Changes induced by intrauterine growth restriction (IUGR) in cardiovascular anatomy and function that persist throughout life have been associated with a higher predisposition to heart disease in adulthood. Together with cardiac morphological remodelling, evaluated through the ventricular sphericity index, alterations in cardiac electrical function have been reported by characterization of the depolarization and repolarization loops, and their angular relationship, measured from the vectorcardiogram. The underlying relationship between the morphological remodelling and the angular variation of QRS and T-wave dominant vectors, if any, has not been explored. The aim of this study was to evaluate this relationship using computational models based on realistic heart and torso in which IUGR-induced morphological changes were incorporated by reducing the ventricular sphericity index. Specifically, we departed from a control model and we built eight different globular heart models by reducing the base-to-apex length and enlarging the basal ventricular diameter. We computed QRS and T-wave dominant vectors and angles from simulated pseudo-electrocardiograms and we compared them with clinical measurements. Results for the QRS to T angles follow a change trend congruent with that reported in clinical data, supporting the hypothesis that the IUGR-induced morphological remodelling could contribute to explain the observed angle changes in IUGR patients. By additionally varying the position of the ventricles with respect to the torso and the electrodes, we found that electrode displacement can impact the quantified angles and should be considered when interpreting the results.

3D echocardiography is an increasingly popular tool for assessing cardiac remodelling in the right ventricle (RV). It allows quantification of the cardiac chambers without any geometric assumptions, which is the main weakness of 2D echocardiography. However, regional quantification of geometry and function is limited by the lower spatial and temporal resolution and the scarcity of identifiable anatomical landmarks, especially within the ventricular cavity. We developed a technique for regionally assessing the volume of 3 relevant RV volumetric regions: apical, inlet and outflow. The proposed parcellation method is based on the geodesic distances to anatomical landmarks that are easily identifiable in the images: the apex and the tricuspid and pulmonary valves, each associated to a region. Based on these distances, we define a partition in the endocardium at end-diastole (ED). This partition is then interpolated to the blood cavity using the Laplace equation, which allows to compute regional volumes. For obtaining an end-systole (ES) partition, the endocardial partition is transported from ED to ES using a commercial image-based tracking software, and then the interpolation process is repeated. We assessed the intra- and inter-observer reproducibility using a 10-subjects dataset containing repeated quantifications of the same images, obtaining intra- and inter- observer errors (7-12% and 10-23% respectively). Finally, we propose a novel synthetic mesh generation algorithm that deforms a template mesh imposing a user-defined strain to a template mesh. We used this method to create a new dataset for involving distinct types of remodelling that were used to assess the sensitivity of the parcellation method to identify volume changes affecting different parts. We show that the parcellation method is adequate for capturing local circumferential and global circumferential and longitudinal RV remodelling, which are the most clinically relevant cases.

Clinical decision requires reasoning in the presence of imperfect data. DTs are a well-known decision support tool, owing to their interpretability, fundamental in safety-critical contexts such as medical diagnosis. However, learning DTs from uncertain data leads to poor generalization, and generating predictions for uncertain data hinders prediction accuracy. Several methods have suggested the potential of probabilistic decisions at the internal nodes in making DTs robust to uncertainty. Some approaches only employ probabilistic thresholds during evaluation. Others also consider the uncertainty in the learning phase, at the expense of increased computational complexity or reduced interpretability. The existing methods have not clarified the merit of a probabilistic approach in the distinct phases of DT learning, nor when the uncertainty is present in the training or the test data. We present a probabilistic DT approach that models measurement uncertainty as a noise distribution, independently realized: (1) when searching for the split thresholds, (2) when splitting the training instances, and (3) when generating predictions for unseen data. The soft training approaches (1, 2) achieved a regularizing effect, leading to significant reductions in DT size, while maintaining accuracy, for increased noise. Soft evaluation (3) showed no benefit in handling noise.

Intrauterine growth restriction (IUGR) due to placental insufficiency is associated with blood flow redistribution in order to maintain delivery of oxygenated blood to the brain. Given that, in the fetus the aortic isthmus (AoI) is a key arterial connection between the cerebral and placental circulations, quantifying AoI blood flow has been proposed to assess this brain sparing effect in clinical practice. While numerous clinical studies have studied this parameter, fundamental understanding of its determinant factors and its quantitative relation with other aspects of haemodynamic remodeling has been limited. Computational models of the cardiovascular circulation have been proposed for exactly this purpose since they allow both for studying the contributions from isolated parameters as well as estimating properties that cannot be directly assessed from clinical measurements. Therefore, a computational model of the fetal circulation was developed, including the key elements related to fetal blood redistribution and using measured cardiac outflow profiles to allow personalization. The model was first calibrated using patient-specific Doppler data from a healthy fetus. Next, in order to understand the contributions of the main parameters determining blood redistribution, AoI and middle cerebral artery (MCA) flow changes were studied by variation of cerebral and peripheral-placental resistances. Finally, to study how this affects an individual fetus, the model was fitted to three IUGR cases with different degrees of severity. In conclusion, the proposed computational model provides a good approximation to assess blood flow changes in the fetal circulation. The results support that while MCA flow is mainly determined by a fall in brain resistance, the AoI is influenced by a balance between increased peripheral-placental and decreased cerebral resistances. Personalizing the model allows for quantifying the balance between cerebral and peripheral-placental remodeling, thus providing potentially novel information to aid clinical follow up.

We propose a technique for myocardial motion estimation based on image registration using both B-mode echocardiographic images and tissue Doppler sequences acquired interleaved. The velocity field is modeled continuously using B-splines and the spatiotemporal transform is constrained to be diffeomorphic. Images before scan conversion are used to improve the accuracy of the estimation. The similarity measure includes a model of the speckle pattern distribution of B-mode images. It also penalizes the disagreement between tissue Doppler velocities and the estimated velocity field. Registration accuracy is evaluated and compared to other alternatives using a realistic synthetic dataset, obtaining mean displacement errors of about 1 mm. Finally, the method is demonstrated on data acquired from six volunteers, both at rest and during exercise. Robustness is tested against low image quality and fast heart rates during exercise. Results show that our method provides a robust motion estimate in these situations.

Ultrasonic imaging is the noninvasive clinical imaging modality of choice for diagnosing heart disease. At present, two-dimensional ultrasonic grayscale images provide a relatively cheap, fast, bedside method to study the morphology of the heart. Several methods have been proposed to assess myocardial function. These have been based on either grayscale or motion (velocity) information measured in real-time. However, the quantitative assessment of regional myocardial function remains an important goal in clinical cardiology. To do this, ultrasonic strain and strain-rate imaging have been introduced. In the clinical setting, these techniques currently only allow one component of the true three-dimensional deformation to be measured. Clinical, multidimensional strain (rate) information can currently thus only be obtained by combining data acquired using different transducer positions. Nevertheless, given the appropriate postprocessing, the clinical value of these techniques has already been shown. Moreover, multidimensional strain and strain-rate estimation of the heart in vivo by means of a single ultrasound acquisition has been shown to be feasible. In this paper, the new techniques of ultrasonic strain rate and strain imaging of the heart are reviewed in terms of definitions, data acquisition, strain-rate estimation, postprocessing, and parameter extraction. Their clinical validation and relevance will be discussed using clinical examples on relevant cardiac pathology. Based on these examples, suggestions are made for future developments of these techniques.