Showing papers on "Parametric Image published in 2009"

Parametric image reconstruction using the discrete cosine transform for optical tomography.

Abstract: It is well known that the inverse problem in optical tomography is highly ill-posed. The image reconstruction process is often unstable and nonunique, because the number of the boundary measurements data is far fewer than the number of the unknown parameters to be reconstructed. To overcome this problem, one can either increase the number of measurement data (e.g., multispectral or multifrequency methods), or reduce the number of unknowns (e.g., using prior structural information from other imaging modalities). We introduce a novel approach for reducing the unknown parameters in the reconstruction process. The discrete cosine transform (DCT), which has long been used in image compression, is here employed to parameterize the reconstructed image. In general, only a few DCT coefficients are needed to describe the main features in an optical tomographic image. Thus, the number of unknowns in the image reconstruction process can be drastically reduced. We show numerical and experimental examples that illustrate the performance of the new algorithm as compared to a standard model-based iterative image reconstructions scheme. We especially focus on the influence of initial guesses and noise levels on the reconstruction results.

PET Parametric Imaging Improved by Global-Two-Stage Method

Abstract: The analysis of positron emission tomography (PET) images at the pixel level may yield unreliable parameter estimates due to the low signal-to-noise ratio of pixel time activity curves (TAC) To address this issue it can be helpful to use techniques developed in the pharmacokinetic/pharmacodynamic area and referred to as ‘population approaches’ In this paper, we describe a new estimation algorithm, the Global-Two-Stage (GTS), and assess its performances through Monte Carlo simulations GTS was compared to the basis function method on synthetic [11C](R)-PK11195 data, and to weighted nonlinear least squares on synthetic [11C]WAY100,635 data In both cases, GTS produced parameter estimates with lower root mean square error and lower bias than the well-established estimation methods used for comparison, with a negligible increase of computational time GTS was applied first to all the pixels of the simulated slices Then, after a preliminary segmentation of pixels into more homogeneous populations, GTS was applied to each subpopulation separately: this last approach provided the best results In conclusion, GTS is a powerful and fast technique that can be applied to improve parametric maps, as long as preliminary estimates of parameters and of their covariance are available

Direct 4D parametric image reconstruction with plasma input and reference tissue models in reversible binding imaging

Abstract: The most active area in brain PET ligand development and imaging continues to involve receptor/transporter studies involving reversible binding The focus of this work has been to develop direct 4D parametric image reconstruction techniques for reversible binding imaging Based on a recent graphical analysis formulation [1], we developed a closed-form 4D EM algorithm to directly reconstruct distribution volume (DV) parametric images using a plasma input model Furthermore, while previous work in the area of 4D imaging has been primarily limited to plasma input models, we sought to also develop reference tissue model schemes whereby distribution volume ratio (DVR) parametric images were reconstructed by the reference tissue model within the 4D image reconstruction task (using the cerebellum as reference) The means of parameters estimated from 55 human 11C-raclopride dynamic PET studies were used for simulation (22 realizations) using a mathematical brain phantom Images were reconstructed using standard FBP or EM methods followed by modeling, as well as the proposed direct methods Noise vs bias quantitative measurements were performed in various regions of the brain Direct 4D EM reconstruction resulted in substantial visual and quantitative accuracy improvements (over 100% noise reduction, with matched bias, in both plasma and reference-tissue input models) Notable improvements were also observed in the coefficient of variation (COV) of the estimated binding potential (BP) values, including even for the relatively low BP regions of grey and thalamus, suggesting the ability for robust parameter estimation even in such regions

Spatial Resolution of Attenuation Imaging

Abstract: The attenuating properties of biological tissue are of great importance in ultrasonic examination even though its anatomical variability limits diagnostics effectiveness. We are currently developing a technique for parametric imaging of attenuation and we intend to apply it for in vivo characterization of tissue. The diagnostic usefulness of the proposed technique crucially depends on the precision of the attenuation estimate and the resolution of the parametric image. These two parameters are highly correlated, since the resolution is reduced whenever averaging is used to minimize the errors introduced by the random character of the backscatter. Here we report on the results of numerical processing of both, simulated and recorded from a tissue-mimicking phantom echoes. We have analyzed the parameters of the estimation technique and examined their influence on the precision of the attenuation estimate and on the parametric image resolution. The optimal selection of attenuation image parameters depending on its intended diagnostic use, was also considered.

Constructing Reliable Parametric Images Using Enhanced GLLS for Dynamic SPECT

Abstract: The generalized linear least square (GLLS) method can successfully construct unbiased parametric images from dynamic positron emission tomography data. Quantitative dynamic single photon emission computed tomography (SPECT) also has the potential to generate physiological parametric images. However, the high level of noise, intrinsic in SPECT, can give rise to unsuccessful voxelwise fitting using GLLS, resulting in physiologically meaningless estimates. In this paper, we systematically investigated the applicability of our recently proposed approaches to improve the reliability of GLLS to parametric image generation from noisy dynamic SPECT data. The proposed approaches include use of a prior estimate of distribution volume ( V d), a bootstrap Monte Carlo (BMC) resampling technique, as well as a combination of both techniques. Full Monte Carlo simulations were performed to generate dynamic projection data, which were then reconstructed with and without resolution recovery, before generating parametric images with the proposed methods. Four experimental clinical datasets were also included in the analysis. The GLLS methods incorporating BMC resampling could successfully and reliably generate parametric images. For high signal-to-noise ratio (SNR) imaging data, the BMC-aided GLLS provided the best estimates of K 1, while the BMC-Vd-aided GLLS proved superior for estimating V d. The improvement in reliability gained with BMC-aided GLLS in low SNR image data came at the expense of some overestimation of V d and increased computation time.

An Activity-Subspace Approach for Estimating the Integrated Input Function and Relative Distribution Volume in PET Parametric Imaging

Abstract: Dynamic positron emission tomography (PET) imaging technique enables the measurement of neuroreceptor distributions corresponding to anatomic structures, and thus, allows image-wide quantification of physiological and biochemical parameters. Accurate quantification of the concentration of neuroreceptor has been the objective of many research efforts. Compartment modeling is the most widely used approach for receptor binding studies. However, current compartment-model-based methods often either require intrusive collection of accurate arterial blood measurements as the input function, or assume the existence of a reference region. To obviate the need for the input function or a reference region, in this paper, we propose to estimate the input function. We propose a novel concept of activity subspace, and estimate the input function by the analysis of the intersection of the activity subspaces. Then, the input function and the distribution volume (DV) parameter are refined and estimated iteratively. Thus, the underlying parametric image of the total DV is obtained. The proposed method is compared with a blind estimation method, iterative quadratic maximum-likelihood (IQML) via simulation, and the proposed method outperforms IQML. The proposed method is also evaluated in a brain PET dataset.

Initial evaluation of a direct 4D PET parametric image reconstruction method

Abstract: 1462 Objectives The goal of this study is to evaluate an algorithm for direct reconstruction of PET parametric images from a one-tissue (1T) kinetic model. Methods Replicates of a 10-cm spherical phantom with 3 regions (Gray matter (GM), White matter(WM) and Basal Ganglia(BG)) were simulated; each comprised a 1-h list mode file (~4x108 events) based on a measured input function, typical kinetic parameters, and C-11 decay. Parametric images were produced with a frame-based method (FM) and the direct 4D method. The FM reconstructed individual frames (21 frames: 6x30s, 3x60s, 2x120s, 10x300s) and estimated each voxel9s kinetic parameters from its time-activity curve with weighted least squares (weights based on noise equivalent counts). The 4D method, an extension of the MOLAR algorithm, was derived from a new likelihood function which incorporated the 1T model into the physical projection model, and employed a novel EM algorithm to estimate the kinetic parameters directly from the list-mode data. Both methods used ordered subsets (30 subsets, 2 iterations). Evaluation was based on each region’s bias and coefficient of variation (COV) across replicates. Results Percent bias of both methods were small, with Conclusions This study indicates that the direct 4D reconstruction method is a promising approach to reduce noise in parametric images. Additional evaluation both by simulation and with real data is required.