Seminar "Quantitative Biomedizin"

In recent years "Quantitative Biomedicine" has become a major topic at the Weierstrass-Institute for Applied Analysis and Stochastics. This seminar series features talks by established scientists in the field to foster the interaction between mathematics and its applications. Thereby, it is focusing on the areas of medical image processing, modeling of the cardiovascular system and modeling of biomaterials.

2017
20.03.2017 Thoralf Niendorf (Charité Max Delbrück Center for Molecular Medicine, B.U.F.F.; MRI.TOOLS GmbH)

Explorations into Ultrahigh Field Magnetic Resonance
Where Physics, Mathematics, Biology and Medicine Meet

The development of ultrahigh field magnetic resonance (UHF-MR) is moving forward at an amazing speed that is breaking through technical barriers almost as fast as they appear. UHF-MR has a staggering number of potential uses in neuroscience, neurology, radiology, cardiology, internal medicine, physiology, oncology, nephrology, ophthalmology and other related clinical fields. With almost 40,000 MR examinations already performed at 7.0 Tesla, the reasons for moving UHF-MR into clinical applications are more compelling than ever ... [>> read more as PDF]

22.05.2017 Ralf Blossey (University of Lille 1 & CNRS)

Beyond Poisson-Boltzmann : charge correlation effects in DNA adsorption and transport through nanopores

In soft matter systems electrostatic effects are often of utmost importance. Their mathematical description is commonly based on mean-field type theories, the standard approach being the Poisson-Boltzmann equation. This equation is known to fail if charge correlation effects, e.g., due to the presence of image charges at interfaces, become important. In this talk I will explain how one can go beyond the Poisson-Boltzmann equation in order to treat such mechanisms. As two exemplary applications I will discuss the correlation-induced adsorption of DNA on like-charged surfaces and the conversion of ionic currents in DNA translocation through nanopores.

12.06.2017 Tobias Schaeffter (Physikalisch-Technische Bundesanstalt Berlin, King's College London)

Advances in Cardiac and Quantitative MRI

Cardiac Magnetic Resonance (CMR) imaging has become a clinically useful tool to assess different physiological parameter in one examination. The widespread use of cardiac MRI, however, is often hampered by the complexity of MRI and the long acquisition time. For this novel, 3D MR-acquisitions have been developed to obtain anatomical, functional and flow information of the whole heart. Fast acquisition can be achieved by advanced motion compensation and new reconstruction approaches. Furthermore, a more quantitative oriented diagnosis can be achieved by pixel-wise measurement of parameters. For instance, mapping of the relaxation times allows the assessment of tissue characteristics and the estimation of contrast agent concentration. In this talk an overview on the different technical developments are given and clinical applications are shown.

18.09.2017 Dominique Chapelle (Inria Saclay Ile-de-France Research Center)

Biomechanical modeling of the heart, and cardiovascular system - From sarcomeres to organ / system, with experimental assessments and patient-specific clinical validations

Cardiac contraction originates at a subcellular - molecular, indeed - scale, within specific components of the cardiomyocytes (i.e. cardiac cells) called sarcomeres. This contractile behaviour then needs to be integrated at the organ level, namely, with a specific structure and shape. Furthermore, this organ crucially interacts with other physiological systems, the first of which being blood circulation via the cardiac function itself, and also the nervous system that controls the heart via various regulation mechanisms, and these interactions must be adequately represented in order to obtain accurate and predictive model simulations. This presentation will provide an overview of the recent advances on cardiac modeling achieved in the M3DISIM group, with a particular focus on the key multiscale, multi-physics and integrated system modeling aspects that need to be addressed, with many associated challenges pertaining to numerical methods, model validation, and inverse problems, in particular.

20.11.2017 Jean-Frederic Gerbeau (Inria Paris & Sorbonne Universités UPMC Paris 6)

Numerical methods for variability modeling and biomarkers design

Many phenomena are modeled by deterministic differential equations, whereas the observation of these phenomena, in particular in life science, exhibit an important inter-subject variability. We will address the following question: how the model can be adapted to reflect the variability observed in a population? We will present a non-parametric and non-intrusive procedure based on offline computations of the deterministic model. The algorithm infers the probability density function of uncertain parameters from the matching of the observable statistical moments at different points in the physical domain. This inverse procedure is improved by incorporating a point selection algorithm that both reduces its computational cost and increases its robustness. The method will be illustrated for different models, based on Ordinary or Partial Differential Equations. In particular, applications to experimental data sets in cardiac electrophysiology will be presented.
In biophysics and medicine, the system of interest is often studied by monitoring quantities, called biomarkers, extracted from measurements. These biomarkers convey some information about relevant hidden quantities, which can be seen as parameters of an underlying model. We propose a strategy to automatically design biomarkers to estimate a given parameter. Such biomarkers are chosen as the solution of a sparse optimization problem. We will show applications in electrophysiology where our algorithm provides composite biomarkers which improve the parameter estimation and the classification problems.