A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy (original) (raw)
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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.
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.
Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs
Medical Physics, 2011
Purpose: Four-dimensional computed tomography (4D CT) can provide 2 patient-specific motion information for radiotherapy planning and deliv-3 ery. Motion estimation in 4D CT is challenging due to the reduced image 4 quality and the presence of artifacts. We aim to improve the robustness 5 of deformable registration applied to respiratory-correlated imaging of the 6 lungs, by using a global problem formulation and pursuing a restrictive 7 parametrization for the spatio-temporal deformation model. 8
Medical Physics, 2008
In this article, our goal is twofold. First, we propose and compare two methods which process deformable registration to estimate patient specific lung and tumor displacements and deformation during free breathing from four-dimensional computed tomography ͑4D-CT͒ data. Second, we propose techniques to quantify the physiological parameters of motion nonlinearity and hysteresis. A Fréchet distance-based criterion is introduced to measure the motion hysteresis. Experiments were conducted with 4D-CT data of five patients treated in radiotherapy for non-small cell lung cancer. The accuracy of deformation fields assessed against expert-selected landmarks was found to be within image voxel tolerance. The second method gave slightly better results in terms of accuracy and consistency, although the differences were not statistically significant between the two methods. Lung motion nonlinearity and hysteresis are patient specific, and vary across regions within the lung during respiration. For all patients, motion between end-exhale and end-inhale was well approximated with a straight line trajectory for the majority of lung points. Hysteresis was found to be globally correlated with trajectory length. The main limitation to the proposed method is that intensity-based deformable registration is dependent on image quality and image resolution. Another limitation is the absence of gold standard which makes the validation of the computed motion difficult. However, the proposed tools provide patient specific motion information which, in radiotherapy for example, can ease the definition of precise internal margins. In the future, the integration of physiological information from multiple patients could help to create a general lung atlas with different clinical applications.
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 2006: Image Processing, 2006
Respiratory motion is a significant source of error in radiotherapy treatment planning. 4D-CT data sets can be useful to measure the impact of organ motion caused by breathing. But modern CT scanners can only scan a limited region of the body simultaneously and patients have to be scanned in segments consisting of multiple slices. For studying free breathing motion multislice CT scans can be collected simultaneously with digital spirometry over several breathing cycles. The 4D data set is assembled by sorting the free breathing multislice CT scans according to the couch position and the tidal volume. But artifacts can occur because there are no data segments for exactly the same tidal volume and all couch positions. We present an optical flow based method for the reconstruction of 4D-CT data sets from multislice CT scans, which are collected simultaneously with digital spirometry. The optical flow between the scans is estimated by a non-linear registration method. The calculated velocity field is used to reconstruct a 4D-CT data set by interpolating data at user-defined tidal volumes. By this technique, artifacts can be reduced significantly. The reconstructed 4D-CT data sets are used for studying inner organ motion during the respiratory cycle. The procedures described were applied to reconstruct 4D-CT data sets for four tumour patients who have been scanned during free breathing. The reconstructed 4D data sets were used to quantify organ displacements and to visualize the abdominothoracic organ motion.
Medical Physics, 2006
Purpose: We propose to simulate an artificial four-dimensional ͑4-D͒ CT image of the thorax during breathing. It is performed by deformable registration of two CT scans acquired at inhale and exhale breath-hold. Materials and methods: Breath-hold images were acquired with the ABC ͑Active Breathing Coordinator͒ system. Dense deformable registrations were performed. The method was a minimization of the sum of squared differences ͑SSD͒ using an approximated second-order gradient. Gaussian and linear-elastic vector field regularizations were compared. A new preprocessing step, called a priori lung density modification ͑APLDM͒, was proposed to take into account lung density changes due to inspiration. It consisted of modulating the lung densities in one image according to the densities in the other, in order to make them comparable. Simulated 4-D images were then built by vector field interpolation and image resampling of the two initial CT images. A variation in the lung density was taken into account to generate intermediate artificial CT images. The Jacobian of the deformation was used to compute voxel values in Hounsfield units. The accuracy of the deformable registration was assessed by the spatial correspondence of anatomic landmarks located by experts. Results: APLDM produced statistically significantly better results than the reference method ͑registration without APLDM preprocessing͒. The mean ͑and standard deviation͒ of distances between automatically found landmark positions and landmarks set by experts were 2.7͑1.1͒ mm with APLDM, and 6.3͑3.8͒ mm without. Interexpert variability was 2.3͑1.2͒ mm. The differences between Gaussian and linear elastic regularizations were not statistically significant. In the second experiment using 4-D images, the mean difference between automatic and manual landmark positions for intermediate CT images was 2.6͑2.0͒ mm. Conclusion: The generation of 4-D CT images by deformable registration of inhale and exhale CT images is feasible. This can lower the dose needed for 4-D CT acquisitions or can help to correct 4-D acquisition artifacts. The 4-D CT model can be used to propagate contours, to compute a 4-D dose map, or to simulate CT acquisitions with an irregular breathing signal. It could serve as a basis for 4-D radiation therapy planning. Further work is needed to make the simulation more realistic by taking into account hysteresis and more complex voxel trajectories.
Medical Physics, 2007
Breathing motion is one of the major limiting factors for reducing dose and irradiation of normal tissue for conventional conformal radiotherapy. This paper describes a relationship between tracking lung motion using spirometry data and image registration of consecutive CT image volumes collected from a multislice CT scanner over multiple breathing periods. Temporal CT sequences from 5 individuals were analyzed in this study. The couch was moved from 11 to 14 different positions to image the entire lung. At each couch position, 15 image volumes were collected over approximately 3 breathing periods. It is assumed that the expansion and contraction of lung tissue can be modeled as an elastic material. Furthermore, it is assumed that the deformation of the lung is small over one-fifth of a breathing period and therefore the motion of the lung can be adequately modeled using a small deformation linear elastic model. The small deformation inverse consistent linear elastic image registration algorithm is therefore well suited for this problem and was used to register consecutive image scans. The pointwise expansion and compression of lung tissue was measured by computing the Jacobian of the transformations used to register the images. The logarithm of the Jacobian was computed so that expansion and compression of the lung were scaled equally. The log-Jacobian was computed at each voxel in the volume to produce a map of the local expansion and compression of the lung during the breathing period. These log-Jacobian images demonstrate that the lung does not expand uniformly during the breathing period, but rather expands and contracts locally at different rates during inhalation and exhalation. The log-Jacobian numbers were averaged over a cross section of the lung to produce an estimate of the average expansion or compression from one time point to the next and compared to the air flow rate measured by spirometry. In four out of five individuals, the average log-Jacobian value and the air flow rate correlated well ͑R 2 = 0.858 on average for the entire lung͒. The correlation for the fifth individual was not as good ͑R 2 = 0.377 on average for the entire lung͒ and can be explained by the small variation in tidal volume for this individual. The correlation of the average log-Jacobian value and the air flow rate for images near the diaphragm correlated well in all five individuals ͑R 2 = 0.943 on average͒. These preliminary results indicate a strong correlation between the expansion/ compression of the lung measured by image registration and the air flow rate measured by spirometry. Predicting the location, motion, and compression/expansion of the tumor and normal tissue using image registration and spirometry could have many important benefits for radiotherapy treatment. These benefits include reducing radiation dose to normal tissue, maximizing dose to the tumor, improving patient care, reducing treatment cost, and increasing patient throughput.
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.