Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI

Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit regularisation terms such as smoothness, in this paper we propose a novel method that can implicitly learn biomechanics-informed regularisation. Such an approach can incorporate application-specific prior knowledge into deep learning based registration. Particularly, the proposed biomechanics-informed regularisation leverages a variational autoencoder (VAE) to learn a manifold for biomechanically plausible deformations and to implicitly capture their underlying properties via reconstructing biomechanical simulations. The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible. The proposed method is validated in the context of myocardial motion tracking on 2D stacks of cardiac MRI data from two different datasets. The results show that it can achieve better performance against other competing methods in terms of motion tracking accuracy and has the ability to learn biomechanical properties such as incompressibility and strains. The method has also been shown to have better generalisability to unseen domains compared with commonly used L2 regularisation schemes.Comment: The paper is early accepted by MICCAI 202

Symmetric Algorithmic Components for Shape Analysis with Diffeomorphisms

International audienceIn computational anatomy, the statistical analysis of temporal deformations and inter-subject variability relies on shape registration. However, the numerical integration and optimization required in diffeomorphic registration often lead to important numerical errors. In many cases, it is well known that the error can be drastically reduced in the presence of a symmetry. In this work, the leading idea is to approximate the space of deformations and images with a possibly non-metric symmetric space structure using an involution, with the aim to perform parallel transport. Through basic properties of symmetries, we investigate how the implementations of a midpoint and the involution compare with the ones of the Riemannian exponential and logarithm on diffeomorphisms and propose a modification of these maps using registration errors. This leads us to identify transvections, the composition of two symmetries, as a mean to measure how far from symmetric the underlying structure is. We test our method on a set of 138 cardiac shapes and demonstrate improved numerical consistency in the Pole Ladder scheme

Learning joint shape and appearance representations with metamorphic auto-encoders

International audienceTransformation-based methods for shape analysis offer a consistent framework to model the geometrical content of images. Most often relying on diffeomorphic transforms, they lack however the ability to properly handle texture and differing topological content. Conversely, modern deep learning methods offer a very efficient way to analyze image textures. Building on the theory of metamorphoses, which models images as combined intensity-domain and spatial-domain transforms of a prototype, we introduce the "metamorphic" auto-encoding architecture. This class of neural networks is interpreted as a Bayesian generative and hierarchical model, allowing the joint estimation of the network parameters, a representative prototype of the training images, as well as the relative importance between the geometrical and texture contents. We give arguments for the practical relevance of the learned prototype and Euclidean latent-space metric, achieved thanks to an explicit normalization layer. Finally, the ability of the proposed architecture to learn joint and relevant shape and appearance representations from image collections is illustrated on BraTs 2018 datasets, showing in particular an encouraging step towards personalized numerical simulation of tumors with data-driven models

Deformetrica 4: an open-source software for statistical shape analysis

International audienceDeformetrica is an open-source software for the statistical analysis of images and meshes. It relies on a specific instance of the large deformation diffeomorphic metric mapping (LDDMM) framework, based on control points: local momenta vectors offer a low-dimensional and interpretable parametrization of global diffeomorphims of the 2/3D ambient space, which in turn can warp any single or collection of shapes embedded in this physical space. Deformetrica has very few requirements about the data of interest: in the particular case of meshes, the absence of point correspondence can be handled thanks to the current or varifold representations. In addition to standard computational anatomy functionalities such as shape registration or atlas estimation, a bayesian version of atlas model as well as temporal methods (geodesic regression and parallel transport) are readily available. Installation instructions, tutorials and examples can be found at http://www.deformetrica.org

Long-Term Retention of Small, Volatile Molecular Species within Metallic Microcapsules

Encapsulation and full retention of small molecular weight active ingredients is a challenging task that remains unsolved by current technologies used in industry and academia. In particular, certain everyday product formulations provide difficult environments in which preventing active leakage through capsule walls is not feasible. For example, a continuous phase that can fully dissolve an encapsulated active will typically force full release over a fraction of the intended lifetime of a product. This is due to the inherent porosity of polymeric membranes typically used as capsule wall material in current technologies. In this study, we demonstrate a method for preventing undesired loss of encapsulated actives under these extreme conditions using a simple threestep process. Our developed methodology, which forms an impermeable metal film around polymer microcapsules, prevents loss of small, volatile oils within an ethanol continuous phase for at least 21 days while polymeric capsules lose their entire content in less than 30 min under the same conditions. Polymer shell–oil core microcapsules are produced using a well-known cosolvent extraction method to precipitate a polymeric shell around the oil core. Subsequently, metallic catalytic nanoparticles are physically adsorbed onto the microcapsule polymeric shells. Finally, this nanoparticle coating is used to catalyze the growth of a secondary metallic film. Specifically, this work shows that it is possible to coat polymeric microcapsules containing a model oil system or a typical fragrance oil with a continuous metal shell. It also shows that the coverage of nanoparticles on the capsule surface can be controlled, which is paramount for obtaining a continuous impermeable metal film. In addition, control over the metal shell thickness is demonstrated without altering the capability of the metal film to retain the encapsulated oils