Tommaso Mansi

Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation.

Cardiac resynchronisation therapy (CRT) is an effective treatment for patients with congestive heart failure and a wide QRS complex. However, up to 30% of patients are non-responders to therapy in terms of exercise capacity or left ventricular reverse remodelling. A number of controversies still remain surrounding patient selection, targeted lead implantation and optimisation of this important treatment. The development of biophysical models to predict the response to CRT represents a potential strategy to address these issues. In this article, we present how the personalisation of an electromechanical model of the myocardium can predict the acute haemodynamic changes associated with CRT. In order to introduce such an approach as a clinical application, we needed to design models that can be individualised from images and electrophysiological mapping of the left ventricle. In this paper the personalisation of the anatomy, the electrophysiology, the kinematics and the mechanics are described. The acute effects of pacing on pressure development were predicted with the in silico model for several pacing conditions on two patients, achieving good agreement with invasive haemodynamic measurements: the mean error on dP/dt(max) is 47.5±35mmHgs(-1), less than 5% error. These promising results demonstrate the potential of physiological models personalised from images and electrophysiology signals to improve patient selection and plan CRT.

iLogDemons: A Demons-Based Registration Algorithm for Tracking Incompressible Elastic Biological Tissues

Tracking soft tissues in medical images using non-linear image registration algorithms requires methods that are fast and provide spatial transformations consistent with the biological characteristics of the tissues. LogDemons algorithm is a fast non-linear registration method that computes diffeomorphic transformations parameterised by stationary velocity fields. Although computationally efficient, its use for tissue tracking has been limited because of its ad-hoc Gaussian regularisation, which hampers the implementation of more biologically motivated regularisations. In this work, we improve the logDemons by integrating elasticity and incompressibility for soft-tissue tracking. To that end, a mathematical justification of demons Gaussian regularisation is proposed. Building on this result, we replace the Gaussian smoothing by an efficient elastic-like regulariser based on isotropic differential quadratic forms of vector fields. The registration energy functional is finally minimised under the divergence-free constraint to get incompressible deformations. As the elastic regulariser and the constraint are linear, the method remains computationally tractable and easy to implement. Tests on synthetic incompressible deformations showed that our approach outperforms the original logDemons in terms of elastic incompressible deformation recovery without reducing the image matching accuracy. As an application, we applied the proposed algorithm to estimate 3D myocardium strain on clinical cine MRI of two adult patients. Results showed that incompressibility constraint improves the cardiac motion recovery when compared to the ground truth provided by 3D tagged MRI.

Benchmarking framework for myocardial tracking and deformation algorithms: An open access database

In this paper we present a benchmarking framework for the validation of cardiac motion analysis algorithms. The reported methods are the response to an open challenge that was issued to the medical imaging community through a MICCAI workshop. The database included magnetic resonance (MR) and 3D ultrasound (3DUS) datasets from a dynamic phantom and 15 healthy volunteers. Participants processed 3D tagged MR datasets (3DTAG), cine steady state free precession MR datasets (SSFP) and 3DUS datasets, amounting to 1158 image volumes. Ground-truth for motion tracking was based on 12 landmarks (4 walls at 3 ventricular levels). They were manually tracked by two observers in the 3DTAG data over the whole cardiac cycle, using an in-house application with 4D visualization capabilities. The median of the inter-observer variability was computed for the phantom dataset (0.77 mm) and for the volunteer datasets (0.84 mm). The ground-truth was registered to 3DUS coordinates using a point based similarity transform. Four institutions responded to the challenge by providing motion estimates for the data: Fraunhofer MEVIS (MEVIS), Bremen, Germany; Imperial College London - University College London (IUCL), UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Inria-Asclepios project (INRIA), France. Details on the implementation and evaluation of the four methodologies are presented in this manuscript. The manually tracked landmarks were used to evaluate tracking accuracy of all methodologies. For 3DTAG, median values were computed over all time frames for the phantom dataset (MEVIS=1.20mm, IUCL=0.73 mm, UPF=1.10mm, INRIA=1.09 mm) and for the volunteer datasets (MEVIS=1.33 mm, IUCL=1.52 mm, UPF=1.09 mm, INRIA=1.32 mm). For 3DUS, median values were computed at end diastole and end systole for the phantom dataset (MEVIS=4.40 mm, UPF=3.48 mm, INRIA=4.78 mm) and for the volunteer datasets (MEVIS=3.51 mm, UPF=3.71 mm, INRIA=4.07 mm). For SSFP, median values were computed at end diastole and end systole for the phantom dataset(UPF=6.18 mm, INRIA=3.93 mm) and for the volunteer datasets (UPF=3.09 mm, INRIA=4.78 mm). Finally, strain curves were generated and qualitatively compared. Good agreement was found between the different modalities and methodologies, except for radial strain that showed a high variability in cases of lower image quality.

An integrated framework for finite-element modeling of mitral valve biomechanics from medical images: Application to MitralClip intervention planning

Treatment of mitral valve (MV) diseases requires comprehensive clinical evaluation and therapy personalization to optimize outcomes. Finite-element models (FEMs) of MV physiology have been proposed to study the biomechanical impact of MV repair, but their translation into the clinics remains challenging. As a step towards this goal, we present an integrated framework for finite-element modeling of the MV closure based on patient-specific anatomies and boundary conditions. Starting from temporal medical images, we estimate a comprehensive model of the MV apparatus dynamics, including papillary tips, using a machine-learning approach. A detailed model of the open MV at end-diastole is then computed, which is finally closed according to a FEM of MV biomechanics. The motion of the mitral annulus and papillary tips are constrained from the image data for increased accuracy. A sensitivity analysis of our system shows that chordae rest length and boundary conditions have a significant influence upon the simulation results. We quantitatively test the generalization of our framework on 25 consecutive patients. Comparisons between the simulated closed valve and ground truth show encouraging results (average point-to-mesh distance: 1.49 ± 0.62 mm) but also the need for personalization of tissue properties, as illustrated in three patients. Finally, the predictive power of our model is tested on one patient who underwent MitralClip by comparing the simulated intervention with the real outcome in terms of MV closure, yielding promising prediction. By providing an integrated way to perform MV simulation, our framework may constitute a surrogate tool for model validation and therapy planning.

A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot

Cardiac remodelling plays a crucial role in heart diseases. Analyzing how the heart grows and remodels over time can provide precious insights into pathological mechanisms, eventually resulting in quantitative metrics for disease evaluation and therapy planning. This study aims to quantify the regional impacts of valve regurgitation and heart growth upon the end-diastolic right ventricle (RV) in patients with tetralogy of Fallot, a severe congenital heart defect. The ultimate goal is to determine, among clinical variables, predictors for the RV shape from which a statistical model that predicts RV remodelling is built. Our approach relies on a forward model based on currents and a diffeomorphic surface registration algorithm to estimate an unbiased template. Local effects of RV regurgitation upon the RV shape were assessed with Principal Component Analysis (PCA) and cross-sectional multivariate design. A generative 3-D model of RV growth was then estimated using partial least squares (PLS) and canonical correlation analysis (CCA). Applied on a retrospective population of 49 patients, cross-effects between growth and pathology could be identified. Qualitatively, the statistical findings were found realistic by cardiologists. 10-fold cross-validation demonstrated a promising generalization and stability of the growth model. Compared to PCA regression, PLS was more compact, more precise and provided better predictions.

Toward Patient-Specific Myocardial Models of the Heart

This article presents a framework for building patient-specific models of the myocardium, to help diagnosis, therapy planning, and procedure guidance. The aim is to be able to introduce such models in clinical applications. Thus, there is a need to design models that can be adjusted from clinical data, images, or signals, which are sparse and noisy. The authors describe the three main components of a myocardial model: the anatomy, the electrophysiology, and the biomechanics. For each of these components, the authors try to obtain the best balance between prior knowledge and observable parameters to be able to adjust these models to patient data. To achieve this, there is a need to design models with the right level of complexity and a computational cost compatible with clinical constraints.

Personalised Electromechanical Model of the Heart for the Prediction of the Acute Effects of Cardiac Resynchronisation Therapy

Cardiac resynchronisation therapy (CRT) has been shown to be an effective adjunctive treatment for patients with dyssynchronous ventricular contraction and symptoms of the heart failure. However, clinical trials have also demonstrated that up to 30% of patients may be classified as non-responders. In this article, we present how the personalisation of an electromechanical model of the myocardium could help the therapy planning for CRT. We describe the four main components of our myocardial model, namely the anatomy, the electrophysiology, the kinematics and the mechanics. For each of these components we combine prior knowledge and observable parameters in order to personalise these models to patient data. Then the acute effects of a pacemaker on the cardiac function are predicted with the in silico model on a clinical case. This is a proof of concept of the potential of virtual physiological models to better select and plan the therapy.

An Artificial Agent for Anatomical Landmark Detection in Medical Images

Fast and robust detection of anatomical structures or pathologies represents a fundamental task in medical image analysis. Most of the current solutions are however suboptimal and unconstrained by learning an appearance model and exhaustively scanning the space of parameters to detect a specific anatomical structure. In addition, typical feature computation or estimation of meta-parameters related to the appearance model or the search strategy, is based on local criteria or predefined approximation schemes. We propose a new learning method following a fundamentally different paradigm by simultaneously modeling both the object appearance and the parameter search strategy as a unified behavioral task for an artificial agent. The method combines the advantages of behavior learning achieved through reinforcement learning with effective hierarchical feature extraction achieved through deep learning. We show that given only a sequence of annotated images, the agent can automatically and strategically learn optimal paths that converge to the sought anatomical landmark location as opposed to exhaustively scanning the entire solution space. The method significantly outperforms state-of-the-art machine learning and deep learning approaches both in terms of accuracy and speed on 2D magnetic resonance images, 2D ultrasound and 3D CT images, achieving average detection errors of 1-2 pixels, while also recognizing the absence of an object from the image.

Correspondence Between Simple 3-D MRI-Based Computer Models and In-Vivo EP Measurements in Swine With Chronic Infarctions

The aim of this paper was to compare several in-vivo electrophysiological (EP) characteristics measured in a swine model of chronic infarct, with those predicted by simple 3-D MRI-based computer models built from ex-vivo scans (voxel size <;1 mm3). Specifically, we recorded electroanatomical voltage maps (EAVM) in six animals, and ECG waves during induction of arrhythmia in two of these cases. The infarct heterogeneities (dense scar and border zone) as well as fiber directions were estimated using diffusion weighted DW-MRI. We found a good correspondence (r = 0.9) between scar areas delineated on the EAVM and MRI maps. For theoretical predictions, we used a simple two-variable macroscopic model and computed the propagation of action potential after application of a train of stimuli, with location and timing replicating the stimulation protocol used in the in-vivo EP study. Simulation results are exemplified for two hearts: one with noninducible ventricular tachycardia (VT), and another with a macroreentrant VT (for the latter, the average predicted VT cycle length was 273 ms, compared to a recorded VT of 250 ms).

An integrated platform for dynamic cardiac simulation and image processing: application to personalised tetralogy of fallot simulation

Processing and visualisation of dynamic data is still a common challenge in medical imaging, especially as for many applications there is an increasing amount of clinical data as well as generated data, such as in cardiac modelling. In this context, there is a strong need for software that can deal with dynamic data of different kinds (i.e. images, meshes, signals, etc.). In this paper we propose a platform that aims at helping researchers and clinicians to visualise and process such dynamic data, as well as evaluate simulation results. To illustrate this platform we chose to follow a concrete clinical application, the personalised simulation of the Tetralogy of Fallot. We show that the software provides the user with a significant help in the assessment and processing of the 3D+t raw data, as well as an adapted framework for visualisation and evaluation of various dynamic simulation results.