired coarctation and patients without coarctation. METHODS: This is a single-centre retrospective study that included patients with available cardiovascular magnetic resonance imaging data and native bicuspid aortic valve diagnosis (n = 525). A statistical shape analysis was performed on patients with a 3-dimensional magnetic imaging resonance (MRI) dataset (n = 108), deriving 3-dimensional aortic reconstructions and computing a mean aortic shape (template) for the whole population as well as for the 3 subgroups of interest (no coarctation, repaired coarctation and unrepaired coarctation). Shape deformations (modes) were computed and correlated with demographic variables, 2-dimensional MRI measurements and volumetric and functional data. RESULTS: Overall, the results showed that patients with coarctation tended towards a more Gothic arch architecture, with decreased ascending and increased descending aorta diameters, with the unrepaired-aortic coarctation subgroup exhibiting more ascending aorta dilation. Careful assessment of patients with repaired coarctation only revealed that a more Gothic arch, increased descending aorta dimensions and ascending aorta dilation were associated with reduced ejection fraction (P ≤ 0.04), increased end-diastolic volume (P ≤ 0.04) and increased ventricular mass (P ≤ 0.02), with arch morphology distinguishing patients with and without recoarctation (P = 0.05). CONCLUSIONS: A statistical shape modelling framework was applied to a bicuspid aortic valve population revealing nuanced differences in arch morphology and demonstrating that morphological features, not immediately described by conventional measurements, can indicate those shape phenotypes associated with compromised function and thus possibly warranting closer follow-up.

Statistical Shape Modeling for Cavopulmonary Assist Device Development: Variability of Vascular Graft Geometry and Implications for Hemodynamics.

Patients born with a single functional ventricle typically undergo three-staged surgical palliation in the first years of life, with the last stage realizing a cross-like total cavopulmonary connection (TCPC) of superior and inferior vena cavas (SVC and IVC) with both left and right pulmonary arteries, allowing all deoxygenated blood to flow passively back to the lungs (Fontan circulation). Even though within the past decades more patients survive into adulthood, the connection comes at the prize of deficiencies such as chronic systemic venous hypertension and low cardiac output, which ultimately may lead to Fontan failure. Many studies have suggested that the TCPC's inherent insufficiencies might be addressed by adding a cavopulmonary assist device (CPAD) to provide the necessary pressure boost. While many device concepts are being explored, few take into account the complex cardiac anatomy typically associated with TCPCs. In this study, we focus on the extra cardiac conduit vascular graft connecting IVC and pulmonary arteries as one possible landing zone for a CPAD and describe its geometric variability in a cohort of 18 patients that had their TCPC realized with a 20mm vascular graft. We report traditional morphometric parameters and apply statistical shape modeling to determine the main contributors of graft shape variability. Such information may prove useful when designing CPADs that are adapted to the challenging anatomical boundaries in Fontan patients. We further compute the anatomical mean 3D graft shape (template graft) as a representative of key shape features of our cohort and prove this template graft to be a significantly better approximation of population and individual patient's hemodynamics than a commonly used simplified tube geometry. We therefore conclude that statistical shape modeling results can provide better models of geometric and hemodynamic boundary conditions associated with complex cardiac anatomy, which in turn may impact on improved cardiac device development.

Statistical shape modelling to aid surgical planning: associations between surgical parameters and head shapes following spring-assisted cranioplasty.

PURPOSE: Spring-assisted cranioplasty is performed to correct the long and narrow head shape of children with sagittal synostosis. Such corrective surgery involves osteotomies and the placement of spring-like distractors, which gradually expand to widen the skull until removal about 4 months later. Due to its dynamic nature, associations between surgical parameters and post-operative 3D head shape features are difficult to comprehend. The current study aimed at applying population-based statistical shape modelling to gain insight into how the choice of surgical parameters such as craniotomy size and spring positioning affects post-surgical head shape. METHODS: Twenty consecutive patients with sagittal synostosis who underwent spring-assisted cranioplasty at Great Ormond Street Hospital for Children (London, UK) were prospectively recruited. Using a nonparametric statistical modelling technique based on mathematical currents, a 3D head shape template was computed from surface head scans of sagittal patients after spring removal. Partial least squares (PLS) regression was employed to quantify and visualise trends of localised head shape changes associated with the surgical parameters recorded during spring insertion: anterior-posterior and lateral craniotomy dimensions, anterior spring position and distance between anterior and posterior springs. RESULTS: Bivariate correlations between surgical parameters and corresponding PLS shape vectors demonstrated that anterior-posterior (Pearson's [Formula: see text]) and lateral craniotomy dimensions (Spearman's [Formula: see text]), as well as the position of the anterior spring ([Formula: see text]) and the distance between both springs ([Formula: see text]) on average had significant effects on head shapes at the time of spring removal. Such effects were visualised on 3D models. CONCLUSIONS: Population-based analysis of 3D post-operative medical images via computational statistical modelling tools allowed for detection of novel associations between surgical parameters and head shape features achieved following spring-assisted cranioplasty. The techniques described here could be extended to other cranio-maxillofacial procedures in order to assess post-operative outcomes and ultimately facilitate surgical decision making.

Investigating Cardiac Motion Patterns Using Synthetic High-Resolution 3D Cardiovascular Magnetic Resonance Images and Statistical Shape Analysis.

Diagnosis of ventricular dysfunction in congenital heart disease is more and more based on medical imaging, which allows investigation of abnormal cardiac morphology and correlated abnormal function. Although analysis of 2D images represents the clinical standard, novel tools performing automatic processing of 3D images are becoming available, providing more detailed and comprehensive information than simple 2D morphometry. Among these, statistical shape analysis (SSA) allows a consistent and quantitative description of a population of complex shapes, as a way to detect novel biomarkers, ultimately improving diagnosis and pathology understanding. The aim of this study is to describe the implementation of a SSA method for the investigation of 3D left ventricular shape and motion patterns and to test it on a small sample of 4 congenital repaired aortic stenosis patients and 4 age-matched healthy volunteers to demonstrate its potential. The advantage of this method is the capability of analyzing subject-specific motion patterns separately from the individual morphology, visually and quantitatively, as a way to identify functional abnormalities related to both dynamics and shape. Specifically, we combined 3D, high-resolution whole heart data with 2D, temporal information provided by cine cardiovascular magnetic resonance images, and we used an SSA approach to analyze 3D motion per se. Preliminary results of this pilot study showed that using this method, some differences in end-diastolic and end-systolic ventricular shapes could be captured, but it was not possible to clearly separate the two cohorts based on shape information alone. However, further analyses on ventricular motion allowed to qualitatively identify differences between the two populations. Moreover, by describing shape and motion with a small number of principal components, this method offers a fully automated process to obtain visually intuitive and numerical information on cardiac shape and motion, which could be, once validated on a larger sample size, easily integrated into the clinical workflow. To conclude, in this preliminary work, we have implemented state-of-the-art automatic segmentation and SSA methods, and we have shown how they could improve our understanding of ventricular kinetics by visually and potentially quantitatively highlighting aspects that are usually not picked up by traditional approaches.

Quantifying the effect of corrective surgery for trigonocephaly: A non-invasive, non-ionizing method using three-dimensional handheld scanning and statistical shape modelling.

Trigonocephaly in patients with metopic synostosis is corrected by fronto-orbital remodelling (FOR). The aim of this study was to quantitatively assess aesthetic outcomes of FOR by capturing 3D forehead scans of metopic patients pre- and post-operatively and comparing them with controls. Ten single-suture metopic patients undergoing FOR and 15 age-matched non-craniosynostotic controls were recruited at Great Ormond Street Hospital for Children (UK). Scans were acquired with a three-dimensional (3D) handheld camera and post-processed combining 3D imaging software. 3D scans were first used for cephalometric measurements. Statistical shape modelling was then used to compute the 3D mean head shapes of the three groups (FOR pre-op, post-op and controls). Head shape variations were described via principal component analysis (PCA). Cephalometric measurements showed that FOR significantly increased the forehead volume and improved trigonocephaly. This improvement was supported visually by pre- and post-operative computed mean 3D shapes and numerically by PCA (p < 0.001). Compared with controls, post-operative scans showed flatter foreheads (p < 0.001). In conclusion, 3D scanning followed by 3D statistical shape modelling enabled the 3D comparison of forehead shapes of metopic patients and non-craniosynostotic controls, and demonstrated that the adopted FOR technique was successful in correcting bitemporal narrowing but overcorrected the rounding of the forehead.

Quantitative analysis of fetal facial morphology using 3D ultrasound and statistical shape modeling: a feasibility study.

BACKGROUND: The antenatal detection of facial dysmorphism using 3-dimensional ultrasound may raise the suspicion of an underlying genetic condition but infrequently leads to a definitive antenatal diagnosis. Despite advances in array and noninvasive prenatal testing, not all genetic conditions can be ascertained from such testing. OBJECTIVES: The aim of this study was to investigate the feasibility of quantitative assessment of fetal face features using prenatal 3-dimensional ultrasound volumes and statistical shape modeling. STUDY DESIGN: Thirteen normal and 7 abnormal stored 3-dimensional ultrasound fetal face volumes were analyzed, at a median gestation of 29+4 weeks (25+0 to 36+1). The 20 3-dimensional surface meshes generated were aligned and served as input for a statistical shape model, which computed the mean 3-dimensional face shape and 3-dimensional shape variations using principal component analysis. RESULTS: Ten shape modes explained more than 90% of the total shape variability in the population. While the first mode accounted for overall size differences, the second highlighted shape feature changes from an overall proportionate toward a more asymmetric face shape with a wide prominent forehead and an undersized, posteriorly positioned chin. Analysis of the Mahalanobis distance in principal component analysis shape space suggested differences between normal and abnormal fetuses (median and interquartile range distance values, 7.31 ± 5.54 for the normal group vs 13.27 ± 9.82 for the abnormal group) (P = .056). CONCLUSION: This feasibility study demonstrates that objective characterization and quantification of fetal facial morphology is possible from 3-dimensional ultrasound. This technique has the potential to assist in utero diagnosis, particularly of rare conditions in which facial dysmorphology is a feature.

Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches.

OBJECTIVE: Today's growing medical image databases call for novel processing tools to structure the bulk of data and extract clinically relevant information. Unsupervised hierarchical clustering may reveal clusters within anatomical shape data of patient populations as required for modern precision medicine strategies. Few studies have applied hierarchical clustering techniques to three-dimensional patient shape data and results depend heavily on the chosen clustering distance metrics and linkage functions. In this study, we sought to assess clustering classification performance of various distance/linkage combinations and of different types of input data to obtain clinically meaningful shape clusters. METHODS: We present a processing pipeline combining automatic segmentation, statistical shape modeling, and agglomerative hierarchical clustering to automatically subdivide a set of 60 aortic arch anatomical models into healthy controls, two groups affected by congenital heart disease, and their respective subgroups as defined by clinical diagnosis. Results were compared with traditional morphometrics and principal component analysis of shape features. RESULTS: Our pipeline achieved automatic division of input shape data according to primary clinical diagnosis with high F-score (0.902 ± 0.042) and Matthews correlation coefficient (0.851 ± 0.064) using the correlation/weighted distance/linkage combination. Meaningful subgroups within the three patient groups were obtained and benchmark scores for automatic segmentation and classification performance are reported. CONCLUSION: Clustering results vary depending on the distance/linkage combination used to divide the data. Yet, clinically relevant shape clusters and subgroups could be found with high specificity and low misclassification rates. SIGNIFICANCE: Detecting disease-specific clusters within medical image data could improve image-based risk assessment, treatment planning, and medical device development in complex disease.

How successful is successful? Aortic arch shape after successful aortic coarctation repair correlates with left ventricular function.

OBJECTIVES: Even after successful aortic coarctation repair, there remains a significant incidence of late systemic hypertension and other morbidities. Independently of residual obstruction, aortic arch morphology alone may affect cardiac function and outcome. We sought to uncover the relationship of arch 3-dimensional shape features with functional data obtained from cardiac magnetic resonance scans. METHODS: Three-dimensional aortic arch shape models of 53 patients (mean age, 22.3 ± 5.6 years) 12 to 38 years after aortic coarctation repair were reconstructed from cardiac magnetic resonance data. A novel validated statistical shape analysis method computed a 3-dimensional mean anatomic shape of all aortic arches and calculated deformation vectors of the mean shape toward each patient's arch anatomy. From these deformations, 3-dimensional shape features most related to left ventricular ejection fraction, indexed left ventricular end-diastolic volume, indexed left ventricular mass, and resting systolic blood pressure were extracted from the deformation vectors via partial least-squares regression. RESULTS: Distinct arch shape features correlated significantly with left ventricular ejection fraction (r = 0.42, P = .024), indexed left ventricular end-diastolic volume (r = 0.65, P < .001), and indexed left ventricular mass (r = 0.44, P = .014). Lower left ventricular ejection fraction, larger indexed left ventricular end-diastolic volume, and increased indexed left ventricular mass were identified with an aortic arch shape that has an elongated ascending aorta with a high arch height-to-width ratio, a relatively short proximal transverse arch, and a relatively dilated descending aorta. High blood pressure seemed to be linked to gothic arch shape features, but this did not achieve statistical significance. CONCLUSIONS: Independently of hemodynamically important arch obstruction or residual aortic coarctation, specific aortic arch shape features late after successful aortic coarctation repair seem to be associated with worse left ventricular function. Analyzing 3-dimensional shape information via statistical shape modeling can be an adjunct to long-term risk assessment in patients after aortic coarctation repair.

Computational modelling for congenital heart disease: how far are we from clinical translation?

Computational models of congenital heart disease (CHD) have become increasingly sophisticated over the last 20 years. They can provide an insight into complex flow phenomena, allow for testing devices into patient-specific anatomies (pre-CHD or post-CHD repair) and generate predictive data. This has been applied to different CHD scenarios, including patients with single ventricle, tetralogy of Fallot, aortic coarctation and transposition of the great arteries. Patient-specific simulations have been shown to be informative for preprocedural planning in complex cases, allowing for virtual stent deployment. Novel techniques such as statistical shape modelling can further aid in the morphological assessment of CHD, risk stratification of patients and possible identification of new 'shape biomarkers'. Cardiovascular statistical shape models can provide valuable insights into phenomena such as ventricular growth in tetralogy of Fallot, or morphological aortic arch differences in repaired coarctation. In a constant move towards more realistic simulations, models can also account for multiscale phenomena (eg, thrombus formation) and importantly include measures of uncertainty (ie, CIs around simulation results). While their potential to aid understanding of CHD, surgical/procedural decision-making and personalisation of treatments is undeniable, important elements are still lacking prior to clinical translation of computational models in the field of CHD, that is, large validation studies, cost-effectiveness evaluation and establishing possible improvements in patient outcomes.

Looks Do Matter! Aortic Arch Shape After Hypoplastic Left Heart Syndrome Palliation Correlates With Cavopulmonary Outcomes.

BACKGROUND: Aortic arch reconstruction after hypoplastic left heart syndrome (HLHS) palliation can vary widely in shape and dimensions between patients. Arch morphology alone may affect cardiac function and outcome. We sought to uncover the relationship of arch three-dimensional shape features with functional and short-term outcome data after total cavopulmonary connection (TCPC). METHODS: Aortic arch shape models of 37 patients with HLHS (age, 2.89 ± 0.99 years) were reconstructed from magnetic resonance data before TCPC completion. A novel, validated statistical shape analysis method was used to compute a three-dimensional anatomic mean shape from the cohort and calculate the deformation vectors of the mean shape toward each patient's specific anatomy. From these deformations, three-dimensional shape features most related to ventricular ejection fraction, indexed end-diastolic volume, and superior cavopulmonary pressure were extracted by partial least-square regression analysis. Shape patterns relating to intensive care unit and hospital lengths of stay after TCPC were assessed. RESULTS: Distinct deformation patterns, which result in an acutely mismatched aortic root and ascending aorta, and a gothic-like transverse arch, correlated with increased indexed end-diastolic volume and higher superior cavopulmonary pressure but not with ejection fraction. Specific arch morphology with pronounced transverse arch and descending aorta mismatch also correlated with longer intensive care unit and hospital lengths of stay after TCPC completion. CONCLUSIONS: Independent of hemodynamically important arch obstruction, altered aortic morphology in HLHS patients appears to have important associations with higher superior cavopulmonary pressure and with short-term outcomes after TCPC completion as highlighted by statistical shape analysis, which could act as adjunct to risk assessment in HLHS.

Three-Dimensional Handheld Scanning to Quantify Head-Shape Changes in Spring-Assisted Surgery for Sagittal Craniosynostosis.

Three-dimensional (3D) imaging is an important tool for diagnostics, surgical planning, and evaluation of surgical outcomes in craniofacial procedures. Gold standard for acquiring 3D imaging is computed tomography that entails ionizing radiations and, in young children, a general anaesthesia. Three-dimensional photographic imaging is an alternative method to assess patients who have undergone calvarial reconstructive surgery. The aim of this study was to assess the utility of 3D handheld scanning photography in a cohort of patients who underwent spring-assisted correction surgery for scaphocephaly. Pre- and postoperative 3D scans acquired in theater and at the 3-week follow-up in clinic were postprocessed for 9 patients. Cephalic index (CI), head circumference, volume, sagittal length, and coronal width over the head at pre-op, post-op, and follow-up were measured from the 3D scans. Cephalic index from 3D scans was compared with measurements from planar x-rays. Statistical shape modeling (SSM) was used to calculate the 3D mean anatomical head shape of the 9 patients at the pre-op, post-op, and follow-up. No significant differences were observed in the CI between 3D and x-ray. Cephalic index, volume, and coronal width increased significantly over time. Mean shapes from SSM visualized the overall and regional 3D changes due to the expansion of the springs in situ. Three-dimensional handheld scanning followed by SSM proved to be an efficacious and practical method to evaluate 3D shape outcomes after spring-assisted cranioplasty in individual patients and the population.

A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta.

BACKGROUND: Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. METHODS: Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. RESULTS: The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. CONCLUSIONS: The suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.