A factorized system matrix utilizing an image domain resolution model is attractive in fully 3D time-of-flight PET image reconstruction using list-mode data. In this paper, we study a factored model based on sparse matrix factorization that is comprised primarily of a simplified geometrical projection matrix and an image blurring matrix. Beside the commonly-used Siddon's ray-tracer, we propose another more simplified geometrical projector based on the Bresenham's ray-tracer which further reduces the computational cost. We discuss in general how to obtain an image blurring matrix associated with a geometrical projector, and provide theoretical analysis that can be used to inspect the efficiency in model factorization. In simulation studies, we investigate the performance of the proposed sparse factorization model in terms of spatial resolution, noise properties and computational cost. The quantitative results reveal that the factorization model can be as efficient as a non-factored model, while its computational cost can be much lower. In addition we conduct Monte Carlo simulations to identify the conditions under which the image resolution model can become more efficient in terms of image contrast recovery. We verify our observations using the provided theoretical analysis. The result offers a general guide to achieve the optimal reconstruction performance based on a sparse factorization model with an image domain resolution model.
#2Jinyi Qi(UC Davis: University of California, Davis)H-Index: 52
Last. Vesna Sossi(UBC: University of British Columbia)H-Index: 56
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In this paper, the authors review the field of resolution modeling in positron emission tomography (PET) image reconstruction, also referred to as point-spread-function modeling. The review includes theoretical analysis of the resolution modeling framework as well as an overview of various approaches in the literature. It also discusses potential advantages gained via this approach, as discussed with reference to various metrics and tasks, including lesion detection observer studies. Furthermore...
Iterative reconstructions in positron emission tomography (PET) need a model relating the recorded data to the object/patient being imaged, called the system matrix (SM). The more realistic this model, the better the spatial resolution in the reconstructed images. However, a serious concern when using a SM that accurately models the resolution properties of the PET system is the undesirable edge artefact, visible through oscillations near sharp discontinuities in the reconstructed images. This a...
Purpose: List-mode processing is an efficient way of dealing with the sparse nature of positron emission tomography (PET) data sets and is the processing method of choice for time-of-flight (ToF) PET image reconstruction. However, the massive amount of computation involved in forward projection and backprojection limits the application of list-mode reconstruction in practice, and makes it challenging to incorporate accurate system modeling. Methods: The authors present a novel formulation for co...
Statistically based iterative image reconstruction has been widely used in positron emission tomography (PET) imaging. The quality of reconstructed images depends on the accuracy of the system matrix that defines the mapping from the image space to the data space. However, an accurate system matrix is often associated with high computation cost and huge storage requirement. In this paper, we present a method to address this problem using sparse matrix factorization and graphics processor unit (G...
Positron emission tomography systems are best described by a linear shift-varying model. However, image reconstruction often assumes simplified shift-invariant models to the detriment of image quality and quantitative accuracy. We investigated a shift-varying model of the geometrical system response based on an analytical formulation. The model was incorporated within a list-mode, fully 3D iterative reconstruction process in which the system response coefficients are calculated online on a graph...
Positron emission tomography (PET) measurements with time-of-flight (TOF) information are often very sparse. As a result, direct reconstruction from raw list-mode data is an attractive strategy for dealing with the large dimension spanned by the measurements. However, even though sparse datasets are more efficiently processed in list mode than as sinograms, list-mode reconstruction remains computationally demanding and computer clusters are typically required for reconstructing clinical PET scan...
#2F C Sureau(Vrije Universiteit Brussel)H-Index: 1
Last. Serge Goldman(ULB: Université libre de Bruxelles)H-Index: 67
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Partial volume effect is an important source of bias in PET images that can be lowered by accounting for the point spread function (PSF) of the scanner. We measured such a PSF in various points of a clinical PET scanner and modelled it as a product of matrices acting in image space, taking the asymmetrical, shift-varying and non-Gaussian character of the PSF into account (AMP modelling), and we integrated this accurate image space modelling into a conventional list-mode OSEM algorithm (EM-AMP re...
The interest in positron emission tomography (PET) and particularly in hybrid integrated PET/CT systems has significantly increased in the last few years due to the improved quality of the obtained images. Nevertheless, one of the most important limits of the PET imaging technique is still its poor spatial resolution due to several physical factors originating both at the emission (e.g. positron range, photon non-collinearity) and at detection levels (e.g. scatter inside the scintillating crysta...
Last. Paul E. Kinahan(UW: University of Washington)H-Index: 69
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Accurate system modeling in tomographic image reconstruction has been shown to reduce the spatial variance of resolution and improve quantitative accuracy. System modeling can be improved through analytic calculations, Monte Carlo simulations, and physical measurements. The purpose of this work is to improve clinical fully-3-D reconstruction without substantially increasing computation time. We present a practical method for measuring the detector blurring component of a whole-body positron emis...
#1Jinyi Qi(UC Davis: University of California, Davis)H-Index: 52
#2Samuel Matej(UPenn: University of Pennsylvania)H-Index: 22
Last. Xuezhu Zhang(UC Davis: University of California, Davis)H-Index: 13
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Total-body PET image reconstruction follows a similar procedure to the image reconstruction process for standard whole-body PET scanners. One unique aspect of total-body imaging is simultaneous coverage of the entire human body, which makes it convenient to perform total-body dynamic PET scans. Therefore, four-dimensional dynamic PET reconstruction and parametric imaging are of great interest in total-body imaging. This article covers some basics of PET image reconstruction and then focuses on t...
#1Xuezhu Zhang(UC Davis: University of California, Davis)H-Index: 13
#2Zhaoheng Xie(UC Davis: University of California, Davis)H-Index: 3
Last. Jinyi Qi(UC Davis: University of California, Davis)H-Index: 52
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: Rationale: The world's first 194-cm long total-body PET/CT scanner (uEXPLORER) has been built by the EXPLORER consortium to offer a transformative platform for human molecular imaging in clinical research and healthcare. Its total-body coverage and ultra-high sensitivity provide opportunities for more accurate tracer kinetic analysis in studies of physiology, biochemistry and pharmacology. The objective of this study is to demonstrate the capability of total-body parametric imaging and to quan...
Last. Jinyi Qi(UC Davis: University of California, Davis)H-Index: 52
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Image reconstruction in positron emission tomography (PET), especially from low-count projection data, is challenging due to the ill-posed nature of the inverse problem. Prior information can substantially improve the quality of reconstructed PET images. Previously, a PET image reconstruction method using a convolutional neural network (CNN) representation was proposed. In this work, we replace the original network with a generative adversarial network (GAN) to improve the network performance un...
#2Abass Alavi(HUP: Hospital of the University of Pennsylvania)H-Index: 130
Last. Issam El Naqa(UM: University of Michigan)H-Index: 58
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Quantitative image analysis has deep roots in the usage of positron emission tomography (PET) in clinical and research settings to address a wide variety of diseases. It has been extensively employed to assess molecular and physiological biomarkers in vivo in healthy and disease states, in oncology, cardiology, neurology, and psychiatry. Quantitative PET allows relating the time-varying activity concentration in tissues/organs of interest and the basic functional parameters governing the biologi...
: Filtered backprojection (FBP) algorithms reduce image noise by smoothing the image. Iterative algorithms reduce image noise by noise weighting and regularization. It is believed that iterative algorithms are able to reduce noise without sacrificing image resolution, and thus iterative algorithms, especially maximum-likelihood expectation maximization (MLEM), are used in nuclear medicine to replace FBP algorithms. Methods: This short paper uses counter examples to show that this belief is not t...
: The first generation Tachyon PET (Tachyon-I) is a demonstration single-ring PET scanner that reaches a coincidence timing resolution of 314 ps using LSO scintillator crystals coupled to conventional photomultiplier tubes. The objective of this study was to quantify the improvement in both lesion detection and quantification performance resulting from the improved time-of-flight (TOF) capability of the Tachyon-I scanner. We developed a quantitative TOF image reconstruction method for the Tachyo...
#1Felipe Godinez('KCL': King's College London)H-Index: 4
#2Kuang Gong(UC Davis: University of California, Davis)H-Index: 13
Last. Ramsey D. Badawi(UC Davis: University of California, Davis)H-Index: 29
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A positron emission tomography (PET) scanner with submillimeter spatial resolution, capable of in vivo imaging of murine extremities was built based on two dual ended readout, hybrid depth of interaction (DOI) PET detectors. Each was composed of a 36 {\times } \,\, 36array of 0.43 mm {\times } \,\, 0.43mm {\times } \,\, 8mm unpolished lutetium oxyorthosilicate crystals separated by a 50~\boldsymbol \mu \text{m}white diffuse reflector. The array was coupled to a position-sensing photo...
Abstract Tomographic image reconstruction is a computationally demanding task, even more so when advanced models are used to describe a more complete and accurate picture of the image formation process. Such advanced modeling and reconstruction algorithms can lead to better images, often with less dose, but at the price of long calculation times that are hardly compatible with clinical workflows. Fortunately, reconstruction tasks can often be executed advantageously on Graphics Processing Units ...
'Open-field' PET, in which an animal is free to move within an enclosed space during imaging, is a very promising advance for neuroscientific research. It provides a key advantage over conventional imaging under anesthesia by enabling functional changes in the brain to be correlated with an animal's behavioural response to environmental or pharmacologic stimuli. Previously we have demonstrated the feasibility of open-field imaging of rats using motion compensation techniques applied to a commerc...
#1Xuezhu Zhang(UC Davis: University of California, Davis)H-Index: 13
#2Jian Zhou(UC Davis: University of California, Davis)H-Index: 140
Last. Jinyi Qi(UC Davis: University of California, Davis)H-Index: 52
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The EXPLORER project aims to build a 2 meter long total-body PET scanner, which will provide extremely high sensitivity for imaging the entire human body. It will possess a range of capabilities currently unavailable to state-of-the-art clinical PET scanners with a limited axial field-of-view. The huge number of lines-of-response (LORs) of the EXPLORER poses a challenge to the data handling and image reconstruction. The objective of this study is to develop a quantitative image reconstruction me...
