An image-based method to synchronize cone-beam CT and optical surface tracking

open5siThe integration of in-room X-ray imaging and optical surface tracking has gained increasing importance in the field of image guided radiotherapy (IGRT). An essential step for this integration consists of temporally synchronizing the acquisition of X-ray projections and surface data. We present an image-based method for the synchronization of cone-beam computed tomography (CBCT) and optical surface systems, which does not require the use of additional hardware. The method is based on optically tracking the motion of a component of the CBCT/gantry unit, which rotates during the acquisition of the CBCT scan. A calibration procedure was implemented to relate the position of the rotating component identified by the optical system with the time elapsed since the beginning of the CBCT scan, thus obtaining the temporal correspondence between the acquisition of X-ray projections and surface data. The accuracy of the proposed synchronization method was evaluated on a motorized moving phantom, performing eight simultaneous acquisitions with an Elekta Synergy CBCT machine and the AlignRT optical device. The median time difference between the sinusoidal peaks of phantom motion signals extracted from the synchronized CBCT and AlignRT systems ranged between -3.1 and 12.9 msec, with a maximum interquartile range of 14.4 msec. The method was also applied to clinical data acquired from seven lung cancer patients, demonstrating the potential of the proposed approach in estimating the individual and daily variations in respiratory parameters and motion correlation of internal and external structures. The presented synchronization method can be particularly useful for tumor tracking applications in extracranial radiation treatments, especially in the field of patient-specific breathing models, based on the correlation between internal tumor motion and external surface surrogates.Fassi, Aurora; Schaerer, Joël; Riboldi, Marco; Sarrut, David; Baroni, GuidoFassi, Aurora; Schaerer, Joël; Riboldi, Marco; Sarrut, David; Baroni, Guid

IGRT and motion management during lung SBRT delivery.

Patient motion can cause misalignment of the tumour and toxicities to the healthy lung tissue during lung stereotactic body radiation therapy (SBRT). Any deviations from the reference setup can miss the target and have acute toxic effects on the patient with consequences onto its quality of life and survival outcomes. Correction for motion, either immediately prior to treatment or intra-treatment, can be realized with image-guided radiation therapy (IGRT) and motion management devices. The use of these techniques has demonstrated the feasibility of integrating complex technology with clinical linear accelerator to provide a higher standard of care for the patients and increase their quality of life

Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Extracting respiratory signals from thoracic cone beam CT projections

Patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principle component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely, the Amsterdam Shroud (AS) method, the intensity analysis (IA) method, and the Fourier-transform based phase analysis (FT-p) method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting respiratory signal. We also identified the applicability of each existing method.Comment: 21 pages, 11 figures, submitted to Phys. Med. Bio

IMAGE-BASED RESPIRATORY MOTION EXTRACTION AND RESPIRATION-CORRELATED CONE BEAM CT (4D-CBCT) RECONSTRUCTION

Accounting for respiration motion during imaging helps improve targeting precision in radiation therapy. Respiratory motion can be a major source of error in determining the position of thoracic and upper abdominal tumor targets during radiotherapy. Thus, extracting respiratory motion is a key task in radiation therapy planning. Respiration-correlated or four-dimensional CT (4DCT) imaging techniques have been recently integrated into imaging systems for verifying tumor position during treatment and managing respiration-induced tissue motion. The quality of the 4D reconstructed volumes is highly affected by the respiratory signal extracted and the phase sorting method used. This thesis is divided into two parts. In the first part, two image-based respiratory signal extraction methods are proposed and evaluated. Those methods are able to extract the respiratory signals from CBCT images without using external sources, implanted markers or even dependence on any structure in the images such as the diaphragm. The first method, called Local Intensity Feature Tracking (LIFT), extracts the respiratory signal depending on feature points extracted and tracked through the sequence of projections. The second method, called Intensity Flow Dimensionality Reduction (IFDR), detects the respiration signal by computing the optical flow motion of every pixel in each pair of adjacent projections. Then, the motion variance in the optical flow dataset is extracted using linear and non-linear dimensionality reduction techniques to represent a respiratory signal. Experiments conducted on clinical datasets showed that the respiratory signal was successfully extracted using both proposed methods and it correlates well with standard respiratory signals such as diaphragm position and the internal markers’ signal. In the second part of this thesis, 4D-CBCT reconstruction based on different phase sorting techniques is studied. The quality of the 4D reconstructed images is evaluated and compared for different phase sorting methods such as internal markers, external markers and image-based methods (LIFT and IFDR). Also, a method for generating additional projections to be used in 4D-CBCT reconstruction is proposed to reduce the artifacts that result when reconstructing from an insufficient number of projections. Experimental results showed that the feasibility of the proposed method in recovering the edges and reducing the streak artifacts

Non-invasive lung tumor motion estimation and mitigation in real-time during radiation therapy

Die interfraktionelle Bewegung von Lungentumoren während der Applikation von externer Strahlentherapie kann ein limitierender Faktor für den ehandlungserfolg sein. Tumorbewegung kann sowohl eine Unterdosierung des Zielvolumens als auch eine schwerwiegende Überdosierung des umliegenden gesunden Gewebes zur Folge haben. Die vorliegende Arbeit besteht aus drei Teilen. Im ersten Teil wird eine Methode zur bildgestützten Bewegungsabschätzung von Lungentumoren in Echtzeit vorgestellt. Vorteile des Verfahrens sind die Unabhängigkeit von implantierten Markern (Pneumothorax Risiko) und die Vermeidung von zusätzlicher Bildgebungsstahlendosis, da das Verfahren mit dem Therapiestrahl akquirierte fluoroskopische Bildsequenzen nutzt. Die Validierung erfolgte sowohl anhand von Bildern, die mit einem beweglichen Thoraxphantom aufgenommen wurden, als auch anhand von Bildern, die während einer Reihe von Lungenstereotaxiebehandlungen aufgenommen wurden. Der Fehler (rmse) wurde für den Phantomdatensatz zu < 1mm und für den klinischen Datensatz zu (2.1 ± 1.7)mm bestimmt. Der zweite Teil dieser Arbeit befasst sich mit der Implementierung und Charakterisierung eines dynamischen, adaptiven Bestralungsystems, das durch das Nachführen des Therapiestrahls in Echtzeit Bewegungen des Zielvolumens kompensieren kann. Das System nutzt eine computergestützte, dynamisch ansteuerbare Strahlungsapertur (MLC), welche mit der Position des Zielvolumens aus dem ersten Teil in Echtzeit angesteuert wird. Die Latenzzeit wurde gemessen ( 250 ms) und durch einen linearen Vorhersagealgorithmus kompensiert. Das System wurde mit einem mit Lungentumortrajektorien programmierten dynamischen Thoraxphantom getestet. Der Fehler konnte von 2.4mm bis 3.5mm auf unter 1mm reduziert werden. Im dritten Teil dieser Arbeit wird der Algorithmus vom ersten Teil eingesetzt, um nach jeder Fraktion einer Lungenstereotaxiebehandlung die applizierte Dosis zu berechnen. Das Konzept wurde mit einem dynamischen Thoraxphantom validiert. Mit der retrospektiven Bildanalyse einer Lungenstereotaxiebehandlung konnte gezeigt werden, daß sich fraktionelle Unterdosierungen des Zielvolumens, etwa durch nicht optimale Patientenpositionierung, mit dieser Methode in Form eines Dosis-Volumen Histogramms (DVH) quantifizieren lassen

Markerless Lung Tumor Trajectory Estimation from Rotating Cone Beam Computed Tomography Projections

Respiration introduces large tumor motion in the thoracic region which influences treatment outcome for lung cancer patients. Tumor motion management techniques require characterization of temporal tumor motions because tumor motion varies patient to patient, day to day and cycle to cycle. This work develops a markerless algorithm to estimate 3 dimensional (3D) lung-tumor trajectories on free breathing cone beam computed tomography (CBCT) projections, which are 2 dimensional (2D) sequential images rotating about an axis and are used to reconstruct 3D CBCT images. A gold standard tumor trajectory is required to guide the algorithm development and estimate the tumor detection accuracy for markerless tracking algorithms. However, a sufficient strategy to validate markerless tracking algorithms is lacking. A validation framework is developed based on fiducial markers. Markers are segmented and marker trajectories are xiv obtained. The displacement of the tumor to the marker is calculated and added to the segmented marker trajectory to generate reference tumor trajectory. Markerless tumor trajectory estimation (MLTM) algorithm is developed and improved to acquire tumor trajectory with clinical acceptable accuracy for locally advanced lung tumors. The development is separate into two parts. The first part considers none tumor deformation. It investigates shape and appearance of the template, moreover, a constraint method is introduced to narrow down the template matching searching region for more precise matching results. The second part is to accommodate tumor deformation near the end of the treatment. The accuracy of MLTM is calculated and compared against 4D CBCT, which is the current standard of care. In summary, a validation framework based on fiducial markers is successfully built. MLTM is successfully developed with or without the consideration of tumor deformation with promising accuracy. MLTM outperforms 4D CBCT in temporal tumor trajectory estimation

Surrogate-driven motion models from cone-beam CT for motion management in radiotherapy treatments

This thesis details a variety of methods to build a surrogate-driven motion model from a cone-beam CT (CBCT) scan. The methods are intended to form a key constituent of a tracked RT treatment system, by providing a markerless means of tracking tumour and organs at risk (OAR) positions in real-time. The beam can then be adjusted to account for the respiratory motion of the tumour, whilst ensuring no adverse e.ects on the OAR from the adjustment in the beam. An approach to describe an iterative method to markerlessly track the lung tumour region is presented. A motion model is built of the tumour region using the CBCT projections, which then gives tumour position information during treatment. For simulated data, the motion model was able to reduce the mean L2-norm error from 4.1 to 1.0 mm, relative to the mean position. The model was used to account for the motion of an object placed within a respiratory phantom. When used to perform a motion compensated reconstruction (MCR), measured dimensions of this object agreed to within the voxel size (1 mm cube) used for the reconstruction. The method was applied to 6 clinical datasets. Improvements in edge contrast of the tumour were seen, and compared to clinically-derived positions for the tumour centres, the mean absolute errors in superior-inferior directions was reduced to under 2.5 mm. The model is then subsequently extended to monitor both tumour and OAR regions during treatment. This extended approach uses both the planning 4DCT and CBCT scans, focusing on the strengths of each respective dataset. Results are presented on three simulated and three clinical datasets. For the simulated data, maximal L2-norm errors were reduced from 14.8 to 4.86 mm. Improvements in edge contrast in the diaphragm and lung regions were seen in the MCR for the clinical data. A final approach to building a model of the entire patient is then presented, utilising only the CBCT data. An optical-flow-based approach is taken, which is adapted to the unique nature of the CBCT data via some interesting conceptualisations. Results on a simulated case are presented, showing increased edge contrast in the MCR using the fitted motion model. Mean L2-norm errors in the tumour region were reduced from 4.2 to 2.6 mm. Future work is discussed, with a variety of extensions to the methods proposed. With further development, it is hoped that some of the ideas detailed could be translated into the clinic and have a direct impact on patient treatment