Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

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

Investigating left ventricular infarct extension after myocardial infarction using cardiac imaging and patient-specific modelling

Acute myocardial infarction (MI) is one of the leading causes of death worldwide that commonly affects the left ventricle (LV). Following MI, the LV mechanical loading is altered and may undergo a maladaptive compensatory mechanism that progressively leads to adverse LV remodelling and then heart failure. One of the remodelling processes is the infarct extension which involves necrosis of healthy myocardium in the border zone (BZ), progressively enlarging the infarct zone (IZ) and recruiting the remote zone (RZ) into the BZ. The mechanisms underlying infarct extension remain unclear, but myocyte stretching has been suggested as the most likely cause. A recent personalized LV modelling work found that infarct extension was correlated to inadequate diastolic fibre stretch and higher infarct stiffness. However, other possible factors of infarct extension may not have been elucidated in this work due to the limited number of myocardial locations analysed at the subendocardium only. Using human patient-specific left- ventricular (LV) models established from cardiac magnetic resonance imaging (MRI) of 6 MI patients, the correlation between infarct extension and regional mechanics impairment was studied. Prior to the modelling, a 2D-4D registration-cum-segmentation framework for the delineation of LV in late gadolinium enhanced (LGE) MRI was first developed, which is a pre-requisite for infarct scar quantification and localization in patient-specific 3D LV models. This framework automatically corrects for motion artifacts in multimodal MRI scans, resolving the issue of inaccurate infarct mapping and geometry reconstruction which is typically done manually in most patient-specific modelling work. The registration framework was evaluated against cardiac MRI data from 27 MI patients and showed high accuracy and robustness in delineating LV in LGE MRI of various quality and different myocardial features. This framework allows the integration of LV data from both LGE and cine scans and to facilitate the reconstruction of accurate 3D LV and infarct geometries for subsequent computational study. In the patient-specific LV mechanical modelling, the LV mechanics were formulated using a quasi-static and nearly incompressible hyperelastic material law with transversely isotropic behaviour. The patient-specific models were incorporated with realistic fibre orientation and excitable contracting myocardium. Optimisation of passive and active material parameters were done by minimizing the myocardial wall distance between the reference and end-diastole/end-systole LV geometries. Full cardiac cycle of the LV models was then simulated and stress/strain data were extracted to determine the correlation between regional mechanics abnormality and infarct extension. The fibre stress-strain loops (FSSLs) were analysed and its abnormality was characterized using the directional regional external work (DREW) index, which measures FSSL area and loop direction. Sensitivity studies were also performed to investigate the effect of infarct stiffness on regional myocardial mechanics and potential for infarct extension. It was found that infarct extension was correlated to severely abnormal FSSL in the form of counter-clockwise loop, as indicated by negative DREW values. In regions demonstrating negative DREW values, substantial isovolumic relaxation (IVR) fibre stretching was observed. Further analysis revealed that the occurrence of severely abnormal FSSL near the RZ-BZ boundary was due to a large amount of surrounding infarcted tissue that worsen with excessively stiff IZ