Real-time prediction of respiratory motion based on local regression methods

Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using these techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on local regression. There are three major ingredients of this approach: (1) forming an augmented state space to capture system dynamics, (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion, (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumor motion and its observed location for ten patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58097/2/pmb7_23_024.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

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

3D Forward and Back-Projection for X-Ray CT Using Separable Footprints

Iterative methods for 3D image reconstruction have the potential to improve image quality over conventional filtered back projection (FBP) in X-ray computed tomography (CT). However, the computation burden of 3D cone-beam forward and back-projectors is one of the greatest challenges facing practical adoption of iterative methods for X-ray CT. Moreover, projector accuracy is also important for iterative methods. This paper describes two new separable footprint (SF) projector methods that approximate the voxel footprint functions as 2D separable functions. Because of the separability of these footprint functions, calculating their integrals over a detector cell is greatly simplified and can be implemented efficiently. The SF-TR projector uses trapezoid functions in the transaxial direction and rectangular functions in the axial direction, whereas the SF-TT projector uses trapezoid functions in both directions. Simulations and experiments showed that both SF projector methods are more accurate than the distance-driven (DD) projector, which is a current state-of-the-art method in the field. The SF-TT projector is more accurate than the SF-TR projector for rays associated with large cone angles. The SF-TR projector has similar computation speed with the DD projector and the SF-TT projector is about two times slower.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85876/1/Fessler5.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

Nonrigid Registration Using Regularization that Accomodates Local Tissue Rigidity

Regularized nonrigid medical image registration algorithms usually estimate the deformation by minimizing a cost function, consisting of a similarity measure and a penalty term that discourages “unreasonable” deformations. Conventional regularization methods enforce homogeneous smoothness properties of the deformation field; less work has been done to incorporate tissue-type-specific elasticity information. Yet ignoring the elasticity differences between tissue types can result in non-physical results, such as bone warping. Bone structures should move rigidly (locally), unlike the more elastic deformation of soft issues. Existing solutions for this problem either treat different regions of an image independently, which requires precise segmentation and incurs boundary issues; or use an empirical spatial varying “filter” to “correct” the deformation field, which requires the knowledge of a stiffness map and departs from the cost-function formulation. We propose a new approach to incorporate tissue rigidity information into the nonrigid registration problem, by developing a space variant regularization function that encourages the local Jacobian of the deformation to be a nearly orthogonal matrix in rigid image regions, while allowing more elastic deformations elsewhere. For the case of X-ray CT data, we use a simple monotonic increasing function of the CT numbers (in HU) as a “rigidity index” since bones typically have the highest CT numbers. Unlike segmentation-based methods, this approach is flexible enough to account for partial volume effects. Results using a B-spline deformation parameterization illustrate that the proposed approach improves registration accuracy in inhale-exhale CT scans with minimal computational penalty.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85935/1/Fessler216.pd

Repeated Assessment of Exploration and Novelty Seeking in the Human Behavioral Pattern Monitor in Bipolar Disorder Patients and Healthy Individuals

Exploration and novelty seeking are cross-species adaptive behaviors that are dysregulated in bipolar disorder (BD) and are critical features of the illness. While these behaviors have been extensively quantified in animals, multivariate human paradigms of exploration are lacking. The human Behavioral Pattern Monitor (hBPM), a human version of the animal open field, identified a signature pattern of hyper-exploration in manic BD patients, but whether exploratory behavior changes with treatment is unknown. The objective of this study was to assess the sensitivity of the hBPM to changes in manic symptoms, a necessary step towards elucidating the neurobiology underlying BD.Twelve acutely hospitalized manic BD subjects and 21 healthy volunteers were tested in the hBPM over three sessions; all subjects were retested one week after their first session and two weeks after their second session. Motor activity, spatial and entropic (degree of unpredictability) patterns of exploration, and interactions with novel objects were quantified. Manic BD patients demonstrated greater motor activity, extensive and more unpredictable patterns of exploration, and more object interactions than healthy volunteers during all three sessions. Exploration and novelty-seeking slightly decreased in manic BD subjects over the three sessions as their symptoms responded to treatment, but never to the level of healthy volunteers. Among healthy volunteers, exploration did not significantly decrease over time, and hBPM measures were highly correlated between sessions.Manic BD patients showed a modest reduction in symptoms yet still demonstrated hyper-exploration and novelty seeking in the hBPM, suggesting that these illness features may be enduring characteristics of BD. Furthermore, behavior in the hBPM is not subject to marked habituation effects. The hBPM can be reliably used in a repeated-measures design to characterize exploration and novelty seeking and, in parallel with animal studies, can contribute to developing treatments that target neuropsychiatric disease

Towards Omni-Tomography—Grand Fusion of Multiple Modalities for Simultaneous Interior Tomography

We recently elevated interior tomography from its origin in computed tomography (CT) to a general tomographic principle, and proved its validity for other tomographic modalities including SPECT, MRI, and others. Here we propose “omni-tomography”, a novel concept for the grand fusion of multiple tomographic modalities for simultaneous data acquisition in a region of interest (ROI). Omni-tomography can be instrumental when physiological processes under investigation are multi-dimensional, multi-scale, multi-temporal and multi-parametric. Both preclinical and clinical studies now depend on in vivo tomography, often requiring separate evaluations by different imaging modalities. Over the past decade, two approaches have been used for multimodality fusion: Software based image registration and hybrid scanners such as PET-CT, PET-MRI, and SPECT-CT among others. While there are intrinsic limitations with both approaches, the main obstacle to the seamless fusion of multiple imaging modalities has been the bulkiness of each individual imager and the conflict of their physical (especially spatial) requirements. To address this challenge, omni-tomography is now unveiled as an emerging direction for biomedical imaging and systems biomedicine