Showing papers on "Parametric Image published in 2011"

Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images.

Abstract: Purpose: Benign and malignant tumors can be classified by using texture analysis of the ultrasound B-scan image to describe the variation in the echogenicity of scatterers. The recently proposed ultrasonic Nakagami parametric image has also been used to detect the concentrations and arrangements of scatterers for tumor characterization applications. B-scan-based texture analysis and the Nakagami parametric image are functionally complementary in ultrasonictissue characterizations and this study aimed to combine these methods in order to improve the ability to characterize breast tumors. Methods: To validate this concept, radio-frequency data obtained from 130 clinical cases were used to construct the texture-feature parametric image and the Nakagami parametric image. Four texture-feature parameters based on a gray-level co-occurrence matrix (homogeneity, contrast, energy, and variance) and the Nakagami parameters of the benign and malignant tumors were calculated. The usefulness of an individual parameter was determined and scatter graphs indicated the relationship between two selected texture-feature parameters. Fisher’s linear discriminant analysis was used to combine the selected texture-feature parameters with the Nakagami parameter. The performance in classifying tumors was evaluated based on the receiver operating characteristic curve. Results: The results indicated that there is a trade-off between sensitivity and specificity when using an individual texture-feature parameter or when combining two such correlated parameters to discriminate benign and malignant cases. However, the best performance was obtained when combining selected texture-feature parameters with the Nakagami parameter. Conclusions: The study findings suggest that combining B-scan-based texture analysis and the Nakagami parametric image could improve the ability to classify benign and malignant breast tumors.

Impact of erroneous kinetic model formulation in Direct 4D image reconstruction

Abstract: Direct parametric image reconstruction has the potential to reduce variance in parameter estimates when applied to PET/CT data. One complication when estimating parametric maps in the body is the difficulty of finding one single model to describe all the different kinetics in the field of view (FOV). Contrary to the post-reconstruction kinetic analysis though, any errors (bias) from the discrepancy between the model and the observed kinetics in the direct 4D reconstruction can potentially propagate spatially from unimportant areas to areas of interest. In this work we investigate this effect on simulated 4-D datasets based on a digital body phantom. Different realistic cases were simulated including differential input functions in the FOV and organs with different kinetics. Micro-parameters (K 1 , k 2 ,Vd, bv) where estimated using a newly proposed spatiotemporal 4D image reconstruction algorithm as well as using post-reconstruction kinetic analysis on noiseless and noisy datasets simulating [15O] H 2 O kinetics in the body. Bias analysis both in noiseless and noisy data showed a bias from badly modelled areas spatially propagates to other regions of interest in the direct reconstruction. Critically though under noisy conditions even with the bias propagation, the direct reconstruction method still outperforms the conventional post-reconstruction methodology. Nevertheless there is a need to ensure that appropriate models are chosen to describe the kinetics in the entire FOV with approaches such as data-driven adaptive kinetic modelling worth exploring.

Spatial modes of phase-sensitive parametric image amplifiers with circular and elliptical Gaussian pumps.

Abstract: We develop a method for finding the number and shapes of the independently squeezed or amplified modes of a spatially-broadband, travelling-wave, frequency- and polarization-degenerate optical parametric amplifier in the general case of an elliptical Gaussian pump. The obtained results show that for tightly focused pump only one mode is squeezed, and this mode has a Gaussian TEM00 shape. For larger pump spot sizes that support multiple modes, the shapes of the most-amplified modes are close to Hermite- or Laguerre-Gaussian profiles. These results can be used to generate matched local oscillators for detecting high amounts of squeezing and to design parametric image amplifiers that introduce minimal distortion.

An exploration of multimodal similarity metrics for parametric image registration based on particle filtering

Abstract: This paper presents an analysis of different multimodal similarity metrics for parametric image registration based on particle filtering. Our analysis includes four similarity metrics found in the literature and we propose a new metric based on the discretization of the kernel predictability, function recently introduced by Gomez-Garcia et al. (2008), that we call histogram kernel predictability (HKP). Hence the metrics studied in this work are mutual information, normalized mutual information, kernel predictibility with gaussian and truncated parabola functions, and HKP. The evaluations include tests varying the number of particles in the filter, the type of pixel sampling, the number of bins used to calculate the histograms, the noise in the images, and the computation time. Furthermore, we also conducted a geometric analysis to inspect convexity properties of the metrics under discussion. The overall evaluation suggests that the normalized mutual information is the best similarity metric for parametric image registration.

Evaluation of a direct 4D reconstruction method using GLLS for estimating parametric maps of micro-parameters

Abstract: Estimation of non-linear micro-parameters is a computationally demanding process, since it involves the use of iterative non-linear fitting algorithms and it often results in very noisy parametric maps Direct reconstruction algorithms can provide parametric maps with reduced variance, but usually the reconstruction is impractically time consuming with common non-linear fitting algorithms In this work we employed a recently proposed direct parametric image reconstruction algorithm to estimate the parametric maps of all micro-parameters of a two-tissue compartment model, used to describe the kinetics of [18F]FDG The algorithm decouples the tomographic and the kinetic modelling problems, allowing the use of previously developed post-reconstruction methods, such as the generalised linear least squares (GLLS) algorithm Results on both clinical and simulated data show that the direct reconstruction method provides considerable quantitative and visual improvements for all micro-parameters compared to the conventional post-reconstruction fitting method Additionally, due to the linearized nature of the GLLS algorithm, the fitting procedure does not considerably affect the overall reconstruction time

Direct 4D parametric image reconstruction for dynamic cardiac PET imaging

Abstract: 1996 Objectives Conventional dynamic myocardial perfusion PET imaging consists of reconstructing data frames individually followed by compartmental analysis to estimate kinetic parameters. The goal of this study is to develop and evaluate (through simulation) a direct parametric image reconstruction method for dynamic cardiac PET studies. Methods We developed a preconditioned steepest ascent (PSA) method that incorporates the one-tissue compartmental model to directly reconstruct parametric images from dynamic sinogram frames. The log-likelihood function for the direct 4D reconstruction has the myocardial activity represented as the contribution from plasma and myocardial tissue. It was maximized with respect to each of the involved kinetic parameters. Rb-82 PET patient organ time activity curves including blood pool and myocardium were acquired and fitted to generate a set of K1, k2, and vB values. The corresponding parametric images created from the XCAT phantom served as the truth for simulation. Image frames created from the parametric images (and the input function) were projected to generate the sinogram frames. Noise comparable to the clinical data level was added. To evaluate the results, polar maps were created from estimated K1 values on the left ventricular myocardium. We compared the resulted K1 polar maps from the PSA direct reconstruction and that from fitting the individually reconstructed 3D image frames. Results The K1 values on the whole polar map and its segments estimated from the PSA direct reconstruction showed significantly improved bias versus noise performance compared to those from fitting the compartmental model to individually reconstructed image frames. Conclusions A direct parametric image reconstruction method was developed to incorporate kinetic modeling in the reconstruction of dynamic cardiac PET data. With realistic simulation, we have demonstrated improved performance of the proposed technique over the conventional method on estimation of the myocardial rate constants

A Study on Static Image Derived Input Function for Non-invasively Constructing Parametric Image in Functional Imaging

Abstract: Positron emission tomography (PET), as functional imaging, provides in-vivo spatial distribution of physiological or biochemical processes. The kinetic modelling process to derive quantitative functional parameter usually requires invasive frequent blood sampling. We proposed a new approach to use static imaging derived information to produce non-invasive input function (SID-IF). The performance of SID-IF was investigated by 609 clinical neurological studies in non-invasively constructing parametric images of cerebral metabolic rate of glucose consumption (CMRGlc). The performance of the personal information feature based input function method (PIFB-IF) was also evaluated in the investigation. The results of area under curve and CMRGlc demonstrated the image feature derived by cerebellum provided less bias in the estimation of SID-IF. The performance of SID-IF was sensitive to the choice of the training set and the plasma glucose concentration in the data set may improve the estimated accuracy. The PIFB-IF method was less sensitive to the glucose range and choice of training set.