A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?

Bruse, Jan L.; McLeod, Kristin; Biglino, Giovanni; Ntsinjana, Hopewell; Capelli, Claudio; Hsia, Tain‐Yen; Sermesant, Maxime; Pennec, Xavier; Schievano, Silvia

doi:10.1007/978-3-319-28712-6_3

Cited by 14 publications

(9 citation statements)

References 13 publications

(18 reference statements)

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“…Our pipeline allows using the full image data provided by clinical CMR acquisition, and thus provides more insight than each image set by itself. Shape analysis has been shown in the literature to be a promising technique for differentiating healthy from pathological subjects, and for quantitatively classifying and describing anatomical shapes ( 9 – 12 , 29 , 30 ). When applied to ED and ES ventricles in our example population, shape analysis allowed us to observe and quantify differences between AS and controls for single cases.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…In order to visualise dominant 3D fetal face features as derived by PCA, shape modes were visualised in ParaView (31) as deformations of the computed template shape along the mode, from -3 standard deviations (SD) to +3SD around the mean shape. Furthermore, shape modes were numerically described by so-called "shape vectors" {fi,k}k=1…m (23,30,32) with each shape vector entry quantifying how much of the shape features described by the respective shape mode is contained within a subject's shape.…”

Section: Statistical Shape Modelling and Principal Component Analysismentioning

confidence: 99%

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

Dall’Asta

Schievano

Bruse

et al. 2017

American Journal of Obstetrics and Gynecology

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“… 37 Regional LV shape differences have been identified between healthy subjects and patients postarterial switch operation. 38 Interestingly, SSM results were found to be associated with clinical expert shape assessment of aortic arch morphology in patients with repaired CoA, 39 suggesting the potential for clinical decision support and diagnosis systems.…”

Section: Statistical Shape Modellingmentioning

confidence: 99%

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

Biglino

Capelli

Bruse

et al. 2016

Heart

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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.

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A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?

Cited by 14 publications

References 13 publications

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

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

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

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

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