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

A Simplified Motion Model for Estimating Respiratory Motion from Orbiting Views

We have shown previously that the internal motion caused by a patient’s breathing can be estimated from a sequence of slowly rotating 2D cone-beam X-ray projection views and a static prior of of the patient’s anatomy.1, 2 The estimator iteratively updates a parametric 3D motion model so that the modeled projection views of the deformed reference volume best match the measured projection views. Complicated motion models with many degrees of freedom may better describe the real motion, but the optimizations assiciated with them may overfit noise and may be easily trapped by local minima due to a large number of parameters. For the latter problem, we believe it can be solved by offering the optimization algorithm a good starting point within the valley containing the global minimum point. Therefore, we propose to start the motion estimation with a simplified motion model, in which we assume the displacement of each voxel at any time is proportional to the full movement of that voxel from extreme exhale to extreme inhale. We first obtain the full motion by registering two breathhold CT volumes at end-expiration and end-inspiration. We then estimate a sequence of scalar displacement proportionality parameters. Thus the goal simplifies to finding a motion amplitude signal. This estimation problem can be solved quickly using the exhale reference volume and projection views with coarse (downsampled) resolution, while still providing acceptable estimation accuracy. The estimated simple motion then can be used to initialize a more complicated motion estimator.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85923/1/Fessler224.pd

Respiratory Motion Estimation from Slowly Rotating X-Ray Projections

As radiotherapy has become increasingly conformal, geometric uncertainties caused by breathing and organ motion have become an important issue. Accurate motion estimates may lead to improved treatment planning and dose calculation in radiation therapy. However, respiratory motion is difficult to study by conventional X-ray CT imaging since object motion causes inconsistent projection views leading to artifacts in reconstructed images. We propose to estimate the parameters of a nonrigid motion model from a set of projection views of the thorax that are acquired using a slowly rotating cone-beam CT scanner, such as a radiotherapy simulator. We use a conventionally reconstructed 3D thorax image, acquired by breath-hold CT, as a reference volume. We represent respiratory motion using a flexible parametric nonrigid motion model based on B-splines. The motion parameters are estimated by optimizing a regularized cost function that includes the squared error between the measured projection views and the reprojections of the deformed reference image. Preliminary 2D simulation results show that there is good agreement between the estimated motion and the true motion.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85878/1/Fessler197.pd

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

Iterative Sorting for 4DCT Images Based ON Internal Anatomy Motion

Geometric uncertainties caused by respiratory motion complicate radiotherapy treatment planning. Therefore 4D CT imaging is important in characterizing anatomy motion during breathing. Current 4D CT imaging techniques using multislice CT scanners involve multiple scans at several axial positions and retrospective sorting processes. Most sorting methods are based on externally monitored signals recorded by external monitoring instruments, which may not always accurately catch the actual breathing status and may lead to severe discontinuity artifacts in the sorted CT volumes. We propose a method to reconstruct time-resolved CT volumes based on internal motion to avoid the inaccuracies caused by external breathing signals. In our method, we iteratively sort the 4D CT slices using internal motion based breathing indices. In each iteration, respiratory motion is estimated by updating a motion model to best match a deformed reference volume to each moving multi-slice sub-volumes. The breathing indices as well as the reference volumes are refined for each iteration based on the currently estimated respiratory motion. An example is presented to illustrate the feasibility of our 4D CT sorting method without using any external motion monitoring systems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85803/1/Fessler229.pd

ESTIMATING RESPIRATORY MOTION FROM CT IMAGES VIA DEFORMABLE MODELS AND PRIORS

Understanding the movement of tumors during breathing is very important for conformal radiotherapy. Without the knowledge of the tumor movement, it is likely that either insufficient dose is delivered to tumors, or unnecessary dose is received by the surrounding normal tissue, or both. However, respiratory motion is very difficult to study by conventional x-ray CT imaging since object motion causes inconsistent projection views, leading to artifacts in reconstructed images. This dissertation focused on developing methods to build four-dimensional (4D) models of patient's anatomy during breathing, especially in thoracic and upper abdominal region, with currently available X-ray imaging techniques. We explored methods to estimate respiratory motion from a sequence of conebeam X-ray projection views acquired using a slowly rotating cone-beam CT (CBCT) scanner that was integrated into a Linac system. The slowly rotating CBCT scanners have a large volume coverage and a high temporal sampling rate. In the proposed deformation from orbiting views (DOV) approach, we modeled the motion as a time varying deformation of a static reference volume of the anatomy. We then optimized the parameters of the motion model by maximizing the similarity between the modeled and actual projection views. We used a B-spline based motion model. Challenges of this estimation problem include the limited gantry rotation in one breathing cycle, Compton scatter contamination of the projection views and heavy computation, which will be addressed in the dissertation. We conducted computer simulations and a phantom experiment to test the performance of this approach. Both cases achieved estimation accuracies within voxel resolution. We also explored methods that accelerate the optimization procedure. We researched the 4DCT imaging methods using multi-slice CT (MSCT) scanners and proposed a method to find the temporal correspondences among the unsorted 4DCT images based on internal anatomical motion. Our method used all the CT slices at each table position to estimate internal motion-based sorting indices. Experiments showed that the internal motion-based sorting greatly reduced tissue mismatch presented in the formed CT volumes using the externally recorded surrogates of breathing motion.Ph.D.Applied SciencesElectrical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/126983/2/3287666.pd

Iterative Sorting for Four-Dimensional CT Images Based on Internal Anatomy Motion

Current four-dimensional (4D) computed tomography (CT) imaging techniques using multislice CT scanners require retrospective sorting of the reconstructed two-dimensional (2D) CT images. Most existing sorting methods depend on externally monitored breathing signals recorded by extra instruments. External signals may not always accurately capture the breathing status and may lead to severe discontinuity artifacts in the sorted CT volumes. This article describes a method to find the temporal correspondences for the free-breathing multislice CT images acquired at different table positions based on internal anatomy movement. The algorithm iteratively sorts the CT images using estimated internal motion indices. It starts from two imperfect reference volumes obtained from the unsorted CT images; then, in each iteration, thorax motion is estimated from the reference volumes and the free-breathing CT images. Based on the estimated motion, the breathing indices as well as the reference volumes are refined and fed into the next iteration. The algorithm terminates when two successive iterations attain the same sorted reference volumes. In three out of five patient studies, our method attained comparable image quality with that using external breathing signals. For the other two patient studies, where the external signals poorly reflected the internal motion, the proposed method significantly improved the sorted 4D CT volumes, albeit with greater computation time.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85976/1/Fessler28.pd