Motion Challenge of Thoracic Tumors at Radiotherapy by Introducing an Available Compensation Strategy

In this chapter a description is explained about radiotherapy as common available method in treatment of thoracic tumors located at thorax region of patient body and move mainly due to respiration. In radiotherapy of dynamic tumors, the correct and accurate information of tumor position during the therapeutic irradiation determine the degree of treatment success. In this chapter we investigate quantitatively the effect of tumor motion on treatment quality by considering to possible drawbacks and errors at external surrogate’s radiotherapy as clinical treatment modality. For this aim, tumor motion information of a group of real patients treated with Cybeknife Synchrony system (from Georgetown University Hospital) was taken into account. A fuzzy logic based correlation model was employed for tumor motion tracking. Final results represent graphically the amount of tumor motion estimated by our utilized correlation model on three dimensions with targeting error calculation. It’s worth mentioning that each strategy that can improve targeting accuracy of dynamic tumors may strongly enhance treatment quality by saving healthy tissues against additional high dose. In this chapter we just tried to introduce readers with thoracic tumor motion error as challenging issue in radiotherapy and motion compensation solutions, implemented clinically up to now

Calculation of Inter- and Intra-Fraction Motion Errors at External Radiotherapy Using a Markerless Strategy Based on Image Registration Combined with Correlation Model

Introduction: A new method based on image registration technique and an intelligent correlation model to calculate. The present study aimed to propose inter- and intra-fraction motion errors in order to address the limitations of conventional Patient positioning methods. Material and Methods: The configuration of the markerless method was accomplished by using four-dimensional computed tomography (4DCT) datasets. Firstly, the MeVisLab software package was used to extract a three-dimensional (3D) surface model of the patient and determine the tumor location. Then, the patient-specific 3D surface model which also included the breathing phases was imported into the MATLAB software package in order to define several control points on the thorax region as virtual external markers. Finally, based on the correlation of breathing signals/patient position with breathing signals/tumor coordinate, an adaptive neuro fuzzy inference system was proposed to both verify and align the inter- and intra-fraction motion errors in radiotherapy, if needed. In order to validate the proposed method, the 4DCT data acquired from four real patients was considered. Results: Final results revealed that our hybrid configuration method was capable of aligning patient setup with lower uncertainties, compared to other available methods. In addition, the 3D root-mean-square error has been reduced from 5.26 to 1.5 mm for all patients. Conclusion: In this study, a markerless method based on the image registration technique in combination with a correlation model was proposed to address the limitations of the available methods, including dependence on operator’s attention, use of passive markers, and rigid-only constraint for patient setup

Investigation the motion data clustering of lung tumor on its position estimation at external surrogates’ radiotherapy

Among thorax tumors, lung tumors move mainly due to respiration. In order to enhance the precision of radiotherapy, one solution is estimating tumor motion from external motion of chest wall and abdomen regions. For this aim, consistent prediction models are constructed and then implemented for real time tumor motion tracking. In these models, clustering of database extracted from tumor motion and chest wall motion has non-negligible effect which has been taken into account in this work. In this investigation, motion database of fifteen patients with lung cancer who were treated by means of Cyberknife Synchrony system at Georgetown University hospital, has been used. Two subtractive and fuzzy C-means as common available clustering strategies have been employed in order to investigate their quantitative effects, in a comparative fashion. Final analyzed results show that the average targeting error of prediction models (difference between tumor position estimated by model and actual position of tumor) over all patients are 6.5 and 7.5 mm implementing subtractive and fuzzy C-means clustering, respectively. Moreover, using fuzzy C-means algorithm, tumor tracking is done with more stability. Since, breathing phenomena has high degree of variations, motion data clustering has an important role on the accuracy of prediction model performance by determining model parameters while constructing at pre-treatment step and while updating the model during the treatment

A Simulation Study on Patient Setup Errors in External Beam Radiotherapy Using an Anthropomorphic 4D Phantom

Introduction Patient set-up optimization is required in radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. In this study, we aimed to develop a new method based on neural network to estimate patient geometrical setup using 4-dimensional (4D) XCAT anthropomorphic phantom. Materials and Methods To access 4D modeling of motion of dynamic organs, a phantom employs non-uniform rational B-splines (NURBS)-based Cardiac-Torso method with spline-based model to generate 4D computed tomography (CT) images. First, to generate all the possible roto-translation positions, the 4D CT images were imported to Medical Image Data Examiner (AMIDE). Then, for automatic, real time verification of geometrical setup, an artificial neural network (ANN) was proposed to estimate patient displacement, using training sets. Moreover, three external motion markers were synchronized with a patient couch position as reference points. In addition, the technique was validated through simulated activities by using reference 4D CT data acquired from five patients. Results The results indicated that patient geometrical set-up is highly depended on the comprehensiveness of training set. By using ANN model, the average patient setup error in XCAT phantom was reduced from 17.26 mm to 0.50 mm. In addition, in the five real patients, these average errors were decreased from 18.26 mm to 1.48 mm various breathing phases ranging from inhalation to exhalation were taken into account for patient setup. Uncertainty error assessment and different setup errors were obtained from each respiration phase. Conclusion This study proposed a new method for alignment of patient setup error using ANN model. Additionally, our correlation model (ANN) could estimate true patient position with less error

CT-Based Brachytherapy Treatment Planning using Monte Carlo Simulation Aided by an Interface Software

Introduction: In brachytherapy, radioactive sources are placed close to the tumor, therefore, small changes in their positions can cause large changes in the dose distribution. This emphasizes the need for computerized treatment planning. The usual method for treatment planning of cervix brachytherapy uses conventional radiographs in the Manchester system. Nowadays, because of their advantages in locating the source positions and the surrounding tissues, CT and MRI images are replacing conventional radiographs. In this study, we used CT images in Monte Carlo based dose calculation for brachytherapy treatment planning, using an interface software to create the geometry file required in the MCNP code. The aim of using the interface software is to facilitate and speed up the geometry set-up for simulations based on the patient’s anatomy. This paper examines the feasibility of this method in cervix brachytherapy and assesses its accuracy and speed. Material and Methods: For dosimetric measurements regarding the treatment plan, a pelvic phantom was made from polyethylene in which the treatment applicators could be placed. For simulations using CT images, the phantom was scanned at 120 kVp. Using an interface software written in MATLAB, the CT images were converted into MCNP input file and the simulation was then performed. Results: Using the interface software, preparation time for the simulations of the applicator and surrounding structures was approximately 3 minutes; the corresponding time needed in the conventional MCNP geometry entry being approximately 1 hour. The discrepancy in the simulated and measured doses to point A was 1.7% of the prescribed dose.  The corresponding dose differences between the two methods in rectum and bladder were 3.0% and 3.7% of the prescribed dose, respectively. Comparing the results of simulation using the interface software with those of simulation using the standard MCNP geometry entry showed a less than 1% difference of the prescribed dose at 67% of the studied points (minimu

An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates

In the radiation treatment of moving targets with external surrogates, information on tumor position in real time can be extracted by using accurate correlation models. A fuzzy environment is proposed here to correlate input surrogate data with tumor motion estimates in real time. In this study, two different data clustering approaches were analyzed due to their substantial effects on the fuzzy modeler performance. Moreover, a comparative investigation was performed on two fuzzy-based and one neuro-fuzzy–based inference systems with respect to state-of-the-art models. Finally, due to the intrinsic interpatient variability in fuzzy models’ performance, a model selectivity algorithm was proposed to select an adaptive fuzzy modeler on a case-by-case basis. The performance of multiple and adaptive fuzzy logic models were retrospectively tested in 20 patients treated with CyberKnife real-time tumor tracking. Final results show that activating adequate model selection of our fuzzy-based modeler can significantly reduce tumor tracking errors