Parametric Image

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

The measurement of blood flow waveforms from X-ray angiography. Part 1: Principles of the method and preliminary validation

Abstract: The principles and implementation of a method for measurement of blood flow waveforms from X-ray angiography are described. Contrast medium mass values are obtained at multitudinous positions along individual vessels and from numerous images in a time sequence. These values are represented as a matrix of grey levels in a parametric image. This image is normalized to represent contrast medium concentration, and the movement over time of isoconcentration portions of the contrast bolus is recovered to determine blood flow. Preliminary validation has been undertaken using parametric images generated in two ways: synthesis from a computer model of vascular pulsatile flow and analysis of cine-angiograms of physical models (plastic and perspex tubes) carrying known pulsatile flows. Two distinct methods for interrogation of parametric images by digital image processing were employed; both provided accurate flow measurements.

Holography and Parametric Image Conversion: Part II

Abstract: It is shown that image upconversion using optically nonlinear materials is equivalent to a conventional hologram constructed at the signal wavelength and readout at the upconverted wavelength. As such, it should be a truly three‐dimensional, high‐resolution process. For the particular case of equal signal and pump wavelengths, the upconverted image will have only half the angular extent of the original object wave. The image's transverse dimensions remain unchanged while its location relative to the frequency doubler and its longitudinal dimensions double in size. It is also shown how the phase matching condition follows from the vector condition for the reconstruction of a thick hologram.

CT-guided PET parametric image reconstruction using deep neural network without prior training data

Abstract: Deep neural networks have attracted growing interests in medical image due to its success in computer vision tasks. One barrier for the application of deep neural networks is the need of large amounts of prior training pairs, which is not always feasible in clinical practice. Recently, the deep image prior framework shows that the convolutional neural network (CNN) can learn intrinsic structure information from the corrupted image. In this work, an iterative parametric reconstruction framework is proposed using deep neural network as constraint. The network does not need prior training pairs, but only the patient’s own CT image. The training is based on Logan plot derived from multi-bed-position dynamic positron emission tomography (PET) images using 68Ga-PRGD2 tracer. We formulated the estimation of the slope of Logan plot as a constraint optimization problem and solved it using the alternating direction method of multipliers (ADMM) algorithm. Quantification results based on real patient dataset shows that the proposed parametric reconstruction method is better than the Gaussian denoising and non-local mean denoising methods.

Comparison of parametric FBP and OS-EM reconstruction algorithm images for PET dynamic study.

Abstract: An ordered subsets expectation maximization (OS-EM) algorithm is used for image reconstruction to suppress image noise and to make non-negative value images. We have applied OS-EM to a digital brain phantom and to human brain 18F-FDG PET kinetic studies to generate parametric images. A 45 min dynamic scan was performed starting injection of FDG with a 2D PET scanner. The images were reconstructed with OS-EM (6 iterations, 16 subsets) and with filtered backprojection (FBP), and K1, k2 and k3 images were created by the Marquardt non-linear least squares method based on the 3-parameter kinetic model. Although the OS-EM activity images correlated fairly well with those obtained by FBP, the pixel correlations were poor for the k2 and k3 parametric images, but the plots were scattered along the line of identity and the mean values for K1, k2 and k3 obtained by OS-EM were almost equal to those by FBP. The kinetic fitting error for OS-EM was no smaller than that for FBP. The results suggest that OS-EM is not necessarily superior to FBP for creating parametric images.

Improvement in echocardiographic evaluation of left ventricular wall motion using still-frame parametric imaging

Abstract: Background: Conventional echocardiographic assessment of left ventricular wall motion is based on visual interpretation of dynamic images, which depends on readers' experience. We tested the feasibility of evaluating endocardial motion using still-frame parametric images. Methods and Results: In protocol 1, integrated backscatter images were obtained in 8 anesthetized pigs at baseline, 5, and 60 seconds after left anterior descending coronary occlusion and during reperfusion. Images from 1 cardiac cycle were analyzed offline to create a parametric image of local video intensity oscillations. Ischemia-induced changes were quantified by segmenting the parametric images and calculating regional pixel-intensity profiles. In protocol 2, parametric images were obtained from contrast-enhanced echocardiograms in 30 patients (18 with wall-motion abnormalities; 12 control subjects). "Gold standard" for wall motion was determined from independent interpretations of dynamic images made by 3 experienced reviewers. Dynamic images were independently classified by 3 inexperienced and 3 intermediate-level readers. Interpretation was then repeated in combination with parametric images. Parametric images showed a bright band in the area spanned by endocardial motion, which gradually decreased in brightness and thickness in the left anterior descending territory during coronary occlusion in all animals. In patients, the agreement with the gold standard correlated with the readers' experience (68% inexperienced, 87% intermediate) and significantly improved by adding parametric images (83% and 91%, respectively). Conclusion: Parametric imaging provides a still-frame display of regional endocardial motion, sensitive to track ischemia-induced abnormalities. When combined with dynamic images, this technique improves the accuracy of the interpretation of wall motion, especially by less experienced echocardiographers. (J Am Soc Echocardiogr 2002;15:926-34.)