Towards Non-Invasive Lung Tumor Tracking Based on Patient Specific Model of Respiratory System (original) (raw)
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Physics in medicine and biology, 2015
Finite element analysis (FEA)-based biomechanical modeling can be used to predict lung respiratory motion. In this technique, elastic models and biomechanical parameters are two important factors that determine modeling accuracy. We systematically evaluated the effects of lung and lung tumor biomechanical modeling approaches and related parameters to improve the accuracy of motion simulation of lung tumor center of mass (TCM) displacements. Experiments were conducted with four-dimensional computed tomography (4D-CT). A Quasi-Newton FEA was performed to simulate lung and related tumor displacements between end-expiration (phase 50%) and other respiration phases (0%, 10%, 20%, 30%, and 40%). Both linear isotropic and non-linear hyperelastic materials, including the neo-Hookean compressible and uncoupled Mooney-Rivlin models, were used to create a finite element model (FEM) of lung and tumors. Lung surface displacement vector fields (SDVFs) were obtained by registering the 50% phase CT...
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.
2015
Abstract: Tumour motion is an essential source of error for treatment planning in radiation therapy. This motion is mostly due to patient respiration. To account for tumour motion, we propose a solution that is based on the biomechanical modelling of the respiratory system. To compute deformations and displacements, we use continuous mechanics laws solved with the finite element method. In this paper, we propose a preliminary study of a complete model of the respiratory system including lungs, chest wall and a simple model of the diaphragm. This feasibility study is achieved by using the data of a “virtual patient”. Results are in accordance with the anatomic reality, showing the feasibility of a complete model of the respiratory system. 1
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.
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.
Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 2015
Lung Cancer is the leading cause of cancer death in both men and women. Among various treatment methods currently being used in the clinic, External Beam Radiation Therapy (EBRT) is used widely not only as the primary treatment method, but also in combination with chemotherapy and surgery. However, this method may lack desirable dosimetric accuracy because of respiration induced tumor motion. Recently, biomechanical modeling of the respiratory system has become a popular approach for tumor motion prediction and compensation. This approach requires reasonably accurate data pertaining to thoracic pressure variation, diaphragm position and biomechanical properties of the lung tissue in order to predict the lung tissue deformation and tumor motion. In this paper, we present preliminary results of an in vivo study obtained from a Finite Element Model (FEM) of the lung developed to predict tumor motion during respiration.
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.
Medical Physics, 2010
Purpose: Predicting complex patterns of respiration can benefit the management of the respiratory motion for radiation therapy of lung cancer. The purpose of the present work was to develop a patient-specific, physiologically relevant respiratory motion model which is capable of predicting lung tumor motion over a complete normal breathing cycle. Methods: Currently employed techniques for generating the lung geometry from four-dimensional computed tomography data tend to lose details of mesh topology due to excessive surface smoothening. Some of the existing models apply displacement boundary conditions instead of the intrapleural pressure as the actual motive force for respiration, while others ignore the nonlinearity of lung tissues or the mechanics of pleural sliding. An intermediate nonuniform rational basis spline surface representation is used to avoid multiple geometric smoothing procedures used in the computational mesh preparation. Measured chest pressure-volume relationships are used to simulate pressure loading on the surface of the model for a given lung volume, as in actual breathing. A hyperelastic model, developed from experimental observations, has been used to model the lung tissue material. Pleural sliding on the inside of the ribcage has also been considered.
Quantification of Lung Tumor Motion and Optimization of Treatment
Journal of biomedical physics & engineering, 2023
Background: Mobility of lung tumors is induced by respiration and causes inadequate dose coverage. Objective: This study quantified lung tumor motion, velocity, and stability for small (≤5 cm) and large (>5 cm) tumors to adapt radiation therapy techniques for lung cancer patients. Material and Methods: In this retrospective study, 70 patients with lung cancer were included that 50 and 20 patients had a small and large gross tumor volume (GTV). To quantify the tumor motion and velocity in the upper lobe (UL) and lower lobe (LL) for the central region (CR) and a peripheral region (PR), the GTV was contoured in all ten respiratory phases, using 4D-CT. Results: The amplitude of tumor motion was greater in the LL, with motion in the superior-inferior (SI) direction compared to the UL, with an elliptical motion for small and large tumors. Tumor motion was greater in the CR, rather than in the PR, by 63% and 49% in the UL compared to 50% and 38% in the LL, for the left and right lung. The maximum tumor velocity for a small GTV was 44.1 mm/s in the LL (CR), decreased to 4 mm/s for both ULs (PR), and a large GTV ranged from 0.4 to 9.4 mm/s. Conclusion: The tumor motion and velocity depend on the tumor localization and the greater motion was in the CR for both lobes due to heart contribution. The tumor velocity and stability can help select the best technique for motion management during radiation therapy.