Topical cardiac cooling--computer simulation of myocardial temperature changes

Framework for Personalized Real-Time Control of Hidden Temperature Variables in Therapeutic Knee Cooling

The paper formalizes, implements and evaluates a framework for personalized real-time control of inner knee temperature during cryotherapy after knee surgery. Studies have shown that the cryotherapy should be controlled depending on the individual patient's feedback on the cooling, which raises the need for smart personalized therapy. The framework is based on the feedback control loop that uses predicted instead of measured inner temperatures because measurements are not feasible or would introduce invasiveness into the system. It uses machine learning to construct a predictive model for estimation of the controlled inner temperature variable based on other variables whose measurement is more feasible - temperatures on the body surface. The machine learning method uses data generated from computer simulation of the therapeutic treatment for different input simulation parameters. A fuzzy proportional-derivative controller is designed to provide adequate near real-time control of the inner knee temperature by controlling the cooling temperature. The framework is evaluated for robustness and controllability. The results show that controlled cooling is essential for small-sized (and large-sized) knees that are significantly more (less) sensitive to the cooling compared to average-sized knees. Moreover, the framework recognizes dynamic physiological changes and potential changes in the system settings, such as extreme changes in the blood flow or changed target inner knee temperature, and consequently adapts the cooling temperature to reach the target value.

Non-invasive real-time prediction of inner knee temperatures during therapeutic cooling

The paper addresses the issue of non-invasive real-time prediction of hidden inner body temperature variables during therapeutic cooling or heating and proposes a solution that uses computer simulations and machine learning. The proposed approach is applied on a real-world problem in the domain of biomedicine - prediction of inner knee temperatures during therapeutic cooling (cryotherapy) after anterior cruciate ligament (ACL) reconstructive surgery. A validated simulation model of the cryotherapeutic treatment is used to generate a substantial amount of diverse data from different simulation scenarios. We apply machine learning methods on the simulated data to construct a predictive model that provides a prediction for the inner temperature variable based on other system variables whose measurement is more feasible, i.e. skin temperatures. First, we perform feature ranking using the RReliefF method. Next, based on the feature ranking results, we investigate the predictive performance and time/memory efficiency of several predictive modeling methods: linear regression, regression trees, model trees, and ensembles of regression and model trees. Results have shown that using only temperatures from skin sensors as input attributes gives excellent prediction for the temperature in the knee center. Moreover, satisfying predictive accuracy is also achieved using short history of temperatures from just two skin sensors (placed anterior and posterior to the knee) as input variables. The model trees perform the best with prediction error in the same range as the accuracy of the simulated data (0.1°C). Furthermore, they satisfy the requirements for small memory size and real-time response. We successfully validate the best performing model tree with real data from in vivo temperature measurement from a patient undergoing cryotherapy after ACL reconstruction.

3D heart model for computer simulations in cardiac surgery

For a satisfactory computer simulation, a model, which imitates a natural situation, is needed. The Human heart is an irregular 3D object and thus difficult to reproduce. Basic data was taken from Visible Human Dataset (VHD), National Library of Medicine. The heart area was cut out of the original cross-sections and different tissues segmented. All the slices also had to be aligned to assure precise overlapping of the structures. A 3D computer heart model with the resolution of 1mm was designed. The heart model was dedicated to simulations of heat transfer during heart surgery however, it is applicable also to other medical simulations.

Biomedical Simulation of Heat Transfer in a Human Heart

Theory and practical experiences from numerical simulations of heat transfer in the field of medicine are presented in this paper. The cooling of a human heart during surgery was taken as an illustrative example. All phases of the simulation process are described starting with the construction of an irregularly shaped 3-dimensional model. The mathematical model is based on diffusion and Navier-Stokes equations. The system of partial differential equations is solved by finite difference approximation using an explicit time-stepping scheme to obtain the time evolution of the solution for the complete simulated interval, which is typically 1 h. A typical domain is composed of several million voxels; therefore, the program was parallelized to speed up the simulation. A speed-up of 8.2 was obtained on 16 processors in a Linux cluster.

Confidential storage and transmission of medical image data

We discuss computationally efficient techniques for confidential storage and transmission of medical image data. Two types of partial encryption techniques based on AES are proposed. The first encrypts a subset of bitplanes of plain image data whereas the second encrypts parts of the JPEG2000 bitstream. We find that encrypting between 20% and 50% of the visual data is sufficient to provide high confidentiality.