Parametric Image

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

GPU-based List-mode Direct Parametric Reconstruction for Dynamic Cardiac SPECT

Abstract: Noninvasive quantification of myocardial blood flow (MBF) using Single-Photon Emission Computed Tomography (SPECT) is an important clinical tool for the diagnosis and characterization of coronary artery disease. Current indirect MBF estimation approach using SPECT suffers from inaccurate quantification due to substantial image noise. Existing direct parametric reconstruction methods for conventional parallel-hole rotational scanners estimate kinetic parameters only for specific ROIs. In this paper we developed an innovative voxel-by-voxel based direct parametric image reconstruction algorithm to substantially reduce noise in MBF quantification using dynamic SPECT/CT and allow for patient radiation dose reduction. GPU-based parallel computing was used to achieve >2000-fold acceleration. The proposed method was evaluated in both simulation and in-vivo studies. Compared with the indirect method, the direct method achieved substantially lower image noise, particularly at high iterations and with low count levels.

The Effects of Delay on the Input Function for Early Dynamics in Total Body Parametric Imaging

Abstract: The recently developed total-body PET scanner enables high temporal resolution in dynamic imaging. Due to the much improved temporal resolution and large field of view, delay and the dispersion effects in the image-derived input function, which vary for different tissues and organs, may affect accuracy in parametric imaging. In this paper, the delay effect was studied using the early kinetics of an FDG scan, which may be approximated using a 1-tissue compartment model. Dynamic reconstructed frames were acquired using the total-body PET scanner with 1-second frames for the first 30 seconds and 2 seconds for the subsequent 60 seconds. The image-derived input function was acquired from the reconstructed dynamic sequence using volumes of interest in the ascending and descending aorta. Voxel-specific delay times for the plasma input function were also modeled within the 1-tissue compartment model. A total of 4 parametric images were generated. Image-based parametric image generation was achieved with a maximum likelihood estimation method. Parametric images with and without the modeling of delay time in the input function were compared. Additional image denoising techniques including Gaussian denoising and non-local-mean denoising were employed. Quantitative evaluation was achieved by the calculation of the Akaike Information Criterion (AIC). The voxel-specific parameters of the 1-tissue compartment together with the delay time were successfully reconstructed using the proposed method. The estimated delay time showed variations as large as 40 seconds. The non-local-mean filter was shown to be able to reduce the image noise of the generated parametric images. Various image artifacts were observed when no delay time model was included. We have shown that with the use of total-body PET and the increased sensitivity, it is possible to estimate parametric images using the very early stages of the FDG injection. The combined effects of delay and dispersion will be studied in the future.

The expectation-maximization (EM) algorithm applied to image identification and restoration

Abstract: Prior to restoring a noisy blurred image, the degradations the image has suffered need to be determined. We present a maximum likelihood blur identification method which estimates the required parameters from the observed noisy blurred image itself, and propose to employ the expectation-maximization algorithm to solve the resulting complicated problem of optimizing the likelihood function. A priori information in the form of [i] initial conditions about the unknown parameters, and [ii] parametric image and blur models are incorporated to make the algorithm applicable to realistic blurs and to improve the identification results. The proposed approach results in a flexible iterative algorithm which is computationally far more efficient than directly optimizing the likelihood function.

Resolving power of a parametric image converter in the case of a nonmonochromatic signal and a divergent pump wave

Abstract: Numerical methods are used to find the form of the scatter function and the resolving power of a parametric image (frequency) converter in the case of a nonmonochromatic signal and of a divergent and nonmonochromatic pump wave.

Solution to the Registration Problem Using Differential Evolution and SSD-ARC Function

Abstract: The problem of image registration is to find the best set of parameters of an affine transformation, which applied to a given image yields the closest match to a target image (possibly with noise). We present a method to perform parametric image registration based on Differential Evolution. Besides using Differential Evolution, we propose to use an error function robust enough to discard misleading information contained in outliers. The results are compared to those obtained using Genetic Algorithms. It is clear that Differential Evolution outperforms Genetic Algorithms in terms of speed (number of evaluations), and quality of the solutions (accuracy). The quality of the solutions provided by Differential Evolution is so good that they do not need to be refined by gradient methods. At the end we present a general analysis and discussion about why DE converges in a better way than GA.