543 research outputs found
Predicting respiratory motion for real-time tumour tracking in radiotherapy
Purpose. Radiation therapy is a local treatment aimed at cells in and around
a tumor. The goal of this study is to develop an algorithmic solution for
predicting the position of a target in 3D in real time, aiming for the short
fixed calibration time for each patient at the beginning of the procedure.
Accurate predictions of lung tumor motion are expected to improve the precision
of radiation treatment by controlling the position of a couch or a beam in
order to compensate for respiratory motion during radiation treatment.
Methods. For developing the algorithmic solution, data mining techniques are
used. A model form from the family of exponential smoothing is assumed, and the
model parameters are fitted by minimizing the absolute disposition error, and
the fluctuations of the prediction signal (jitter). The predictive performance
is evaluated retrospectively on clinical datasets capturing different behavior
(being quiet, talking, laughing), and validated in real-time on a prototype
system with respiratory motion imitation.
Results. An algorithmic solution for respiratory motion prediction (called
ExSmi) is designed. ExSmi achieves good accuracy of prediction (error
mm/s) with acceptable jitter values (5-7 mm/s), as tested on out-of-sample
data. The datasets, the code for algorithms and the experiments are openly
available for research purposes on a dedicated website.
Conclusions. The developed algorithmic solution performs well to be
prototyped and deployed in applications of radiotherapy
Biomechanical Modeling for Lung Tumor Motion Prediction during Brachytherapy and Radiotherapy
A novel technique is proposed to develop a biomechanical model for estimating lung’s tumor position as a function of respiration cycle time. Continuous tumor motion is a major challenge in lung cancer treatment techniques where the tumor needs to be targeted; e.g. in external beam radiotherapy and brachytherapy. If not accounted for, this motion leads to areas of radiation over and/or under dosage for normal tissue and tumors. In this thesis, biomechanical models were developed for lung tumor motion predication in two distinct cases of lung brachytherapy and lung external beam radiotherapy. The lung and other relevant surrounding organs geometries, loading, boundary conditions and mechanical properties were considered and incorporated properly for each case. While using material model with constant incompressibility is sufficient to model the lung tissue in the brachytherapy case, in external beam radiation therapy the tissue incompressibility varies significantly due to normal breathing. One of the main issues tackled in this research is characterizing lung tissue incompressibility variations and measuring its corresponding parameters as a function of respiration cycle time. Results obtained from an ex-vivo porcine deflated lung indicated feasibility and reliability of using the developed biomechanical model to predict tumor motion during brachytherapy. For external beam radiotherapy, in-silico studies indicated very significant impact of considering the lung tissue incompressibility on the accuracy of predicting tumor motion. Furthermore, ex-vivo porcine lung experiments demonstrated the capability and reliability of the proposed approach for predicting tumor motion as a function of cyclic time. As such, the proposed models have a good potential to be incorporated effectively in computer assisted lung radiotherapy treatment systems
A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)
Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data.
To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data.
The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified
Advances in real-time thoracic guidance systems
Substantial tissue motion: \u3e1cm) arises in the thoracic/abdominal cavity due to respiration. There are many clinical applications in which localizing tissue with high accuracy: \u3c1mm) is important. Potential applications include radiation therapy, radio frequency ablation, lung/liver biopsies, and brachytherapy seed placement. Recent efforts have made highly accurate sub-mm 3D localization of discrete points available via electromagnetic: EM) position monitoring. Technology from Calypso Medical allows for simultaneous tracking of up to three implanted wireless transponders. Additionally, Medtronic Navigation uses wired electromagnetic tracking to guide surgical tools for image guided surgery: IGS). Utilizing real-time EM position monitoring, a prototype system was developed to guide a therapeutic linear accelerator to follow a moving target: tumor) within the lung/abdomen. In a clinical setting, electromagnetic transponders would be bronchoscopically implanted into the lung of the patient in or near the tumor. These transponders would ax to the lung tissue in a stable manner and allow real-time position knowledge throughout a course of radiation therapy. During each dose of radiation, the beam is either halted when the target is outside of a given threshold, or in a later study the beam follows the target in real-time based on the EM position monitoring. We present quantitative analysis of the accuracy and efficiency of the radiation therapy tumor tracking system. EM tracking shows promise for IGS applications. Tracking the position of the instrument tip allows for minimally invasive intervention and alleviates the trauma associated with conventional surgery. Current clinical IGS implementations are limited to static targets: e.g. craniospinal, neurological, and orthopedic intervention. We present work on the development of a respiratory correlated image guided surgery: RCIGS) system. In the RCIGS system, target positions are modeled via respiratory correlated imaging: 4DCT) coupled with a breathing surrogate representative of the patient\u27s respiratory phase/amplitude. Once the target position is known with respect to the surrogate, intervention can be performed when the target is in the correct location. The RCIGS system consists of imaging techniques and custom developed software to give visual and auditory feedback to the surgeon indicating both the proper location and time for intervention. Presented here are the details of the IGS lung system along with quantitative results of the system accuracy in motion phantom, ex-vivo porcine lung, and human cadaver environments
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
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