Parametric Image

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

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.

Novel quantitative whole-body parametric PET imaging utilizing multiple clustering realizations

Abstract: Whole-body parametric PET imaging along with Patlak graphical analysis has the potential to provide improved diagnosis. However, a voxel-based fitting approach for a short dynamic scan protocol results in high statistical noise in the parametric images. The objective of our study is to present the framework of a novel multiple clustering realizations (MCR) method for estimating parametric images with improved image quality. The method relies primarily on using standard k-means clustering for segmenting the time-activity curves within the whole-body volume. In addition, in order to obtain improved accuracy without increasing noise, multiple realizations of clustering were performed. During each realization, cluster centers were selected from a unique ordered set of time-activity curves within the whole body volume. All the remaining data were classified into the cluster centers based on minimum Eucledian distance measure. Patlak analysis was performed on the cluster average to form the slope and intercept images. Parametric images thus obtained for all realizations were averaged. An XCAT phantom based simulations for the torso were performed using dynamic time-activity curves to model FDG uptake. Five dynamic images each representing 1 min scan time with 7 min intervals were created starting 60 minutes post injection. In addition, 5 whole-body dynamic FDG patient datasets with image-derived blood input function and whole-body dynamic data measurements were also used. All dynamic data were reconstructed using OSEM applying corrections for image-degrading factors. Slope and intercept parametric images were obtained for the voxel-fitting and MCR method. Noise in a liver region of interest increased as a function of the number of clusters for the simulated data. On the other hand, bias decreased with increasing number of clusters. However, as number of clustering realizations increased, noise reduced and K i estimates stabilized. The parametric images obtained with MCR method showed better image quality compared to voxel-based fitting method for the patient and simulated datasets. Multiple clustering realizations method has the potential to provide improved parametric image quality for short scan whole-body parametric PET imaging.

Mixture principal component analysis for distribution volume parametric imaging in brain PET studies

Abstract: In this paper, we present a mixture principal component analysis (mPCA)-based approach for voxel level quantification of dynamic positron emission tomography (PET) data in brain studies. The parameters of the probabilistic mixture model are determined using an EM algorithm. The problem of interest here requires neither the accurate arterial blood measurements as the input function nor the existence of a reference region. The effects of mPCA are examined in two different ways on the basis of whether the compartmental model for tracer dynamics is considered. First, the mPCA approach itself is used to classify all voxels into the specific binding and non-specific binding groups, and the resulting power is used for revealing the underlying distribution volume (DV) image. Second, the proposed mPCA-based classification approach is incorporated as the clustering preprocessing into our earlier work to simultaneously estimate the DV parametric image and the input function. The efficiency and superiority of the proposed scheme is demonstrated by real brain PET data.

Comparison of Algorithms for Generating Parametric Image of Cerebral Blood Flow Using ${H_2}^{15}O$ PET Positron Emission Tomography

Abstract: Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS) dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared In audition, parametric images from dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for images on Pentium III processor Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation