Parametric Image

Abstract: 1. Introduction 2. The Human Neuroscanning Project 3. The mathematical setting I PARAMETRIC IMAGE REGISTRATION 4. Landmark based registration 5. Principal axes based registration 6. Optimal linear registration 7. Summarizing parametric image registration II NON-PARAMETRIC IMAGE REGISTRATION 8. Non-parametric image registration 9. Elastic registration 10. Fluid registration 11. Diffusion registration 12. Curvature registration 13. Concluding remarks

Abstract: Our goal in this paper is the estimation of kinetic model parameters for each voxel corresponding to a dense three-dimensional (3-D) positron emission tomography (PET) image. Typically, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated by fitting a model to the reconstructed time-activity response of each voxel. However, this "indirect" approach to kinetic parameter estimation tends to reduce signal-to-noise ratio (SNR) because of the requirement that the sinogram data be divided into individual time frames. In 1985, Carson and Lange proposed, but did not implement, a method based on the expectation-maximization (EM) algorithm for direct parametric reconstruction. The approach is "direct" because it estimates the optimal kinetic parameters directly from the sinogram data, without an intermediate reconstruction step. However, direct voxel-wise parametric reconstruction remained a challenge due to the unsolved complexities of inversion and spatial regularization. In this paper, we demonstrate and evaluate a new and efficient method for direct voxel-wise reconstruction of kinetic parameter images using all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem. The PICD algorithm is computationally efficient and is implemented with spatial regularization in the domain of the physiologically relevant parameters. Our experimental simulations of a rat head imaged in a working small animal scanner indicate that direct parametric reconstruction can substantially reduce root-mean-squared error (RMSE) in the estimation of kinetic parameters, as compared to indirect methods, without appreciably increasing computation.

Abstract: In many image restoration/resolution enhancement applications, the blurring process, i.e., point spread function (PSF) of the imaging system, is not known or is known only to within a set of parameters. We estimate these PSF parameters for this ill-posed class of inverse problem from raw data, along with the regularization parameters required to stabilize the solution, using the generalized cross-validation method (GCV). We propose efficient approximation techniques based on the Lanczos algorithm and Gauss quadrature theory, reducing the computational complexity of the GCV. Data-driven PSF and regularization parameter estimation experiments with synthetic and real image sequences are presented to demonstrate the effectiveness and robustness of our method.

Abstract: A broadband method for measuring backscatter coefficients sigma b and other acoustic parameters is described. From the sigma b measurements, using a commercially-available imaging system, four high-resolution parametric ultrasound images are formed in a C-scan image plane. Scatterer size images are computed from the frequency dependence of sigma b and a correlation model function that describes the structure and elastic properties of the medium. Scattering strength images are computed from the absolute magnitude of sigma b. Chi-square images are generated to display how well the correlation model represents the interrogated medium. Integrated backscatter coefficient images are formed over the transducer bandwidth. All four images are generated simultaneously and compared with the corresponding B-mode image. Test samples with known physical properties were used to demonstrate experimentally that accurate parametric images are possible if an accurate correlation model is used. Local variations in attenuation, the center frequency and bandwidth of the transducer, and the distribution of scatterer sizes greatly influence the accuracy of estimates and the appearance of the image, thus demonstrating the importance of these factors in parametric image interpretation.

Abstract: Parametric image segmentation consists of finding a label field that defines a partition of an image into a set of nonoverlapping regions and the parameters of the models that describe the variation of some property within each region. A new Bayesian formulation for the solution of this problem is presented, based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators for both this field and the model parameters by the minimization of a differentiable function. An efficient minimization algorithm and comparisons with existing methods on synthetic images are presented, as well as examples of realistic applications to the segmentation of Magnetic Resonance volumes and to motion segmentation.