Parametric Image

About: Parametric Image is a research topic. Over the lifetime, 311 publications have been published within this topic receiving 6095 citations.

On the Duality Theory for Finite Dimensional Variational Inequalities

Abstract: By means of a separation scheme, considered in a parametric image space, it is possible to associate to a Variational Inequality a parametric dual problem from which dual variational inequalities can be derived.

Parametric image formation using clustering for dynamic cardiac SPECT

Abstract: Dynamic cardiac SPECT imaging can provide quantitative and possibly even absolute measures of physiological parameters. However, a dynamic cardiac SPECT study involves a number of steps to obtain estimates of physiological parameters of interest. One of the key steps involves the selection of regions of interest. In the past, this has been done manually or by using a semi-automatic method. We propose to use cluster analysis to segment the data to obtain improved parameter estimates. The algorithm consists of using a standard k-means clustering followed by a blood input fine-tuning procedure using fuzzy k-means performed to obtain a more accurate blood input function. Computer simulations were used to test the algorithm and to compute bias in kinetic rate parameters with and without the use of blood input fine-tuning. This was followed by performing eight studies in three canines and three studies in two patients with a dynamic cardiac perfusion SPECT protocol. The short-axis slice image data were used as input for the cluster analysis program as well as for a previously validated semi-automatic method. All of the time activity curves were fit to a two-compartment model. Parametric images of the wash-in rate parameter were obtained after cluster analysis. The wash-in rate estimates from the selected regions of interest with both of the methods were compared using microsphere derived flows as a gold standard in the case of canine studies. Our results suggest that in regions with low noise, cluster analysis provides parameter estimates comparable to the semi-automatic method in addition to providing improved visual defect localization and contrast. Moreover, the clustered curves have less noise and yield reasonable fits where with the semi-automatic method the fitting routine sometimes failed to converge. The use of clustering also required less manual intervention than the semi-automatic method. These results indicate that use of clustering may bring dynamic cardiac SPECT closer to clinical feasibility.

Formation of parametric images using mixed-effects models: a feasibility study.

Abstract: Mixed-effects models have been widely used in the analysis of longitudinal data. By presenting the parameters as a combination of fixed effects and random effects, mixed-effects models incorporating both within- and between-subject variations are capable of improving parameter estimation. In this work, we demonstrate the feasibility of using a non-linear mixed-effects (NLME) approach for generating parametric images from medical imaging data of a single study. By assuming that all voxels in the image are independent, we used simulation and animal data to evaluate whether NLME can improve the voxel-wise parameter estimation. For testing purposes, intravoxel incoherent motion (IVIM) diffusion parameters including perfusion fraction, pseudo-diffusion coefficient and true diffusion coefficient were estimated using diffusion-weighted MR images and NLME through fitting the IVIM model. The conventional method of non-linear least squares (NLLS) was used as the standard approach for comparison of the resulted parametric images. In the simulated data, NLME provides more accurate and precise estimates of diffusion parameters compared with NLLS. Similarly, we found that NLME has the ability to improve the signal-to-noise ratio of parametric images obtained from rat brain data. These data have shown that it is feasible to apply NLME in parametric image generation, and the parametric image quality can be accordingly improved with the use of NLME. With the flexibility to be adapted to other models or modalities, NLME may become a useful tool to improve the parametric image quality in the future. Copyright © 2015 John Wiley & Sons, Ltd.