[show abstract][hide abstract] ABSTRACT: In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object's 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projec-tion(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region's motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method's application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof.
[show abstract][hide abstract] ABSTRACT: We present a novel 2D/3D deformable registration method, called Registration Efficiency and Accuracy through Learning Metric on Shape (REALMS), that can support real-time Image-Guided Radia-tion Therapy (IGRT). The method consists of two stages: planning-time learning and registration. In the planning-time learning, it firstly models the patient's 3D deformation space from the patient's time-varying 3D planning images using a low-dimensional parametrization. Secondly, it samples deformation parameters within the deformation space and gen-erates corresponding simulated projection images from the deformed 3D image. Finally, it learns a Riemannian metric in the projection space for each deformation parameter. The learned distance metric forms a Gaussian kernel of a kernel regression that minimizes the leave-one-out regression residual of the corresponding deformation parameter. In the registration, REALMS interpolates the patient's 3D deformation param-eters using the kernel regression with the learned distance metrics. Our test results showed that REALMS can localize the tumor in 10.89 ms (91.82 fps) with 2.56 ± 1.11 mm errors using a single projection image. These promising results show REALMS's high potential to support real-time, accurate, and low-dose IGRT.
MICCAI workshop on Medical Computer Vision; 10/2012
[show abstract][hide abstract] ABSTRACT: Purpose: To study the feasibility of a novel 2D/3D image registration method, called Projection Metric Learning for Shape Kernel Regression (PML-SKR), in supporting on-board x-ray imaging systems to perform real-time image-guided radiation therapy in the lung. Methods: PML-SKR works in two stages: planning and treatment. At planning stage, firstly it parameterizes the patient's respiratory deformation from the patient's treatment-planning Respiratory-Correlated CTs (RCCTs) by doing PCA analysis on the inter-phase respiratory deformations. Secondly, it simulates a set of training projection images from a set of deformed CTs where their associated deformation parameters are sampled within 3 standard deviations of the parameter's values observed in the RCCTs. Finally, it learns a Riemannian distance metric on projection intensity for each deformation parameter. The learned distance metric forms a Gaussian kernel of a kernel regression that minimizes the leave-one-out regression residual of the corresponding deformation parameter. At treatment stage, PML-SKR interpolates the patient's 3D deformation parameters from the parameter's values in the training cases using the kernel regression with the learned distance metrics. Results: We tested PML-SKR on the NST (Nanotube Stationary Tomosynthesis) x-ray imaging system. In each test case, a DRR (dimension: 64×64) of an x-ray source in the NST was simulated from a target CT for registration. The target CTs were deformed by normally distributed random samples of the first three deformation parameters. We generated 300 synthetic test cases from 3 lung datasets and measured the registration quality by the mTRE (mean Target Registration Error) over all cases and all voxels at tumor sites. With PML-SKR's registrations, the average mTRE and its standard deviation are down from 10.89±4.44 to 0.67±0.46 mm using 125 training projection images. The computation time for each registration is 12.71±0.70 ms. Conclusion: The synthetic results have shown PML-SKR's promise in supporting real-time, accurate, and low-dose lung IGRT. This work was partially supported by Siemens Medical Solutions.
Medical Physics 06/2012; 39(6):3875-3876. · 2.91 Impact Factor
[show abstract][hide abstract] ABSTRACT: Purpose: To evaluate the feasibility of patient specific deformation models (PSDM) in the male pelvis for IGRT by limited angular imaging. Methods: In IGRT via limited angular imaging, insufficient angular projections are acquired to uniquely determine a 3D attenuation distribution. For highly limited geometries, image quality may be too poor for successful non-rigid registration. This can be overcome by restricting the transformation space to one containing only feasible transformations learned from prior 3D images. This has been successfully applied in the lung region where a majority of deformation is due to respiratory motion which can be adequately observed at planning time with RCCT. Typically, the phases of the RCCT are registered together to form an group-wise mean image and transformations to each training image. PCA is then performed on the transformation displacement vector fields. The transformation is found at treatment time by registration of digitally reconstructed radiographs of the transformed image to the measured projections, optimizing over the parameters of the PCA subspace. In the male pelvis, deformation is much more complicated than respiratory deformation and is largely inter-fractional due to changes in bladder and rectal contents, articulation, and motion of the bowels. A similar model is developed for the male pelvis which takes into account pelvic anatomical information and handles the more complicated deformation space. Results: Using the leave-one-out method, dice similarity coefficients in the prostate compared with manual segmentations are increased over the those obtained by rigid registration and are comparable with those obtained by 3D non-rigid registration methods. Conclusions: This method produces better results than rigid registration and is comparable with results obtained by 3D/3D registration even though it uses limited angle projections. However, its relies on daily training CTs, so it is not yet a viable clinical method. Funding provided in part by Siemens Medical.
Medical Physics 06/2012; 39(6):3667. · 2.91 Impact Factor
[show abstract][hide abstract] ABSTRACT: One goal of statistical shape analysis is the discrimination between two populations of objects. Whereas traditional shape analysis was mostly concerned with single objects, analysis of multi-object complexes presents new challenges related to alignment and pose. In this paper, we present a methodology for discriminant analysis of multiple objects represented by sampled medial manifolds. Non-euclidean metrics that describe geodesic distances between sets of sampled representations are used for alignment and discrimination. Our choice of discriminant method is the distance-weighted discriminant because of its generalization ability in high-dimensional, low sample size settings. Using an unbiased, soft discrimination score, we associate a statistical hypothesis test with the discrimination results. We explore the effectiveness of different choices of features as input to the discriminant analysis, using measures like volume, pose, shape, and the combination of pose and shape. Our method is applied to a longitudinal pediatric autism study with 10 subcortical brain structures in a population of 70 subjects. It is shown that the choices of type of global alignment and of intrinsic versus extrinsic shape features, the latter being sensitive to relative pose, are crucial factors for group discrimination and also for explaining the nature of shape change in terms of the application domain.
[show abstract][hide abstract] ABSTRACT: 4D image-guided radiation therapy (IGRT) for free-breathing lungs is challenging due to the complicated respiratory dynamics. Effective modeling of respiratory motion is crucial to account for the motion affects on the dose to tumors. We propose a shape-correlated statistical model on dense image deformations for patient-specic respiratory motion estimation in 4D lung IGRT. Using the shape deformations of the high-contrast lungs as the surrogate, the statistical model trained from the planning CTs can be used to predict the image deformation during delivery verication time, with the assumption that the respiratory motion at both times are similar for the same patient. Dense image deformation fields obtained by diffeomorphic image registrations characterize the respiratory motion within one breathing cycle. A point-based particle optimization algorithm is used to obtain the shape models of lungs with group-wise surface correspondences. Canonical correlation analysis (CCA) is adopted in training to maximize the linear correlation between the shape variations of the lungs and the corresponding dense image deformations. Both intra- and inter-session CT studies are carried out on a small group of lung cancer patients and evaluated in terms of the tumor location accuracies. The results suggest potential applications using the proposed method.
[show abstract][hide abstract] ABSTRACT: Principal component analysis (PCA) for various types of image data is analyzed in terms of the forward and backward stepwise
viewpoints. In the traditional forward view, PCA and approximating subspaces are constructed from lower dimension to higher
dimension. The backward approach builds PCA in the reverse order from higher dimension to lower dimension.We see that for
manifold data the backward view gives much more natural and accessible generalizations of PCA. As a backward stepwise approach,
composite Principal Nested Spheres, which generalizes PCA, is proposed. In an example describing the motion of the lung based
on CT images, we show that composite Principal Nested Spheres captures landmark data more succinctly than forward PCA methods.
[show abstract][hide abstract] ABSTRACT: Intensity modulated radiation therapy (IMRT) for cancers in the lung remains challenging due to the complicated respiratory dynamics. We propose a shape-navigated dense image deformation model to estimate the patient-specific breathing motion using 4D respiratory correlated CT (RCCT) images. The idea is to use the shape change of the lungs, the major motion feature in the thorax image, as a surrogate to predict the corresponding dense image deformation from training.To build the statistical model, dense diffeomorphic deformations between images of all other time points to the image at end expiration are calculated, and the shapes of the lungs are automatically extracted. By correlating the shape variation with the temporally corresponding image deformation variation, a linear mapping function that maps a shape change to its corresponding image deformation is calculated from the training sample. Finally, given an extracted shape from the image at an arbitrary time point, its dense image deformation can be predicted from the pre-computed statistics.The method is carried out on two patients and evaluated in terms of the tumor and lung estimation accuracies. The result shows robustness of the model and suggests its potential for 4D lung radiation treatment planning.
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 06/2009; 2009:875-878.