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Abstract: Adding external knowledge improves the results for ill-posed problems. In this paper we present a new computational framework for image registration when adding constraints on the transformation. We demonstrate that unconstrained registration can lead to ambiguous and non-physical results. Adding appropriate constraints introduces prior knowledge and contributes to reliability and uniqueness of the registration. Particularly, we consider recently proposed locally rigid transformations and volume preserving constraints as examples. [Copyright &y& Elsevier]

Abstract: A previously validated mathematical model of the cardiovascular system (CVS) is made subject-specific using an iterative, proportional gain-based identification method. Prior works utilised a complete set of experimentally measured data that is not clinically typical or applicable. In this paper, parameters are identified using proportional gain-based control and a minimal, clinically available set of measurements. The new method makes use of several intermediary steps through identification of smaller compartmental models of CVS to reduce the number of parameters identified simultaneously and increase the convergence stability of the method. This new, clinically relevant, minimal measurement approach is validated using a porcine model of acute pulmonary embolism (APE). Trials were performed on five pigs, each inserted with three autologous blood clots of decreasing size over a period of four to five hours. All experiments were reviewed and approved by the Ethics Committee of the Medical Faculty at the University of Liege, Belgium. Continuous aortic and pulmonary artery pressures (P ao , P pa ) were measured along with left and right ventricle pressure and volume waveforms. Subject-specific CVS models were identified from global end diastolic volume (GEDV), stroke volume (SV), P ao , and P pa measurements, with the mean volumes and maximum pressures of the left and right ventricles used to verify the accuracy of the fitted models. The inputs (GEDV, SV, P ao , P pa ) used in the identification process were matched by the CVS model to errors <0.5%. Prediction of the mean ventricular volumes and maximum ventricular pressures not used to fit the model compared experimental measurements to median absolute errors of 4.3% and 4.4%, which are equivalent to the measurement errors of currently used monitoring devices in the ICU (∼5–10%). These results validate the potential for implementing this approach in the intensive care unit. [Copyright &y& Elsevier]