Surrogate-driven deformable motion model for organ motion tracking in particle radiation therapy (original) (raw)
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International journal of radiation oncology, biology, physics
To develop a tumor tracking method based on a surrogate-driven motion model, which provides noninvasive dynamic localization of extracranial targets for the compensation of respiration-induced intrafraction motion in high-precision radiation therapy. The proposed approach is based on a patient-specific breathing motion model, derived a priori from 4-dimensional planning computed tomography (CT) images. Model parameters (respiratory baseline, amplitude, and phase) are retrieved and updated at each treatment fraction according to in-room radiography acquisition and optical surface imaging. The baseline parameter is adapted to the interfraction variations obtained from the daily cone beam (CB) CT scan. The respiratory amplitude and phase are extracted from an external breathing surrogate, estimated from the displacement of the patient thoracoabdominal surface, acquired with a noninvasive surface imaging device. The developed method was tested on a database of 7 lung cancer patients, in...
A patient-specific respiratory model of anatomical motion for radiation treatment planning
Medical Physics, 2007
Modeling of respiratory motion is important for a more accurate understanding and accounting of its effect on dose to cancers in the thorax and abdomen by radiotherapy. We have developed a model of respiration-induced organ motion in the thorax, without the commonly adopted assumption of repeatable breath cycles. The model describes the motion of a volume of interest within the patient, based on a reference 3-dimensional image (at end-expiration), and the diaphragm positions at different time points. The input data are respiration-correlated CT images of patients treated for nonsmall cell lung cancer, consisting of 3D images, including the diaphragm positions, at 10 phases of the respiratory cycle. A deformable image registration algorithm calculates the deformation field that maps each 3D image to the reference 3D image. A principle component analysis is performed to parameterize the 3D deformation field in terms of the diaphragm motion. We show that the first two principal components are adequate to accurately and completely describe the organ motion in the data of 4 patients. Artifacts in the RCCT images that commonly occur at the mid-respiration states are reduced in the model-generated images. Further validation of the model is demonstrated in the successful application of the parameterized 3D deformation field to RCCT data of the same patient but acquired several days later. We have developed a method for predicting respiration-induced organ motion in patients that has potential for improving the accuracy of dose calculation in radiotherapy.
A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy
Medical Physics, 2006
Respiratory motion causes errors when planning and delivering radiotherapy treatment to lung cancer patients. To reduce these errors, methods of acquiring and using four-dimensional computed tomography ͑4DCT͒ datasets have been developed. We have developed a novel method of constructing computational motion models from 4DCT. The motion models attempt to describe an average respiratory cycle, which reduces the effects of variation between different cycles. They require substantially less memory than a 4DCT dataset, are continuous in space and time, and facilitate automatic target propagation and combining of doses over the respiratory cycle. The motion models are constructed from CT data acquired in cine mode while the patient is free breathing ͑free breathing CT -FBCT͒. A "slab" of data is acquired at each couch position, with 3-4 contiguous slabs being acquired per patient. For each slab a sequence of 20 or 30 volumes was acquired over 20 seconds. A respiratory signal is simultaneously recorded in order to calculate the position in the respiratory cycle for each FBCT. Additionally, a high quality reference CT volume is acquired at breath hold. The reference volume is nonrigidly registered to each of the FBCT volumes. A motion model is then constructed for each slab by temporally fitting the nonrigid registration results. The value of each of the registration parameters is related to the position in the respiratory cycle by fitting an approximating B spline to the registration results. As an approximating function is used, and the data is acquired over several respiratory cycles, the function should model an average respiratory cycle. This can then be used to calculate the value of each degree of freedom at any desired position in the respiratory cycle. The resulting nonrigid transformation will deform the reference volume to predict the contents of the slab at the desired position in the respiratory cycle. The slab model predictions are then concatenated to produce a combined prediction over the entire region of interest. We have performed a number of experiments to assess the accuracy of the nonrigid registration results and the motion model predictions. The individual slab models were evaluated by expert visual assessment and the tracking of easily identifiable anatomical points. The combined models were evaluated by calculating the discontinuities between the transformations at the slab boundaries. The experiments were performed on five patients with a total of 18 slabs between them. For the point tracking experiments, the mean distance between where a clinician manually identified a point and where the registration results located the point, the target registration error ͑TRE͒, was 1.3 mm. The mean distance between a manually identified point and the models prediction of the point's location, the target model error ͑TME͒, was 1.6 mm. The mean discontinuity between model predictions at the slab boundaries, the Continuity Error, was 2.2 mm. The results show that the motion models perform with a level of accuracy comparable to the slice thickness of 1.5 mm.
Iranian Journal of Radiation Research, 2012
The aim of this work was to study the feasibility of constructing a fast thorax model suitable for simulating lung motion due to respiration using only one CT dataset. Materials and Methods: For each of six patients with different thorax sizes, two sets of CT images were obtained in single-breath-hold inhale and exhale stages in the supine position. The CT images were then analyzed by measurements of the displacements due to respiration in the thorax region. Lung and thorax were 3D reconstructed and then transferred to the ABAQUS software for biomechanical fast finite element (FFE) modeling. The FFE model parameters were tuned based on three of the patients, and then was tested in a predictive mode for the remaining patients to predict lung and thorax motion and deformation following respiration. Results: Starting from end-exhale stage, the model, tuned for a patient created lung wall motion at end-inhale stage that matched the measurements for that patient within 1 mm (its limit of accuracy). In the predictive mode, the mean discrepancy between the imaged landmarks and those predicted by the model (formed from averaged data of two patients) was 4.2 mm. The average computation time in the fast predictive mode was 89 sec. Conclusion: Fast prediction of approximate, lung and thorax shapes in the respiratory cycle has been feasible due to the linear elastic material approximation, used in the FFE model. Iran.
Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking
Medical Physics, 2005
Four-dimensional ͑4D͒ radiotherapy is the explicit inclusion of the temporal changes in anatomy during the imaging, planning, and delivery of radiotherapy. Temporal anatomic changes can occur for many reasons, though the focus of the current investigation is respiration motion for lung tumors. The aim of this study was to develop 4D radiotherapy treatment-planning methodology for DMLC-based respiratory motion tracking. A 4D computed tomography ͑CT͒ scan consisting of a series of eight 3D CT image sets acquired at different respiratory phases was used for treatment planning. Deformable image registration was performed to map each CT set from the peak-inhale respiration phase to the CT image sets corresponding to subsequent respiration phases. Deformable registration allows the contours defined on the peak-inhale CT to be automatically transferred to the other respiratory phase CT image sets. Treatment planning was simultaneously performed on each of the eight 3D image sets via automated scripts in which the MLC-defined beam aperture conforms to the PTV ͑which in this case equaled the GTV due to CT scan length limitations͒ plus a penumbral margin at each respiratory phase. The dose distribution from each respiratory phase CT image set was mapped back to the peak-inhale CT image set for analysis. The treatment intent of 4D planning is that the radiation beam defined by the DMLC tracks the respiration-induced target motion based on a feedback loop including the respiration signal to a real-time MLC controller. Deformation with respiration was observed for the lung tumor and normal tissues. This deformation was verified by examining the mapping of high contrast objects, such as the lungs and cord, between image sets. For the test case, dosimetric reductions for the cord, heart, and lungs were found for 4D planning compared with 3D planning. 4D radiotherapy planning for DMLC-based respiratory motion tracking is feasible and may offer tumor dose escalation and/or a reduction in treatment-related complications. However, 4D planning requires new planning tools, such as deformable registration and automated treatment planning on multiple CT image sets.
Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy
2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), 2016
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
Novel breathing motion model for radiotherapy
International Journal of …, 2005
Purpose: An accurate model of breathing motion under quiet respiration is desirable to obtain the most accurate and conformal dose distributions for mobile lung cancer lesions. On the basis of recent lung motion measurements and the physiologic functioning of the lungs, we have determined that the motion of lung and lung tumor tissues can be modeled as a function of five degrees of freedom, the position of the tissues at a user-specified reference breathing phase, tidal volume and its temporal derivative airflow (tidal volume phase space). Time is an implicit variable in this model. Methods and Materials: To test this hypothesis, a mathematical model of motion was developed that described the motion of objects p in the lungs as linear functions of tidal volume and airflow. The position of an object was described relative to its position P ជ 0 at the reference tidal volume and zero airflow, and the motion of the object was referenced to this position. Hysteresis behavior was hypothesized to be caused by pressure imbalances in the lung during breathing and was, in this model, a function of airflow. The motion was modeled as independent tidal volume and airflow displacement vectors, with the position of the object at time t equal to the vector sum r ជ P (t) ϭ r ជ v (t) ϩ r ជ f (t) where r ជ v (t) and r ជ f (t) were displacement vectors with magnitudes approximated by linear functions of the tidal volume and airflow. To test this model, we analyzed five-dimensional CT scans (CT scans acquired with simultaneous real-time monitoring of the tidal volume) of 4 patients. The scans were acquired throughout the lungs, but the trajectories were analyzed in the couch positions near the diaphragm. A template-matching algorithm was implemented to identify the positions of the points throughout the 15 scans. In total, 76 points throughout the 4 patients were tracked. The lateral motion of these points was minimal; thus, the model was described in two spatial dimensions, with a total of six parameters necessary to describe the 30 degrees of freedom inherent in the 15 positions. Results: For the 76 evaluated points, the average discrepancy (the distance between the measured and prediction positions) of the 15 locations for each tracked point was 0.75 ؎ 0.25 mm, with an average maximal discrepancy of 1.55 ؎ 0.54 mm. The average discrepancy was also tabulated as a fraction of the breathing motion. Discrepancies of <10% and 15% of the overall motion occurred in 73% and 95% of the tracked points, respectively. Conclusion: The motion tracking algorithms are being improved and automated to provide more motion data to test the models. This may allow a measurement of the motion-fitting parameters throughout the lungs. If the parameters vary smoothly, interpolation may be possible, yielding a continuous mathematical model of the breathing motion throughout the lungs. The utility of the model will depend on its stability as a function of time. If the model is only robust during the measurement session, it may be useful for determining lung function. If it is robust for weeks, it may be useful for treatment planning and gating of lung treatments. The use of tidal volume phase space for characterizing breathing motion appears to have provided, for the first time, the potential for a patient-specific mathematical model of breathing motion.
Simulating lung tumor motion for dynamic tumor-tracking irradiation
2007 IEEE Nuclear Science Symposium Conference Record, 2007
This study proposes methods for the support of radiotherapy planning for dynamic tumor-tracking irradiation for lung tumors. It aims to simulate the deformation of the lung caused by respiration and to visualize the result as DRRs (Digitally Reconstructed Radiographs) in real time. Our lungdeformation model treats the lung as an elastic object and analyzes the deformation based on linear FEM (the Finite Element Method). The simulation models the lung using CT volume data and generates a model with boundary conditions with freely adjustable regions, displacements, and phases. The doctor planning the radiotherapy can reproduce the movement of the lung tumor by freely adjusting the regions, displacements, and phases of the boundary conditions while comparing the position of the lung tumor in an X-ray photograph. For highspeed display we propose a method for rapid-generation DRRs by slice-based volume rendering. The result of several functional evaluations and trials of simulation established that the proposed method can describe the movement of the lung tumor with adequate precision. The developed system is expected to be useful for radiotherapy planning for real-time tumor-pursuing irradiation.
4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling
Medical Physics, 2008
Four-dimensional computed tomography ͑4D-CT͒ imaging technology has been developed for radiation therapy to provide tumor and organ images at the different breathing phases. In this work, a procedure is proposed for estimating and modeling the respiratory motion field from acquired 4D-CT imaging data and predicting tissue motion at the different breathing phases. The 4D-CT image data consist of series of multislice CT volume segments acquired in ciné mode. A modified optical flow deformable image registration algorithm is used to compute the image motion from the CT segments to a common full volume 3D-CT reference. This reference volume is reconstructed using the acquired 4D-CT data at the end-of-exhalation phase. The segments are optimally aligned to the reference volume according to a proposed a priori alignment procedure. The registration is applied using a multigrid approach and a feature-preserving image downsampling maxfilter to achieve better computational speed and higher registration accuracy. The registration accuracy is about 1.1Ϯ 0.8 mm for the lung region according to our verification using manually selected landmarks and artificially deformed CT volumes. The estimated motion fields are fitted to two 5D ͑spatial 3D + tidal volume+ airflow rate͒ motion models: forward model and inverse model. The forward model predicts tissue movements and the inverse model predicts CT density changes as a function of tidal volume and airflow rate. A leave-one-out procedure is used to validate these motion models. The estimated modeling prediction errors are about 0.3 mm for the forward model and 0.4 mm for the inverse model.