Reconstruction of implanted marker trajectories from cone-beam CT projection images using interdimensional correlation modeling

PURPOSE: Cone-beam CT (CBCT) is a widely used imaging modality for image-guided radiotherapy. Most vendors provide CBCT systems that are mounted on a linac gantry. Thus, CBCT can be used to estimate the actual 3-dimensional (3D) position of moving respiratory targets in the thoracic/abdominal region using 2D projection images. The authors have developed a method for estimating the 3D trajectory of respiratory-induced target motion from CBCT projection images using interdimensional correlation modeling. METHODS: Because the superior-inferior (SI) motion of a target can be easily analyzed on projection images of a gantry-mounted CBCT system, the authors investigated the interdimensional correlation of the SI motion with left-right and anterior-posterior (AP) movements while the gantry is rotating. A simple linear model and a state-augmented model were implemented and applied to the interdimensional correlation analysis, and their performance was compared. The parameters of the interdimensional correlation models were determined by least-square estimation of the 2D error between the actual and estimated projected target position. The method was validated using 160 3D tumor trajectories from 46 thoracic/abdominal cancer patients obtained during CyberKnife treatment. The authors' simulations assumed two application scenarios: (1) retrospective estimation for the purpose of moving tumor setup used just after volumetric matching with CBCT; and (2) on-the-fly estimation for the purpose of real-time target position estimation during gating or tracking delivery, either for full-rotation volumetric-modulated arc therapy (VMAT) in 60 s or a stationary six-field intensity-modulated radiation therapy (IMRT) with a beam delivery time of 20 s. RESULTS: For the retrospective CBCT simulations, the mean 3D root-mean-square error (RMSE) for all 4893 trajectory segments was 0.41 mm (simple linear model) and 0.35 mm (state-augmented model). In the on-the-fly simulations, prior projections over more than 60° appear to be necessary for reliable estimations. The mean 3D RMSE during beam delivery after the simple linear model had established with a prior 90° projection data was 0.42 mm for VMAT and 0.45 mm for IMRT. CONCLUSIONS: The proposed method does not require any internal/external correlation or statistical modeling to estimate the target trajectory and can be used for both retrospective image-guided radiotherapy with CBCT projection images and real-time target position monitoring for respiratory gating or tracking.NHMRC, National Research Foundation of Kore

Regularized 4D-CT reconstruction from a single dataset with a spatio-temporal prior

X-ray Computerized Tomography (CT) reconstructions can be severely impaired by the patient’s respiratory motion and cardiac beating. Motion must thus be recovered in addition to the 3D reconstruction problem. The approach generally followed to reconstruct dynamic volumes consists of largely increasing the number of projections so that independent reconstructions be possible using only subsets of projections from the same phase of the cyclic movement. Apart from this major trend, motion compensation (MC) aims at recovering the object of interest and its motion by accurately modeling its deformation over time, allowing to use the whole dataset for 4D reconstruction in a coherent way.We consider a different approach for dynamic reconstruction based on inverse problems, without any additional measurements, nor explicit knowledge of the motion. The dynamic sequence is reconstructed out of a single data set, only assuming the motion’s continuity and periodicity. This inverse problem is solved by the minimization of the sum of a data-fidelity term, consistent with the dynamic nature of the data, and a regularization term which implements an efficient spatio-temporal version of the total variation (TV). We demonstrate the potential of this approach and its practical feasibility on 2D and 3D+t reconstructions of a mechanical phantom and patient data

Estimating 3-D Respiratory Motion From Orbiting Views by Tomographic Image Registration

Respiratory motion remains a significant source of errors in treatment planning for the thorax and upper abdomen. Recently, we proposed a method to estimate two-dimensional (2-D) object motion from a sequence of slowly rotating X-ray projection views, which we called deformation from orbiting views (DOVs). In this method, we model the motion as a time varying deformation of a static prior of the anatomy. We then optimize the parameters of the motion model by maximizing the similarity between the modeled and actual projection views. This paper extends the method to full three-dimensional (3-D) motion and cone-beam projection views. We address several practical issues for using a cone-beam computed tomography (CBCT) scanner that is integrated in a radiotherapy system, such as the effects of Compton scatter and the limited gantry rotation for one breathing cycle. We also present simulation and phantom results to illustrate the performance of this method.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85995/1/Fessler38.pd

Surrogate driven respiratory motion model derived from CBCT projection data

Cone Beam Computed Tomography (CBCT) is the most common imaging method for Image Guided Radiation Therapy (IGRT). However due to the slow rotating gantry, the image quality of CBCT can be adversely affected by respiratory motion, as it blurs the tumour and nearby organs at risk (OARs), which makes visualization of organ boundaries difficult, in particular for organs in the thoracic region. Currently one approach to tackle the problem of respiratory motion is the use of respiratory motion model to compensate for the motion during CBCT image reconstruction. The overall goal of this work is to estimate the 3D motion, including the breath-to-breath variability, on the day of treatment directly from the CBCT projection data, without requiring any external devices. The work presented here consist of two main parts: firstly, we introduce a novel data driven method based on Principal Component Analysis PCA, with the goal to extract a surrogate signal related to the internal anatomy from the CBCT projections. Secondly, using the extracted signals, we use surrogate-driven respiratory motion models to estimate the patient’s 3D respiratory motion. We utilized a recently developed generalized framework that unifies image registration and correspondence model fitting into a single optimization. This enables the model to be fitted directly to unsorted/unreconstructed data (CBCT projection data), thereby allowing an estimate of the patient’s respiratory motion on the day of treatment. To evaluate our methods, we have used an anthropomorphic software phantom combined with CBCT projection simulations. We have also tested the proposed method on clinical data with promising results obtained

Estimating 3D Respiratory Motion from Orbiting Views

This paper describes a method for estimating 3D respiratory motion so as to characterize tumor motion. This method uses two sets of measurements. One is a reference thorax volume obtained from a conventional fast CT scanner under breath-hold condition. The other is a sequence of projection views of the same patient (acquired at treatment time) using a slowly rotating cone-beam system (1 minute per rotation) during free breathing. We named this method deformation from orbiting views (DOV). Breathing motion over the entire acquisition period is estimated by deforming the reference volume through time so that its projections best match the measured projection views. The nonrigid breathing motion is described by a B-spline based deformation model. The parameters of this model are estimated by minimizing a regularized squared error cost function, using a conjugate gradient descent algorithm. Performance of this approach was evaluated by simulation. Results showed good agreement between the estimated and synthesized motion, with a mean absolute error of 1.63 mm. Relatively larger errors tended to occur in uniform regions, which would not have significant effects on generating deformed volumes based on the estimated motion. The results indicate that it is feasible to estimate realistic nonrigid motion from a sequence of slowly rotating cone beam projection views.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85996/1/Fessler214.pd