Surface Matching via Currents

Caiazzo

et al. 2014

Medical Image Analysis

Abstract3D computational fluid dynamics (CFD) in patient-specific geometries provides complementary insights to clinical imaging, to better understand how heart disease, and the side effects of treating heart disease, affect and are affected by hemodynamics. This information can be useful in treatment planning for designing artificial devices that are subject to stress and pressure from blood flow. Yet, these simulations remain relatively costly within a clinical context. The aim of this work is to reduce the complexity of patient-specific simulations by combining image analysis, computational fluid dynamics and model order reduction techniques. The proposed method makes use of a reference geometry estimated as an average of the population, within an efficient statistical framework based on the currents representation of shapes. Snapshots of blood flow simulations performed in the reference geometry are used to build a POD (Proper Orthogonal Decomposition) basis, which can then be mapped on new patients to perform reduced order blood flow simulations with patient specific boundary conditions. This approach is applied to a data-set of 17 tetralogy of Fallot patients to simulate blood flow through the pulmonary artery under normal (healthy or synthetic valves with almost no backflow) and pathological (leaky or absent valve with backflow) conditions to better understand the impact of regurgitated blood on pressure and velocity at the outflow tracts.The model reduction approach is further tested by performing patient simulations under exercise and varying degrees of pathophysiological conditions based on reduction of reference solutions (rest and medium backflow conditions respectively).

Section: Surface-to-surface Registrationmentioning

confidence: 99%

Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images

Guibert

Caiazzo

et al. 2014

Medical Image Analysis

“…This means that we are left with a finite dimensional optimization problem [37,7]. Notice that the number of unknowns is directly linked to the number of Diracs in the source surface S, which is usually the atlas.…”

Section: An Algorithm For Surface Registration Using Currentsmentioning

confidence: 99%

Statistical Shape Analysis of Surfaces in Medical Images Applied to the Tetralogy of Fallot Heart

Modeling in Computational Biology and Biomedicine

Mansi

Sermesant

et al. 2012

International audienceThere is an increasing need for shape statistics in medical imaging to provide quantitative measures to aid in diagnosis, prognosis and therapy planning. In view of this, we describe methods for computing such statistics by utilizing a well-posed framework for representing the shape of surfaces as currents. Given this representation we can compute an atlas as a mean representation of the population and the main modes of variation around this mean. The modes are computed using principal component analysis (PCA) and applying standard correlation analysis to these allows to correlate shape features with clinical indices. Beyond this, we can compute a generative model of growth using partial least squares regression (PLS) and canonical correlation analysis (CCA). In this chapter, we investigate a clinical application of these statistical techniques on the shape of the heart for patients with repaired Tetralogy of Fallot (rToF), a severe congenital heard defect that requires surgical repair early in infancy. We relate the shape to the severity of the pathology and we build a bi-ventricular growth model of the rToF heart from cross-sectional data which gives insights about the evolution of the disease

“…The deformation ϕ i that registers the template T to the current T i is estimated using the Large Deformation Diffeomorphic Mappings (LDDMM) framework [3]. ϕ i is parameterised by a smooth initial vector speed v i 0 , which also belongs to a Gaussian RKHS V with variance λ 2 V .…”

Section: Unbiased Template Of the Pulmonary Artery In Tof Patientsmentioning

confidence: 99%

Atlas-Based Reduced Models of Blood Flows for Fast Patient-Specific Simulations

Statistical Atlases and Computational Models of the Heart

Caiazzo

Fernández

et al. 2010

Abstract.Model-based interpretation of the complex clinical data now available (shape, motion, flow) can provide quantitative information for diagnosis as well as predictions. However such models can be extremely time consuming, which does not always fit with the clinical time constraints. The aim of this work is to propose a model reduction technique to perform faster patient-specific simulations with prior knowledge built from simulations on an average anatomy. Rather than simulating a full fluid problem on individual patients, we create a representative 'template' of the artery shape. A full flow simulation is carried out only on this template, and a reduced model is built from the results. Then this reduced model can be transported to the individual geometries, allowing faster computational analysis. Here we propose a preliminary validation of this idea. A well-posed framework based on currents representation of shapes is used to create an unbiased template of the pulmonary artery for 4 patients with Tetralogy of Fallot. Then, a reduced computational fluid dynamics model is built on this template. Finally, we demonstrate that this reduced model can represent a specific patient simulation.