Floris Ernst

Prediction of respiratory motion with wavelet-based multiscale autoregression

In robotic radiosurgery, a photon beam source, moved by a robot arm, is used to ablate tumors. The accuracy of the treatment can be improved by predicting respiratory motion to compensate for system delay. We consider a wavelet-based multiscale autoregressive prediction method. The algorithm is extended by introducing a new exponential averaging parameter and the use of the Moore-Penrose pseudo inverse to cope with long-term signal dependencies and system matrix irregularity, respectively. In test cases, this new algorithm outperforms normalized LMS predictors by as much as 50%. With real patient data, we achieve an improvement of around 5 to 10%.

Non-orthogonal tool/flange and robot/world calibration

For many robot‐assisted medical applications, it is necessary to accurately compute the relation between the robots coordinate system and the coordinate system of a localisation or tracking device. Today, this is typically carried out using hand‐eye calibration methods like those proposed by Tsai/Lenz or Daniilidis.

Compensating for quasi-periodic motion in robotic radiosurgery /

Compensating for quasi-periodic motion in robotic radiosurgery / , Compensating for quasi-periodic motion in robotic radiosurgery / , کتابخانه دیجیتال جندی شاپور اهواز

A comparison of two clinical correlation models used for real-time tumor tracking of semi-periodic motion: A focus on geometrical accuracy in lung and liver cancer patients.

PURPOSE A head-to-head comparison of two clinical correlation models with a focus on geometrical accuracy for internal tumor motion estimation during real-time tumor tracking (RTTT). METHODS AND MATERIALS Both the CyberKnife (CK) and the Vero systems perform RTTT with a correlation model that is able to describe hysteresis in the breathing motion. The CK dual-quadratic (DQ) model consists of two polynomial functions describing the trajectory of the tumor for inhale and exhale breathing motion, respectively. The Vero model is based on a two-dimensional (2D) function depending on position and speed of the external breathing signal to describe a closed-loop tumor trajectory. In this study, 20 s of internal motion data, using an 11 Hz (on average) full fluoroscopy (FF) sequence, was used for training of the CK and Vero models. Further, a subsampled set of 15 internal tumor positions (15p) equally spread over the different phases of the breathing motion was used for separate training of the CK DQ model. Also a linear model was trained using 15p and FF tumor motion data. Fifteen liver and lung cancer patients, treated on the Vero system with RTTT, were retrospectively evaluated comparing the CK FF, CK 15p and Vero FF models using an in-house developed simulator. The distance between estimated target position and the tumor position localized by X-ray imaging was measured in the beams-eye view (BEV) to calculate the 95th percentile BEV modeling errors (ME(95,BEV)). Additionally, the percentage of ME(95,BEV) smaller than 5 mm (P(5mm)) was determined for all correlation models. RESULTS In general, no significant difference (p>0.05, paired t-test) was found between the CK FF and Vero models. Based on patient-specific evaluation of the geometrical accuracy of the linear, CK DQ and Vero correlation models, no statistical necessity (p>0.05, two-way ANOVA) of including hysteresis in correlation models was proven, although during inhale breathing motion, the linear model resulted in a decreased P(5mm) with 5-6% compared to both the DQ CK and Vero models. CONCLUSION Dual-quadratic CyberKnife and 2D Vero correlation models were interchangeable in terms of geometrical accuracy with the CK linear ME(95,BEV)=4.1 mm, CK dual-quadratic ME(95,BEV)=3.9 mm and Vero ME(95,BEV)=3.7 mm, when modeled with FF sequence. CK DQ modeling based on 15p acquired in 20 s may lead to problems for internal motion estimation.

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.

Robust real-time robot–world calibration for robotized transcranial magnetic stimulation

For robotized transcranial magnetic stimulation (TMS), the magnetic coil is placed on the patients head by a robot. As the robotized TMS system requires tracking of head movements, robot and tracking camera need to be calibrated. However, for robotized TMS in a clinical setting, such calibration is required frequently. Mounting/unmounting a marker to the end effector and moving the robot into different poses is impractical. Moreover, if either system is moved during treatment, recalibration is required.

Estimating soft tissue thickness from light-tissue interactions––a simulation study

Immobilization and marker-based motion tracking in radiation therapy often cause decreased patient comfort. However, the more comfortable alternative of optical surface tracking is highly inaccurate due to missing point-to-point correspondences between subsequent point clouds as well as elastic deformation of soft tissue. In this study, we present a proof of concept for measuring subcutaneous features with a laser scanner setup focusing on the skin thickness as additional input for high accuracy optical surface tracking. Using Monte-Carlo simulations for multi-layered tissue, we show that informative features can be extracted from the simulated tissue reflection by integrating intensities within concentric ROIs around the laser spot center. Training a regression model with a simulated data set identifies patterns that allow for predicting skin thickness with a root mean square error of down to 18 µm. Different approaches to compensate for varying observation angles were shown to yield errors still below 90 µm. Finally, this initial study provides a very promising proof of concept and encourages research towards a practical prototype.

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.

Preliminary study on optical feature detection for head tracking in radiation therapy

Marker-less tracking provides a non-invasive as well as comfortable approach to compensate for head motion in high precision radiotherapy. However, it suffers from a lack of point-to-point correspondences, typically requiring characteristic spatial landmarks to match point clouds. In this study, we show that cutaneous and subcutaneous structures can be uncovered using an 850 nm laser setup. For three subjects, we compare features extracted from camera images with MR scans serving as an anatomical ground truth. The results confirm the validity of the optically detected structures. The negative correlation between skin thickness and reflected light energy is likewise predicted by Monte-Carlo simulations and can be used to improve spatial point cloud matching. Tissue thickness and its facial structure can be predicted with submillimeter accuracy using a Support Vector regression machine. In addition, the optical measurements reveal the location of vessels that are not immediately visible in the MR scan. These promising findings highly encourage its application for a marker-less tracking system.