Sinogram Blurring Matrix Estimation From Point Sources Measurements With Rank-One Approximation for Fully 3-D PET
Reads0
Chats0
TLDR
A rank-one approximation for each blurring kernel image formed by a row vector of the sinogram blurring matrix is proposed to improve the stability of the 4-D blurring matrices estimation.Abstract:
An accurate system matrix is essential in positron emission tomography (PET) for reconstructing high quality images To reduce storage size and image reconstruction time, we factor the system matrix into a product of a geometry projection matrix and a sinogram blurring matrix The geometric projection matrix is computed analytically and the sinogram blurring matrix is estimated from point source measurements Previously, we have estimated a 2-D blurring matrix for a preclinical PET scanner The 2-D blurring matrix only considers blurring effects within a transaxial sinogram and does not compensate for inter-sinogram blurring effects For PET scanners with a long axial field of view, inter-sinogram blurring can be a major problem influencing the image quality in the axial direction Hence, the estimation of a 4-D blurring matrix is desirable to further improve the image quality The 4-D blurring matrix estimation is an ill-conditioned problem due to the large number of unknowns Here, we propose a rank-one approximation for each blurring kernel image formed by a row vector of the sinogram blurring matrix to improve the stability of the 4-D blurring matrix estimation The proposed method is applied to the simulated data as well as the real data obtained from an Inveon microPET scanner The results show that the newly estimated 4-D blurring matrix can improve the image quality over those obtained with a 2-D blurring matrix and requires less point source scans to achieve similar image quality compared with an unconstrained 4-D blurring matrix estimationread more
Citations
More filters
Journal ArticleDOI
PET Image Reconstruction Using Deep Image Prior
TL;DR: Quantification results based on simulation and real data show that the proposed reconstruction framework can outperform Gaussian post-smoothing and anatomically guided reconstructions using the kernel method or the neural-network penalty.
Journal ArticleDOI
PET Image Denoising Using a Deep Neural Network Through Fine Tuning
TL;DR: A deep convolutional neural network was trained to improve PET image quality by employingceptual loss based on features derived from a pretrained VGG network instead of the conventional mean squared error to preserve image details.
Journal ArticleDOI
Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images.
TL;DR: Deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images are presented and a modified U-net structure is proposed, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper.
Journal ArticleDOI
Corrections to “Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity”
TL;DR: This work examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals and shows that the method that combines PET temporal information and MRI spatial information adaptively based on the structure similarity index has the best performance in terms of noise reduction and resolution improvement.
Journal ArticleDOI
Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction
TL;DR: In this article, machine learning techniques have been applied to estimate the position and arrival time of high-energy photons using waveform digitizers for time-of-flight positron emission tomography and quantitative image reconstruction.
References
More filters
Journal ArticleDOI
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
TL;DR: It is shown that if a certain restricted isometry property holds for the linear transformation defining the constraints, the minimum-rank solution can be recovered by solving a convex optimization problem, namely, the minimization of the nuclear norm over the given affine space.
Journal Article
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
TL;DR: In this paper, it was shown that if a certain restricted isometry property holds for the linear transformation defining the constraints, the minimum-rank solution can be recovered by solving a convex optimization problem, namely, the minimization of the nuclear norm over the given affine space.
Proceedings ArticleDOI
A rank minimization heuristic with application to minimum order system approximation
TL;DR: It is shown that the heuristic to replace the (nonconvex) rank objective with the sum of the singular values of the matrix, which is the dual of the spectral norm, can be reduced to a semidefinite program, hence efficiently solved.
Journal ArticleDOI
Exact and approximate rebinning algorithms for 3-D PET data
TL;DR: This paper presents two new rebinning algorithms for the reconstruction of three-dimensional (3-D) positron emission tomography (PET) data that are approximate but allows an efficient implementation based on taking 2-D Fourier transforms of the data.
Journal ArticleDOI
Calculation of positron range and its effect on the fundamental limit of positron emission tomography system spatial resolution
Craig S. Levin,Edward J. Hoffman +1 more
TL;DR: A Monte Carlo simulation code is developed that models positron trajectories and calculates the distribution of the end point coordinates in water for the most common PET isotopes used: 18F, 13N, 11C and 15O to calculate what effect positron range has on the overall PET system spatial resolution, and how this influences the choice of PET system design parameters such as detector element size and system diameter.