Evaluation of knowledge-based reconstruction for magnetic resonance volumetry of the right ventricle after arterial switch operation for dextro-transposition of the great arteries

Nyns, Emile C.A.; Dragulescu, Andréea; Yoo, Shi Joon; Grosse-Wortmann, Lars

doi:10.1007/s10554-016-0921-1

Cited by 9 publications

(13 citation statements)

References 26 publications

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“…In one method known as knowledge-based reconstruction, the patient customization is performed using a linear combination of database templates [45]. Using a database of ~60 sub-systemic RV geometries, this method provides accurate and rapid analysis of right ventricular volumes in patients with tetralogy of Fallot, and after arterial switch operation for dextro-transposition of the great arteries [46,47]. In patients with transposition of the great arteries after atrial switch, the right ventricle was dilated and more spherical than controls, with reduced basal regional function relative to patients with congenitally corrected transposition of the great arteries [48].…”

Section: Overview Of Methods For Atlas Constructionmentioning

confidence: 99%

Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease

Gilbert

Forsch

Hegde

et al. 2018

J. of Cardiovasc. Trans. Res.

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Approximately 1% of all babies are born with some form of congenital heart defect. Many serious forms of CHD can now be surgically corrected after birth, which has led to improved survival into adulthood. However, many patients require serial monitoring to evaluate progression of heart failure and determine timing of interventions. Accurate multidimensional quantification of regional heart shape and function is required for characterizing these patients. A computational atlas of single ventricle and biventricular heart shape and function enables quantification of remodeling in terms of z scores in relation to specific reference populations. Progression of disease can then be monitored effectively by longitudinal evaluation of z scores. A biomechanical analysis of cardiac function in relation to population variation enables investigation of the underlying mechanisms for developing pathology. Here, we summarize recent progress in this field, with examples in single ventricle and biventricular congenital pathologies.

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Section: Overview Of Methods For Atlas Constructionmentioning

confidence: 99%

Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease

Gilbert

Forsch

Hegde

et al. 2018

J. of Cardiovasc. Trans. Res.

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“…The KBR method has been evaluated with good results to quantify RV volumes from sequential 2DE datasets for healthy right ventricles. Also, various pathologies such as Tetralogy of Fallot, ventricular septal defects, transposition of great arteries after palliative surgery and pulmonary hypertension have been analysed with the KBR method (16)(17)(18)(19)(20)(36)(37)(38). In combination with RT3DE it also compares favourably to other RT3DE software, usually based on semiautomatic border tracing in accuracy and reproducibility (15,39).…”

Section: Knowledge Based Reconstructionmentioning

confidence: 99%

Real-time 3D-echocardiography of the right ventricle—paediatric reference values for right ventricular volumes using knowledge-based reconstruction: a multicentre study

Herberg¹,

Smit²,

Winkler³

et al. 2021

Quant Imaging Med Surg

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Background: Real-time 3D echocardiography is a promising method for non-invasive assessment of right ventricular performance in children with congenital heart disease. Volume quantification using knowledgebased reconstruction (KBR) enables the calculation of right ventricular dimensions by matching endocardial landmarks with a reference library of right ventricular shapes. However, paediatric reference values for volumes based on KBR are missing. Aim of this study was to establish reference values for right ventricular volumes in a large paediatric population using 3D echocardiography and KBR.Methods: In a multicentre prospective-design study, 545 healthy children and adolescents (age range, 1 day to 216 months) underwent 3D echocardiography of the right ventricle using two different vendors (iE33, Philips or Vivid 7, GE). Volume analysis was performed by a semiautomatic quantification software (VMS, Ventripoint Diagnostics Ltd., Washington, US). Reference centiles were computed using Cole's LMS method and the gamlss package in R. For vendor comparison, 3D datasets were recorded subsequently in 20 subjects using both ultrasound devices.Results: 3D datasets of 406/545 (74.5%) subjects provided an adequate image quality. Right ventricular volumes had a significant association with age, body size and sex. We created sex-specific percentiles indexed to body surface area (BSA). Intra-and interobserver-variation for all volume calculations were excellent with intraclass correlation coefficients (ICCs) between 0.973-0.998. Agreement of both vendors showed slightly higher end-diastolic and stroke volumes (bias ± standard deviation 2.2%±6.8% respectively 4.5%±8.1%) and smaller end-systolic volumes (−0.9±10.3%) using Philips datasets.Conclusions: Calculation of ventricular volumes by KBR allows reliable non-invasive assessment of right ventricular volumes with excellent intra-and interobserver variations. The calculated percentiles based on a large paediatric population serve as a reference and may facilitate the use of real-time 3D echocardiography for the analysis of right ventricular size and function.

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“…The algorithm will reconstruct the requested anatomy from a new patient based on data out of the database. 30 Arash-band method…”

Section: Knowledgebasedmentioning

confidence: 99%

Artificial Intelligence in Patients with Congenital Heart Disease: Where Do We Stand?

Pool¹,

Kauw

Bleijendaal³

et al. 2021

EMJ Cardiol

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Life expectancy of patients with congenital heart disease (CHD) has increased in recent decades; however, late complications remain frequent and difficult to predict. Progress in data science has spurred the development of decision support systems and could aid physicians in predicting clinical deterioration and in the management of CHD patients. Newly developed artificial intelligence (AI) algorithms have shown performances comparable to humans in clinical diagnostics using statistical and computational algorithms and are expected to partly surpass human intelligence in the near future. Although much research on AI has been performed in patients with acquired heart disease, little data is available with respect to research on AI in patients with CHD. Learning algorithms in patients with CHD have shown to be promising in the interpretation of ECG, cardiac imaging, and the prediction of surgical outcome. However, current learning algorithms are not accurate enough to be implemented into daily clinical practice. Data on AI possibilities remain scarce in patients with CHD, and studies on large data sets are warranted to increase sensitivity, specificity, accuracy, and clinical relevance of these algorithms.

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Evaluation of knowledge-based reconstruction for magnetic resonance volumetry of the right ventricle after arterial switch operation for dextro-transposition of the great arteries

Cited by 9 publications

References 26 publications

Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease

Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease

Real-time 3D-echocardiography of the right ventricle—paediatric reference values for right ventricular volumes using knowledge-based reconstruction: a multicentre study

Artificial Intelligence in Patients with Congenital Heart Disease: Where Do We Stand?

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