Motion Compensation In Radiotherapy Research Articles

I094] Ultrasound guidance in radiotherapy - renaissance through innovation

Ultrasound imaging has been used for motion compensation in radiotherapy since the late 1990s. It does not utilize ionizing radiation and can provide real-time visualization and localization at a low cost. Early systems relied on freehand 2D imaging and facilitated soft-tissue image guidance in radiotherapy for the first time. However, they suffered from strong user dependency and limited positioning accuracy, slowing their widespread utilization. Many of their drawbacks were overcome when 3D ultrasound was introduced. 3D imaging is now commonly used for prostate treatment setup and under investigation for other sites. Innovations in hard- and software have led to the latest generation of ultrasound systems which can provide extremely high volumetric framerates with superior image quality by using matrix array transducers. Due to the large field-of-view and the high spatiotemporal resolution, these new systems are ideally suited for real-time motion compensation tasks. By accessing the volume data directly, it has now become possible to implement fast image processing for target detection. This information can then be used for gating the treatment beam or dynamic tracking techniques such as MLC or robotic systems to follow tumor motion with low latency and good dosimetric results. To aid less experienced, non-expert users with the setup, tools have been developed to calculate the ideal probe position and guide the user through the anatomy with real-time visual feedback, potentially further aided by augmented reality applications in the future. Furthermore, robotic solutions for probe positioning and stabilization are currently being investigated in order to eliminate user dependency, one of the biggest challenges in ultrasound imaging to date. Recent developments in technology have facilitated such new approaches and helped overcome some of the previous drawbacks. The evident benefits of ultrasound imaging make it a promising modality for real-time motion compensation in radiotherapy of soft tissues.

Performance behavior of prediction filters for respiratory motion compensation in radiotherapy

Abstract Introduction: In radiotherapy, tumors may move due to the patient’s respiration, which decreases treatment accuracy. Some motion mitigation methods require measuring the tumor position during treatment. Current available sensors often suffer from time delays, which degrade the motion mitigation performance. However, the tumor motion is often periodic and continuous, which allows predicting the motion ahead. Method and Materials: A couch tracking system was simulated in MATLAB and five prediction filters selected from literature were implemented and tested on 51 respiration signals (median length: 103 s). The five filters were the linear filter (LF), the local regression (LOESS), the neural network (NN), the support vector regression (SVR), and the wavelet least mean squares (wLMS). The time delay to compensate was 320 ms. The normalized root mean square error (nRMSE) was calculated for all prediction filters and respiration signals. The correlation coefficients between the nRMSE of the prediction filters were computed. Results: The prediction filters were grouped into a low and a high nRMSE group. The low nRMSE group consisted of the LF, the NN, and the wLMS with a median nRMSE of 0.14, 0.15, and 0.14, respectively. The high nRMSE group consisted of the LOESS and the SVR with both a median nRMSE of 0.34. The correlations between the low nRMSE filters were above 0.87 and between the high nRMSE filters it was 0.64. Conclusion: The low nRMSE prediction filters not only have similar median nRMSEs but also similar nRMSEs for the same respiration signals as the high correlation shows. Therefore, good prediction filters perform similarly for identical respiration patterns, which might indicate a minimally achievable nRMSE for a given respiration pattern.

Motion Compensation in Radiotherapy

Image-guided radiotherapy (IGRT) has helped to dramatically reduce safety margins compensating for positioning uncertainties in radiotherapy. A remaining issue posing problems for photon radiotherapy (RT), but even more so for particle RT, is target motion during treatment delivery. This review outlines the various strategies currently being developed or already in clinical use to compensate for organ motion, predominantly breathing-induced motion of liver and lung targets. Several motion compensation strategies have recently been introduced clinically. Among these are optimized margins encompassing the individual range of target motion, treatment under breath hold, gated treatments, and tumor tracking with a dedicated treatment device. A variety of surveillance strategies for gating and tracking, such as indirect tracking with external fiducial markers and surface scanning devices, direct tracking with implanted electromagnetic markers, fiducial markers, and fluoroscopy, and ultrasound-based tracking are already in clinical use or are under development. Tracked treatment with linear accelerators based on tumor-synchronous MLC- or treatment-table adaptation are moving toward clinical use. A multitude of strategies to reduce the impact of intrafractional target motion in RT have been developed and are increasingly being used clinically. The clinical introduction of advanced strategies currently under development is imminent. After IGRT minimized treatment margins for static tumors, the implementation of motion compensation strategies will achieve the same for targets being subject to intrafractional breathing-induced motion.

Tracking latency in image-based dynamic MLC tracking with direct image access

Purpose. Target tracking is a promising method for motion compensation in radiotherapy. For image-based dynamic multileaf collimator (DMLC) tracking, latency has been shown to be the main contributor to geometrical errors in tracking of respiratory motion, specifically due to slow transfer of image data from the image acquisition system to the tracking system via image file storage on a hard disk. The purpose of the current study was to integrate direct image access with a DMLC tracking system and to quantify the tracking latency of the integrated system for both kV and MV image-based tracking. Method. A DMLC tracking system integrated with a linear accelerator was used for tracking of a motion phantom with an embedded tungsten marker. Real-time target localization was based on x-ray images acquired either with a portal imager or a kV imager mounted orthogonal to the treatment beam. Images were processed directly without intermediate disk access. Continuous portal images and system log files were stored during treatment delivery for detailed offline analysis of the tracking latency. Results. The mean tracking system latency for kV and MV image-based tracking as function of the imaging interval ΔTimage increased linearly with ΔTimage as 148 ms + 0.58 * ΔTimage (kV) and 162 ms + 1.1 * ΔTimage (MV). The latency contribution from image acquisition and image transfer for kV image-based tracking was independent on ΔTimage at 103 ± 14 ms. For MV-based tracking, it increased with ΔTimage as 124 ms + 0.44 * ΔTimage. For ΔTimage = 200 ms (5 Hz imaging), the total latency was reduced from 550 ms to 264 ms for kV image-based tracking and from 500 ms to 382 ms for MV image-based tracking as compared to the previously used indirect image transfer via image file storage on a hard disk. Conclusion. kV and MV image-based DMLC tracking was successfully integrated with direct image access. It resulted in substantial tracking latency reductions compared with image-based tracking without direct image access.

TH‐C‐BRC‐06: Performance Measures and Pre‐Processing for Respiratory Motion Prediction

Purpose: Much research has been done on prediction of respiratory motion traces for motion compensation in radiotherapy. Unfortunately, the results of different groups cannot be compared easily due to different standards in preprocessing and analysis of the results. Furthermore, it has been speculated that the typically used measure for prediction quality, the RMS error, is not sufficient alone. Methods: We propose a set of guidelines for signal preprocessing (i.e., for scaling, detrending, resampling, and denoising) as well as measures for the analysis of the prediction results. The latter complement the RMS error with confidence intervals, the signalˈs smoothness (called jitter) and a measure for the periodicity of the error (called frequency content). Additionally, we have developed an extendable cross‐platform prediction toolkit for easy analysis of prediction algorithms. Results: We found that very different signals (corrupted by noise, scaled by a constant factor, delayed in time, and scaled by random factors for each respiratory period) feature the exact same RMS error when compared to the original signal. The fundamental difference in the error signals can only be determined when using spectral measures, like the frequency content. Conclusion: Using the guidelines developed, the proposed evaluation measures, as well as the publicly available prediction toolkit, should help the community in establishing a better understanding for the capabilities and shortcomings of individual prediction methods. Additionally, it should allow others to more readily compare newly developed methods to already published algorithms. In the future, it would be desirable to also create a database of motion traces from various sources. If these signals would represent the characteristics of motion traces observed in the clinic, it could serve as a general benchmark for the quality of algorithms for motion prediction.

Motion prediction and control for patient motion compensation in radiotherapy

AbstractThis paper summarizes the work carried out at Coventry University on video tracking and patient motion compensation during the MAESTRO European project. A novel combination of motion prediction, couch control and couch nonlinearities compensation was developed and implemented in real time to control an Elekta AB (Publ) ‘Precise’ patient support system or ‘couch'. The reference trajectory was obtained from a video tracking system implemented using LabVIEW. A new anthropomorphic robotic system was used to generate target motions which were captured with a video tracking system that provided a reference to the PSS control system implemented using Matlab® Simulink® and dSPACE. The PSS with its new control system reduced the vertical and longitudinal motion of a surrogate marker located on a robotic arm to less than 1mm RMS and 2mm 95% confidence interval. The motion compensation resulted in significant dosimetric improvements compared to irradiating a moving target. Irradiation of Gafchromic films produced dose distributions with no noticeable differences between fixed target and a motion compensated moving target.

Fully automatic detection of corresponding anatomical landmarks in volume scans of different respiratory state

A method is described which provides fully automatic detection of corresponding anatomical landmarks in volume scans taken at different respiratory states. The resulting control points are needed for creating a volumetric deformation model for motion compensation in radiotherapy. Prior to treatment two CT volumes are taken, one scan during inhalation, one during exhalation. These scans and the detected control point pairs are taken as input for creating the four-dimensional model by using thin-plate splines.