## Personalization

## Anatomical Modeling

Current tomographic imaging modalities allow the geometrically accurate reconstruction of an individual's cardiac anatomy. A key objective is the conception of methods which allow to turn such tomographic image stacks into discrete anatomical representations which can be used for modeling cardiac physiology.

#### Image Based Mesh Generation

#### Rule-Based Fiber Algorithm

#### Universal Ventricular Coordinates (UVC)

## Digital Twinning

A digital twin is a digital replica of an individual person.

## Mechanical Personalization

The detailed workflow used for parameterization of electro-mechanical LV models is described in [Augustin et al.,2016] , personalizing passive and active mechanical model components as well as circulatory components. Constitutive relations accounting for the passive mechanical properties of the cardiac tissue are represented in terms of the Guccione Model . From the anatomical models at end-diastolic state, a stress-free reference configuration is computed using default material parameters and end-diastolic pressure in the LV. This is accomplished by unloading the geometry using a backward displacement method. The EDPVR is fitted to the empiric Klotz EDPVR using default values from literature for the parameters [Guccione et al.,1991] adapting only the stiffness parameter \(C_{\mathrm{Guc}}\). A simplified phenomenological contractile model was used to represent active stress generation , which was fitted using default values of \(\tau_\mathrm{C}\), \(\hat{S}_{\rm a}\), \(\tau_\mathrm{R}\) as initial guess. \(T_\mathrm{dur}\) was initialised with the RT interval observed in the ECG. A linear mapping was used to correct the active stress model parameters. When measured pressure traces \(p_\mathrm{lv,m}(t)\) were not available, only \(\hat{S}_{\rm a}\) was iteratively adjusted by a fixed-point iteration \(\hat{S}_{{\rm a},i+1} = \hat{S}_{{\rm a},i} \cdot \hat{p}_{\mathrm{lv}}/\hat{p}_{\mathrm{lv},i}\). Otherwise, rate of rise \(\tau_\mathrm{C}\) and decline \(\tau_\mathrm{R}\) of active stress were adjusted accordingly using \(\frac{p_{\rm lv,m}}{dt}|_\mathrm{max}\) and \(\frac{p_{\rm lv,m}}{dt}|_\mathrm{min}\) as references and fitted in an similar manner. Circulatory components in terms of the three-element Windkessel model were identified separately using a global-local optimization approach. Both workflows are visualized below.