Showing papers on "Parametric Image published in 2013"

Direct estimation of kinetic parametric images for dynamic PET.

Abstract: Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed.

Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging

Abstract: Purpose: Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. Methods: In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. Results: On a cohort of 50 breast DCE-MRI studies, PrEIm yielded overall better region and boundary-based statistics compared to the original DCE-MR image, FCM, and PCA based image representations. Additionally, SEAC outperformed a hybrid AC applied to both PCA and FCM image representations. Mean dice similarity coefficient (DSC) for SEAC was significantly better (DSC = 0.74 ± 0.21) than FCM+AC (DSC = 0.50 ± 0.32) and similar to PCA+AC (DSC = 0.73 ± 0.22). Boundary-based metrics of mean absolute difference and Hausdorff distance followed the same trends. Of the automated segmentation methods, breast lesion classification based on morphologic features derived from SEAC segmentation using a support vector machine classifier also performed better (AUC = 0.67 ± 0.05;p < 0.05) than FCM+AC (AUC = 0.50 ± 0.07), and PCA+AC (AUC = 0.49 ± 0.07). Conclusions: In this work, we presented SEAC, an accurate, general purpose AC segmentation tool that could be applied to any imaging domain that employs time series data. SE allows for projection of time series data into a PrEIm representation so that every voxel is characterized by the dominant eigenvectors, capturing the global and local time-intensity curve similarities in the data. This PrEIm allows for the calculation of strong tensor gradients and better region statistics than the original image intensities or alternative image representations such as PCA and FCM. The PrEIm also allows for building a more accurate hybrid AC scheme.

Direct 4D whole-body PET/CT parametric image reconstruction: Concept and comparison vs. indirect parametric imaging

Abstract: 2133 Objectives Recently, indirect whole-body parametric PET/CT imaging, using Patlak method, has been introduced to enhance tumor quantification and detectability in regions with high background activity, where SUV performs relatively poorly. However, in tumor regions of low-uptake the noise can be significant, due to short dynamic frames employed, affecting contrast and quantitative accuracy. Our aim is to apply a direct 4D reconstruction algorithm to reduce the noise, especially in low-uptake regions. Methods Indirect whole-body parametric imaging consists of two distinct phases: initially, independent 3D OSEM reconstructions are conducted at each frame, followed by linear regression, to estimate the slope and intercept parameters of Patlak. Here, we propose a novel direct 4D whole-body reconstruction algorithm that unifies the two steps into a closed-form OSEM scheme, by incorporating the kinetic modeling within the system matrix and, thus, allowing for estimation of the parametric images directly from the available dynamic sinograms. To compare the performance of the two methods, we conducted Monte Carlo simulations of a 4D PET acquisition consisting of a 6min dynamic scan of the heart, followed by 6 whole-body scans. Data from literature were used to model the dynamics of FDG tracer in a set of liver and lung spherical tumors and their background regions in the XCAT phantom. Results By comparing the slope parametric images, we observe enhanced tumor to background contrast (average over 15 realizations) in direct 4D reconstruction, particularly for the low-uptake tumors (lung). Conclusions Direct 4D parametric image reconstruction can enhance tumor detectability and quantification compared to indirect. In addition to image data size reduction and faster generation of parametric images, our proposed method is designed to address challenges particularly associated with whole-body PET parametric imaging, such as presence of high noise, thus contributing to its clinical adoption.

A novel approach for direct reconstruction of parametric images for myocardial blood flow from PET imaging.

Abstract: Purpose: The aim of this study is to develop and evaluate a novel direct reconstruction method to improve the signal-to-noise ratio (SNR) of parametric images in dynamic positron-emission tomography (PET), especially for applications in myocardial perfusion studies. Methods: Simulation studies were used to test the performance in SNR and computational efficiency for different methods. The NCAT phantom was used to generate simulated dynamic data. Noise realization was performed in the sinogram domain and repeated for 30 times with four different noise levels by varying the injection dose (ID) from standard ID to 1/8 of it. The parametric images were calculated by (1) three direct methods that compute the kinetic parameters from the sinogram and (2) an indirect method, which computes the kinetic parameter with pixel-by-pixel curve fitting in image space using weighted least-squares. The first direct reconstruction maximizes the likelihood function using trust-region-reflective (TRR) algorithm. The second approach uses tabulated parameter sets to generate precomputed time-activity curves for maximizing the likelihood functions. The third approach, as a newly proposed method, assumes separable complete data to derive the M-step for maximizing the likelihood. Results: The proposed method with the separable complete data performs similarly to the other two direct reconstruction methods in terms of the SNR, providing a 5%–10% improvement as compared to the indirect parametric reconstruction under the standard ID. The improvement of SNR becomes more obvious as the noise level increases, reaching more than 30% improvement under 1/8 ID. Advantage of the proposed method lies in the computation efficiency by shortening the time requirement to 25% of the indirect approach and 3%–6% of other direct reconstruction methods. Conclusions: With results provided from this simulation study, direct reconstruction of myocardial blood flow shows a high potential for improving the parametric image quality for clinical use.

Direct 4D PET MLEM reconstruction of parametric images using the simplified reference tissue model with the basis function method

Abstract: The purpose of this work is to assess the one-step late maximum likelihood expectation maximization (OSL-MLEM) direct 4D PET reconstruction algorithm when using the simplified reference tissue model with the basis function method (SRTM-BFM) To date, the OSL-MLEM method has been evaluated using kinetic models based on two-tissue compartments with an irreversible component We therefore extend the evaluation of this method for two-tissue compartments with a reversible component, using SRTM-BFM on simulated 2D and 3D + time data sets (with use of [11C]raclopride time-activity curves (TACs) from real data) and on real data sets acquired with the high resolution research tomograph (HRRT) Furthermore, this work investigates the impact of correcting the TACs by the frame length, as assumed by most conventional kinetic parameter estimation techniques (applied post-reconstruction) used in practice The performance of the proposed method is evaluated by comparing binding potential (BP) estimates with those obtained from conventional post-reconstruction kinetic parameter estimation It is shown that, for the 2D + time simulation, SRTM-BFM within the OSL-MLEM framework delivers lower %BIAS and %CV, and thus lower %RMSE, in BP estimates compared to the post reconstruction approach, while for the real 3D data set the method delivers lower spatial %CV, in addition to better BP parametric image quality, when using resolution modeling Finally, frame length correction can be applied but correct weighting is necessary to obtain the best performance

Direct parametric image reconstruction of rapid multi-tracer PET.

Abstract: The separation of multiple PET tracers within an overlapped scan based on intrinsic difference of pharmacokinetics is challenging due to the limited SNR of PET measurements and high complexity of fitting models. This study developed a novel direct parametric reconstruction method by integrating a multi-tracer model with reduced number of fitting parameters into image reconstruction. To incorporate the multi-tracer model, we adopted EM surrogate functions for the optimization of the penalized log-likelihood. The algorithm was validated on realistic simulation phantoms and real rapid [18F]FDG and [18F]FLT PET imaging of mice with lymphoma mouse tumor. Both results have been compared with conventional methods and demonstrated evident improvements for the separation of multiple tracers.

Comparison between different methods for parametric image estimation in analyzer-based phase contrast images

Abstract: Analyzer-based phase contrast imaging (ABI) is an emerging imaging technique capable of visualizing complex interactions between X-rays and an object Like many of the modern imaging techniques, ABI is a computed imaging method where parametric images are estimated from raw data

Parametric Image Restoration Using Consensus: An Application to Nonstationary Noise Filtering

Abstract: Image quality gets affected by unavoidable degradations. Several techniques have been proposed based on a priori information of the degradation. However, these techniques fail when the underlying parameters cannot be estimated. We propose a method to deal with situations when the underlying parameters are not known. It is based on the consensus achieved by using a set of aggregation functions and a penalty function. The method is tested in the case of a nonstationary Gaussian noise, and the Wiener filter is used to prove this methodology. The results show that the approach is consistent and it achieves comparable results for known parameters.

Magnetic resonance and ultrasound parametric image fusion

Abstract: The invention relates to magnetic resonance and ultrasound parametric image fusion. Magnetic resonance and ultrasound parametric images are fused or combined. MRI and ultrasound imaging are used to acquire (30, 32) the same type of parametric images. Fused data is created (44) by combining ultrasound and MRI parametric data at times for which both types of data are available. Rather than sacrificing rate, fused data is created (44) for times for which MRI data is not acquired. A curve representing the values of the parameter over time is fit to the available MRI and ultrasound data of each location, resulting in fused data at times for which MRI data is not available.