Parametric Image

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

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.

Continuous-wave phase-sensitive parametric image amplification

Abstract: We study experimentally parametric amplification in the continuous regime using a transverse-degenerate type-II Optical Parametric Oscillator operated below threshold. We demonstrate that this device is able to amplify either in the phase insensitive or phase sensitive way first a single mode beam, then a multimode image. Furthermore the total intensities of the amplified image projected on the signal and idler polarizations are shown to be correlated at the quantum level.

Medical ultrasound pressure gradient measurement

Abstract: Flow information (e.g., velocity and acceleration) and/or pressure gradient information are determined with ultrasound. Both velocity and acceleration are simultaneously estimated using a model and least squares analysis. Anti-aliasing of velocity information may be provided using the model and least squares analysis. Pressure gradient is calculated from velocity information automatically, more likely providing consistent measurements. Consistency may be increased further by automatically positioning a region of interest in either a multidimensional spatial image or a spatial-temporal image. A parametric image of pressure gradient for each spatial location within the image is generated as well as a delta pressure curve. Any single one or combinations of two or more of the features described above may be used.

Image-derived input function derived from a supervised clustering algorithm: methodology and validation in a clinical protocol using [11C](R)-rolipram.

Abstract: Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [11C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter- and intra-operator variability. To overcome this limitation, we developed a fully automated technique for deriving IDIF with a supervised clustering algorithm (SVCA). To validate this technique, 25 healthy controls and 26 patients with moderate to severe major depressive disorder (MDD) underwent T1-weighted brain magnetic resonance imaging (MRI) and a 90-minute [11C](R)-rolipram PET scan. For each subject, metabolite-corrected input function was measured from the radial artery. SVCA templates were obtained from 10 additional healthy subjects who underwent the same MRI and PET procedures. Cluster-IDIF was obtained as follows: 1) template mask images were created for carotid and surrounding tissue; 2) parametric image of weights for blood were created using SVCA; 3) mask images to the individual PET image were inversely normalized; 4) carotid and surrounding tissue time activity curves (TACs) were obtained from weighted and unweighted averages of each voxel activity in each mask, respectively; 5) partial volume effects and radiometabolites were corrected using individual arterial data at four points. Logan-distribution volume (VT/fP) values obtained by cluster-IDIF were similar to reference results obtained using arterial data, as well as those obtained using manual-IDIF; 39 of 51 subjects had a VT/fP error of 10%. With automatic voxel selection, cluster-IDIF curves were less noisy than manual-IDIF and free of operator-related variability. Cluster-IDIF showed widespread decrease of about 20% [11C](R)-rolipram binding in the MDD group. Taken together, the results suggest that cluster-IDIF is a good alternative to full arterial input function for estimating Logan-VT/fP in [11C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.

Parametric Image Reconstruction for Edge Recovery From Synthetic Aperture Radar Echoes

Abstract: The edges of a target provide essential geometric information and are extremely important for human visual perception and image recognition. However, due to the coherent superposition of received echoes, the continuous edges of targets are discretized in synthetic aperture radar (SAR) images, i.e., the edges become dispersed points, which seriously affects the extraction of visual and geometric information from SAR images. In this article, we focus on solving the problem of how to recover smooth linear edges (SLEs). By introducing multiangle observations, we propose an SAR parametric image reconstruction method (SPIRM) that establishes a parametric framework to recover SLEs from SAR echoes. At the core of the SPIRM is a novel physical characteristic parameter called the scattering-phase-mutation feature (SPMF), which reveals the most essential difference between the residual endpoints of a disappeared SLE and points. Numerical simulations and real-data experiments demonstrate the robustness and effectiveness of the proposed method.