Robert Dürichen

Multitask Gaussian Processes for Multivariate Physiological Time-Series Analysis

Gaussian process (GP) models are a flexible means of performing nonparametric Bayesian regression. However, GP models in healthcare are often only used to model a single univariate output time series, denoted as single-task GPs (STGP). Due to an increasing prevalence of sensors in healthcare settings, there is an urgent need for robust multivariate time-series tools. Here, we propose a method using multitask GPs (MTGPs) which can model multiple correlated multivariate physiological time series simultaneously. The flexible MTGP framework can learn the correlation between multiple signals even though they might be sampled at different frequencies and have training sets available for different intervals. Furthermore, prior knowledge of any relationship between the time series such as delays and temporal behavior can be easily integrated. A novel normalization is proposed to allow interpretation of the various hyperparameters used in the MTGP. We investigate MTGPs for physiological monitoring with synthetic data sets and two real-world problems from the field of patient monitoring and radiotherapy. The results are compared with standard Gaussian processes and other existing methods in the respective biomedical application areas. In both cases, we show that our framework learned the correlation between physiological time series efficiently, outperforming the existing state of the art.

Respiratory Motion Compensation with Relevance Vector Machines

In modern robotic radiation therapy, tumor movements due to respiration can be compensated. The accuracy of these methods can be increased by time series prediction of external optical surrogates. An algorithm based on relevance vector machines (RVM) is introduced. We evaluate RVM with linear and nonlinear basis functions on a real patient data set containing 304 motion traces and compare it with a wavelet based least mean square algorithm (wLMS), the best algorithm for this data set so far. Linear RVM outperforms wLMS significantly and increases the prediction accuracy for 80.3% of the data. We show that real time prediction is possible in case of linear RVM and discuss how the predicted variance can be used to construct promising hybrid algorithms, which further reduce the prediction error.

Tissue thickness estimation for high precision head-tracking using a galvanometric laser scanner - a case study.

Marker-less optical head-tracking constitutes a comfortable alternative with no exposure to radiation for realtime monitoring in radiation therapy. Supporting information such as tissue thickness has the potential to improve spatial tracking accuracy. Here we study how accurate tissue thickness can be estimated from the near-infrared (NIR) backscatter obtained from laser scans. In a case study, optical data was recorded with a galvanometric laser scanner from three subjects. A tissue ground truth from MRI was robustly matched via customized bite blocks. We show that Gaussian Processes accurately model the relationship between NIR features and tissue thickness. They were able to predict the tissue thickness with less than 0.5 mm root mean square error. Individual scaling factors for all features and an additional incident angle feature had positive effects on this performance.

Evaluation of the potential of multi-modal sensors for respiratory motion prediction and correlation

In modern robotic radiotherapy, precise radiation of moving tumors is possible by tracking external optical surrogates. The surrogates are used to compensate for time delays and to predict internal landmarks using a correlation model. The correlation depends significantly on the surrogate position and breathing characteristics of the patient. In this context, we aim to increase the accuracy and robustness of prediction and correlation models by using a multi-modal sensor setup. Here, we evaluate the correlation coefficient of a strain belt, an acceleration and temperature sensor (air flow) with respect to external optical sensors and one internal landmark in the liver, measured by 3D ultrasound. The focus of this study is the influence of breathing artefacts, like coughing and harrumphing. Evaluating seven subjects, we found a strong decrease of the correlation for all modalities in case of artefacts. The results indicate that no precise motion compensation during these times is possible. Overall, we found that apart from the optical markers, the strain belt and temperature sensor data show the best correlation to external and internal motion.

Controlling motion prediction errors in radiotherapy with relevance vector machines

PurposeRobotic radiotherapy can precisely ablate moving tumors when time latencies have been compensated. Recently, relevance vector machines (RVM), a probabilistic regression technique, outperformed six other prediction algorithms for respiratory compensation. The method has the distinct advantage that each predicted point is assumed to be drawn from a normal distribution. Second-order statistics, the predicted variance, were used to control RVM prediction error during a treatment and to construct hybrid prediction algorithms.MethodsFirst, the duty cycle and the precision were correlated to the variance by interrupting the treatment if the variance exceeds a threshold. Second, two hybrid algorithms based on the variance were developed, one consisting of multiple RVMs (

A UNIFIED APPROACH FOR RESPIRATORY MOTION PREDICTION AND CORRELATION WITH MULTI-TASK GAUSSIAN PROCESSES

\hbox {HYB}_{\textit{RVM}}

Evaluating and comparing algorithms for respiratory motion prediction.

HYBRVM) and the other of a combination between a wavelet-based least mean square algorithm (wLMS) and a RVM (