Parametric Image

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

A Flexible 2D-3D Parametric Image Registration Algorithm for Cardiac MRI

Abstract: We propose a mathematical formulation aimed at parametric intensity-based registration of a deformed 3D volume to a 2D slice. The approach is flexible and can accommodate various regularization schemes, similarity measures, and optimizers. We evaluate the framework on 2D-3D registration experiments of in vivo cardiac magnetic resonance imaging (MRI) aimed at image-guided surgery applications that use of real-time MRI as a visualization tool. An affine transformation is used to demonstrate this parametric model. Target registration error, Jaccard and Dice indices are used to validate the algorithm and demonstrate the accuracy of the registration scheme on both simulated and clinical data.

Direct Image Alignment for Active Near Infrared Image Differencing

Abstract: One of the difficult challenges in face recognition is dealing with the illumination variations that occur in varying environments. A practical and efficient way to address harsh illumination variations is to use active image differencing in near-infrared frequency range. In this method, two types of image frames are taken: an illuminated frame is taken with near infrared illumination, and an ambient frame is taken without the illumination. The difference between face regions of these frames reveals the face image illuminated only by the illumination. Therefore the image is not affected by the ambient illumination and illumination robust face recognition can be achieved. But the method assumes that there is no motion between two frames. Faces in different locations on the two frames introduces a motion artifact. To compensate for motion between two frames, a motion interpolation method has been proposed; but it has limitations, including an assumption that the face motion is linear. In this paper, we propose a new image alignment method that directly aligns the actively illuminated and ambient frames. The method is based on Lucas-Kanade parametric image alignment method and involves a new definition of errors based on the properties of the two types of frames. Experimental results show that the direct method outperforms the motion interpolation method in terms of face recognition rates.

Direct 4D parametric image reconstruction for dynamic cardiac PET imaging

Abstract: 1996 Objectives Conventional dynamic myocardial perfusion PET imaging consists of reconstructing data frames individually followed by compartmental analysis to estimate kinetic parameters. The goal of this study is to develop and evaluate (through simulation) a direct parametric image reconstruction method for dynamic cardiac PET studies. Methods We developed a preconditioned steepest ascent (PSA) method that incorporates the one-tissue compartmental model to directly reconstruct parametric images from dynamic sinogram frames. The log-likelihood function for the direct 4D reconstruction has the myocardial activity represented as the contribution from plasma and myocardial tissue. It was maximized with respect to each of the involved kinetic parameters. Rb-82 PET patient organ time activity curves including blood pool and myocardium were acquired and fitted to generate a set of K1, k2, and vB values. The corresponding parametric images created from the XCAT phantom served as the truth for simulation. Image frames created from the parametric images (and the input function) were projected to generate the sinogram frames. Noise comparable to the clinical data level was added. To evaluate the results, polar maps were created from estimated K1 values on the left ventricular myocardium. We compared the resulted K1 polar maps from the PSA direct reconstruction and that from fitting the individually reconstructed 3D image frames. Results The K1 values on the whole polar map and its segments estimated from the PSA direct reconstruction showed significantly improved bias versus noise performance compared to those from fitting the compartmental model to individually reconstructed image frames. Conclusions A direct parametric image reconstruction method was developed to incorporate kinetic modeling in the reconstruction of dynamic cardiac PET data. With realistic simulation, we have demonstrated improved performance of the proposed technique over the conventional method on estimation of the myocardial rate constants

Relative Patlak Plot for Dynamic PET Parametric Imaging Without the Need for Early-time Input Function

Abstract: The Patlak graphical method is widely used in parametric imaging for modeling irreversible radiotracer kinetics in dynamic PET. The net influx rate of radiotracer can be determined from the slope of the Patlak plot. The implementation of the standard Patlak method requires the knowledge of full-time input function from the injection time until the scan end time, which presents a challenge for use in the clinic. This paper proposes a new relative Patlak plot method that does not require early-time input function and therefore can be more efficient for parametric imaging. Theoretical analysis proves that the effect of early-time input function is a constant scaling factor on the Patlak slope estimation. Thus, the parametric image of the slope of the relative Patlak plot is related to the parametric image of standard Patlak slope by a global scaling factor. This theoretical finding has been further demonstrated by computer simulation and real patient data. The study indicates that parametric imaging of the relative Patlak slope can be used as a substitute of parametric imaging of standard Patlak slope for certain clinical tasks such as lesion detection and tumor volume segmentation.

Multiple Linear Analysis for Generating Parametric Images of Irreversible Radiotracer

Abstract: Purpose: Biological parameters can be quantified using dynamic PET data with compartment modeling and Nonlinear Least Square (NLS) estimation. However, the generation of parametric images using the NLS is not appropriate because of the initial value problem and excessive computation time. In irreversible model, Patlak graphical analysis (PGA) has been commonly used as an alternative to the NLS method. In PGA, however, the start time (, time where linear phase starts) has to be determined. In this study, we suggest a new Multiple Linear Analysis for irreversible radiotracer (MLAIR) to estimate fluoride bone influx rate (Ki). Methods: dynamic PET scans was acquired for 60 min in three normal mini-pigs. The plasma input curve was derived using blood sampling from the femoral artery. Tissue time-activity curves were measured by drawing region of interests (ROls) on the femur head, vertebra, and muscle. Parametric images of Ki were generated using MLAIR and PGA methods. Result: In ROI analysis, estimated Ki values using MLAIR and PGA method was slightly higher than those of NLS, but the results of MLAIR and PGA were equivalent. Patlak slopes (Ki) were changed with different in low uptake region. Compared with PGA, the quality of parametric image was considerably improved using new method. Conclusion: The results showed that the MLAIR was efficient and robust method for the generation of Ki parametric image from PET. It will be also a good alternative to PGA for the radiotracers with irreversible three compartment model.