Parametric Image

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

Parametric analysis of main motion to study the regional wall motion of the left ventricle in echocardiography

Abstract: Parameters which are related to the wall motion of the left ventricle are estimated from the time signal amplitude curves of the pixels of an echocardiographic image sequence. They yield parametric images, which are interpreted by clinicians. Factor analysis of the left ventricle in echocardiography is such a method. Its theoretical limits are first listed. To overcome them, a generalized method, the parametric analysis of main motion (PAMM), is proposed in this paper. The new parameters are computed as the projections of the time signal amplitude curves on a set of curves, which are generated by translation and scale transformations of a mother curve. This mother curve represents a generic motion curve. The PAMM method has been tested on a simulated sequence and applied to patients. Results demonstrate the theoretical interest of the method and show its potential contribution to clinical studies.

Direct List Mode Parametric Reconstruction for Dynamic Cardiac SPECT

Abstract: Recently introduced stationary dedicated cardiac SPECT scanners provide new opportunities to quantify myocardial blood flow (MBF) using dynamic SPECT. However, comparing to PET, the low sensitivity of SPECT scanners affects MBF quantification due to the high noise level, especially for 201 Thallium (201Tl) due to its typically low injected dose. The conventional indirect method for generating parametric images typically starts by reconstructing a time series of frame images followed by fitting the time-activity curve (TAC) for each voxel or segment with an appropriate kinetic model. The indirect method is simple and easy to implement; however, it usually suffers from substantial image noise that could also lead to bias. In this paper, we developed a list mode direct parametric image reconstruction algorithm to substantially reduce noise in MBF quantification using dynamic SPECT and allow for patient radiation dose reduction. GPU-based parallel computing was used to achieve more than 2000-fold acceleration. The proposed method was evaluated in both simulation and in vivo canine studies. Compared with the indirect method, the proposed direct method achieved substantially lower image noise and variability, particularly at large number of iterations and at low-count levels.

Reconstruction of kinetic parameter images directly from dynamic PET sinograms

Abstract: Recently, there has been interest in estimating kinetic model parameters for each voxel in a PET image. To do this, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated by fitting a model to the reconstructed time-activity response of each voxel. However, this indirect approach to kinetic parameter estimation tends to reduce signal-to-noise ratio (SNR) because of the requirement that the sinogram data be divided into individual time frames. In 1985, Carson and Lange proposed, but did not implement, a method based on the EM algorithm for direct parametric reconstruction. More recently, researchers have developed semi-direct methods which use spline-based reconstruction, or direct methods for estimation of kinetic parameters from image regions. However, direct voxel-wise parametric reconstruction has remained a challenge due to the unsolved complexities of inversion and required spatial regularization. In this work, we demonstrate an efficient method for direct voxel-wise reconstruction of kinetic parameters (as a parametric image) from all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem. This PICD algorithm is computationally efficient and allows the physiologically important kinetic parameters to be spatially regularized. Our experimental simulations demonstrate that direct parametric reconstruction can substantially reduce estimation error of kinetic parameters as compared to indirect methods.

Estimation of ultrasound attenuation coefficient using log-spectrum domain processing

Abstract: Ultrasound attenuation coefficient is an important diagnostic parameter in medical ultrasonography Furthermore, it is a parameter of a component related to the attenuation process of the space-variant point spread function, which can be used to improve the spatial resolution of ultrasound images through deconvolution A recently published approach to the estimation of the ultrasound attenuation coefficient from B-scan radiofrequency data is extended and explained in a detail First, a parametric image of the mean attenuation coefficients between the probe and a given pixel position is computed by applying linear regression to log-spectra of short segments of radiofrequency signals Three methods of forming the parametric image are presented As a second step, the local tissue-specific attenuation coefficients are estimated in small regions of the obtained parametric image The method has been tested on synthetic radiofrequency data and on radiofrequency data recorded from a tissue-mimicking phantom A fairly good correlation with the known reference values was achieved