Association of central obesity with unique cardiac remodelling in young adults born small for gestational age

Being born small for gestational age (SGA, 10% of all births) is associated with increased risk of cardiovascular mortality in adulthood together with lower exercise tolerance, but mechanistic pathways are unclear. Central obesity is known to worsen cardiovascular outcomes, but it is uncertain how it affects the heart in adults born SGA. We aimed to assess whether central obesity makes young adults born SGA more susceptible to cardiac remodelling and dysfunction.A perinatal cohort from a tertiary university hospital in Spain of young adults (30-40 years) randomly selected, 80 born SGA (birth weight below 10th centile) and 75 with normal birth weight (controls) was recruited. We studied the associations between SGA and central obesity (measured via the hip-to-waist ratio and used as a continuous variable) and cardiac regional structure and function, assessed by cardiac magnetic resonance using statistical shape analysis. Both SGA and waist-to-hip were highly associated to cardiac shape (F = 3.94, P < 0.001; F = 5.18, P < 0.001 respectively) with a statistically significant interaction (F = 2.29, P = 0.02). While controls tend to increase left ventricular end-diastolic volumes, mass and stroke volume with increasing waist-to-hip ratio, young adults born SGA showed a unique response with inability to increase cardiac dimensions or mass resulting in reduced stroke volume and exercise capacity.SGA young adults show a unique cardiac adaptation to central obesity. These results support considering SGA as a risk factor that may benefit from preventive strategies to reduce cardiometabolic risk.© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: [email protected]

Le coeur numérique personnalisé

International audienceDuring the last past years, significant progress in Medical Image Analysis, in biomathematics and biophysics have led to development of the first personalized digital cardiac models. These digital models are personalized which means they can reproduce the anatomy and physiology of specific patients. They allow the quantitative analysis of the organ function and the simulation of some therapies to evaluate their expected benefit. In this article we describe some recent research work done on these topics in our project team Asclepios at Inria, in collaboration with other Inria teams (Macs, Reo, Sisyphe) and external academic, clinical and industrial partners. If a number of challenges in mathematics and informatics still have to be solved before such personalized digital cardiac models can be used in current clinical practice, these first results announce a new generation of tools in digital medicine which can contribute more widely to a more preventive and more predictive personalized medicine.Au cours de ces dernières années, des progrès importants dans l'analyse informatique des images médicales et dans la modélisation biomathématique et biophysique ont rendu possible la construction des premiers modèles numériques et personnalisés du cœur humain. Ces modèles informatiques sont personnalisés car ils reproduisent l'anatomie et la physiologie de patients spécifiques. Ils permettent d'analyser et de quantifier le fonctionnement de l'organe et de simuler certainesthérapies pour en évaluer le bénéfice espéré. Dans cet article nous décrivons des travaux de recherche récents réalisés sur ce thème au sein de l'équipe projet Asclepios à l'Inria, en collaboration avec d'autres équipes Inria (Macs, Reo, Sisyphe) et des partenaires extérieurs académiques, cliniques et industriels. Si de grands défis en modélisation informatique et mathématique doivent encore être relevés avant une utilisation clinique courante du cœur numérique personnalisé, ces premiers résultats annoncent une nouvelle génération d'outils de médecine numérique pouvant contribuer plus largement à une médecine personnalisée plus préventive et plus prédictive

A framework combining window width-level adjustment and Gaussian filter-based multi-resolution for automatic whole heart segmentation

Heart diseases are prevalent among the general population. These diseases can be diagnosed in their early stages through a quantitative evaluation of cardiac functions. In a typical procedure, heart segmentation is initially performed. Quantitative information is then obtained from a 3D reconstructed image of the heart. However, manual segmentation is time-consuming and prone to inter- and intra-observer variations. As such, automatic methods must be developed to assess cardiac functions quantitatively. In this study, an automatic algorithm for whole heart segmentation was established through window width-level adjustment and Gaussian filter-based multi-resolution methods. The proposed algorithm preprocesses the image by adjusting the window width and the centre to acquire cardiac images with clear anatomical structures. The cardiac image is then decomposed into several resolution layers by using a Gaussian filter to eliminate discontinuity associated with traditional pyramid down-sampling and decomposition. A registration-based segmentation algorithm is applied to the cardiac image. The proposed segmentation algorithm was validated with a clinical dataset of 14 cardiac dual-source computed tomography images. Results show that the proposed methods improve the registration accuracy of the epicardium and the endocardium. The volume of the manual segmentation standard is not significantly different from that of the proposed segmentation and the accuracy of the method reaches almost 1 mm in most areas. Thus, the proposed method can be used to perform a high-precision segmentation of the whole heart

A 5D computational phantom for pharmacokinetic simulation studies in dynamic emission tomography

Introduction: Dynamic image acquisition protocols are increasingly used in emission tomography for drug development and clinical research. As such, there is a need for computational phantoms to accurately describe both the spatial and temporal distribution of radiotracers, also accounting for periodic and non-periodic physiological processes occurring during data acquisition. Methods: A new 5D anthropomorphic digital phantom was developed based on a generic simulation platform, for accurate parametric imaging simulation studies in emission tomography. The phantom is based on high spatial and temporal information derived from real 4D MR data and a detailed multi-compartmental pharmacokinetic modelling simulator. Results: The proposed phantom is comprised of three spatial and two temporal dimensions, including periodic physiological processes due to respiratory motion and non-periodic functional processes due to tracer kinetics. Example applications are shown in parametric [18F]FDG and [15O]H2O PET imaging, successfully generating realistic macro- and micro-parametric maps. Conclusions: The envisaged applications of this digital phantom include the development and evaluation of motion correction and 4D image reconstruction algorithms in PET and SPECT, development of protocols and methods for tracer and drug development as well as new pharmacokinetic parameter estimation algorithms, amongst others. Although the simulation platform is primarily developed for generating dynamic phantoms for emission tomography studies, it can easily be extended to accommodate dynamic MR and CT imaging simulation protocols

Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model

International audienceThe objective of this paper is to propose and assess an estimation procedure - based on data assimilation principles - well-suited to obtain some regional values of key biophysical parameters in a beating heart model, using actual Cine-MR images. The motivation is twofold: (1) to provide an automatic tool for personalizing the characteristics of a cardiac model in order to achieve predictivity in patient-specific modeling, and (2) to obtain some useful information for diagnosis purposes in the estimated quantities themselves. In order to assess the global methodology we specifically devised an animal experiment in which a controlled infarct was produced and data acquired before and after infarction, with an estimation of regional tissue contractility - a key parameter directly affected by the pathology - performed for every measured stage. After performing a preliminary assessment of our proposed methodology using synthetic data, we then demonstrate a full-scale application by first estimating contractility values associated with 6 regions based on the AHA subdivision, before running a more detailed estimation using the actual AHA segments. The estimation results are assessed by comparison with the medical knowledge of the specific infarct, and with late enhancement MR images. We discuss their accuracy at the various subdivision levels, in the light of the inherent modeling limitations and of the intrinsic information contents featured in the data

Automatic whole heart segmentation based on image registration

Whole heart segmentation can provide important morphological information of the heart, potentially enabling the development of new clinical applications and the planning and guidance of cardiac interventional procedures. This information can be extracted from medical images, such as these of magnetic resonance imaging (MRI), which is becoming a routine modality for the determination of cardiac morphology. Since manual delineation is labour intensive and subject to observer variation, it is highly desirable to develop an automatic method. However, automating the process is complicated by the large shape variation of the heart and limited quality of the data. The aim of this work is to develop an automatic and robust segmentation framework from cardiac MRI while overcoming these difficulties. The main challenge of this segmentation is initialisation of the substructures and inclusion of shape constraints. We propose the locally affine registration method (LARM) and the freeform deformations with adaptive control point status to tackle the challenge. They are applied to the atlas propagation based segmentation framework, where the multi-stage scheme is used to hierarchically increase the degree of freedom. In this segmentation framework, it is also needed to compute the inverse transformation for the LARM registration. Therefore, we propose a generic method, using Dynamic Resampling And distance Weighted interpolation (DRAW), for inverting dense displacements. The segmentation framework is validated on a clinical dataset which includes nine pathologies. To further improve the nonrigid registration against local intensity distortions in the images, we propose a generalised spatial information encoding scheme and the spatial information encoded mutual information (SIEMI) registration. SIEMI registration is applied to the segmentation framework to improve the accuracy. Furthermore, to demonstrate the general applicability of SIEMI registration, we apply it to the registration of cardiac MRI, brain MRI, and the contrast enhanced MRI of the liver. SIEMI registration is shown to perform well and achieve significantly better accuracy compared to the registration using normalised mutual information