Shape and function in congenital heart disease: a translational study using image, statistical and computational analyses

While medical image analysis techniques are becoming technically more advanced, analysis of shape and structure in clinical practice is often limited to two-dimensional morphometry, neglecting potentially crucial three-dimensional (3D) anatomical information provided by the original images. This thesis aims at closing this gap by combining state-of-the-art medical image analysis, engineering and data analysis tools to elucidate relationships between 3D shape features and clinically relevant functional outcomes. In particular, patient cohorts affected by congenital heart disease were studied since shape and structure of the heart and its components are crucial for diagnosis, therapy and management of those patients. At first, a statistical shape model was coupled with partial least squares regression to extract anatomical 3D shape biomarkers related to clinical parameters from cardiovascular magnetic resonance image data. After establishing a step-by-step protocol to guide the user with respect to parameter selection, results were shown to be in accordance with traditional morphometry as well as with clinical expert opinion. Novel aortic arch shape biomarkers relating to cardiac functional parameters were found in a cohort of patients post aortic coarctation repair (CoA). By combining statistical shape modelling results with computational fluid dynamics simulations, a mechanistic basis for the observed results was provided. Methods were then extended towards a hierarchical shape clustering framework, which achieved good unsupervised classification performance in a population of healthy and pathological aortic arch shapes. Applied to a cohort of CoA patients, previously unknown anatomical patterns were discovered. This thesis demonstrates that combining medical image analysis and engineering tools with data mining and statistics provides a powerful platform to detect novel shape biomarkers and patient sub-groups. Results may ultimately improve risk-stratification, treatment-planning and medical device development, thereby promoting translation of advanced computational analysis techniques into clinical practice

Investigating Cardiac Motion Patters 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

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

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 modelling 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/pm0.042) and Matthews Correlation Coefficient (0.851/pm0.064) 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

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

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