Joint estimation of activity image and attenuation sinogram using time-of-flight positron emission tomography data consistency condition filtering

Li, Quanzheng; Li, Hao; Kim, Kyungsang; Fakhri, Georges El

doi:10.1117/1.jmi.4.2.023502

Cited by 6 publications

(3 citation statements)

References 30 publications

(32 reference statements)

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“…Pseudo CT will be created by non-rigidly registering the atlas to patient MR images (Wollenweber et al 2013, Burgos et al 2014, Izquierdo-Garcia et al 2014, Yang et al 2017a). With the availability of time-of-flight (TOF) information, emission based methods have been developed to estimate the activity image and the attenuation map simultaneously without the use of MR information (Defrise et al 2012, Rezaei et al 2012, Li et al 2017), or aided by MR information (Mehranian and Zaidi 2015, Kim et al 2016, Mehranian et al 2017). Finally, there are efforts adopting machine learning based approaches to pseudo CT generation driven by prior MR and CT pairs, such as the random forest (Huynh et al 2016) and neural network methods(Han 2017, Nie et al 2017, Liu et al 2017, Leynes et al 2017b).…”

Section: Introductionmentioning

confidence: 99%

Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images

et al. 2018

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Positron emission tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as magnetic resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior to other Dixon-based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure.

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Section: Introductionmentioning

confidence: 99%

Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images

et al. 2018

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“…This has further been extended toward simultaneous estimation of attenuation and activity. 109 Alternatively, TOF PET data can be naturally stored in the histo-image format without information loss, and the DIRECT approach can be used for efficient 3-D TOF PET reconstruction. [110][111][112] Thanks to the histo-image parameterization, the consistency equations in histo-image format are more concise than in the sinogram format.…”

Section: B Analytic Attenuation Estimationmentioning

confidence: 99%

Attenuation correction in emission tomography using the emission data—A review

Berker

2016

Medical Physics

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The problem of attenuation correction (AC) for quantitative positron emission tomography (PET) had been considered solved to a large extent after the commercial availability of devices combining PET with computed tomography (CT) in 2001; single photon emission computed tomography (SPECT) has seen a similar development. However, stimulated in particular by technical advances toward clinical systems combining PET and magnetic resonance imaging (MRI), research interest in alternative approaches for PET AC has grown substantially in the last years. In this comprehensive literature review, the authors first present theoretical results with relevance to simultaneous reconstruction of attenuation and activity. The authors then look back at the early history of this research area especially in PET; since this history is closely interwoven with that of similar approaches in SPECT, these will also be covered. We then review algorithmic advances in PET, including analytic and iterative algorithms. The analytic approaches are either based on the Helgason-Ludwig data consistency conditions of the Radon transform, or generalizations of John's partial differential equation; with respect to iterative methods, we discuss maximum likelihood reconstruction of attenuation and activity (MLAA), the maximum likelihood attenuation correction factors (MLACF) algorithm, and their offspring. The description of methods is followed by a structured account of applications for simultaneous reconstruction techniques: this discussion covers organ-specific applications, applications specific to PET/MRI, applications using supplemental transmission information, and motion-aware applications. After briefly summarizing SPECT applications, we consider recent developments using emission data other than unscattered photons. In summary, developments using time-of-flight (TOF) PET emission data for AC have shown promising advances and open a wide range of applications. These techniques may both remedy deficiencies of purely MRI-based AC approaches in PET/MRI and improve standalone PET imaging. C

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“…This implies TOF information removes cross-talk artifacts in JE solutions, except for scaling, in theory. Because of its applicability to PET/MR and/or recent developments in TOF technologies, JE methods have recently attracted considerable interest (Cheng et al 2016a, Li et al 2017, Mihlin and Levin 2017. For an extensive overview on the subject of JE the interested reader is referred to the review paper by Berker and Li (2016).…”

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confidence: 99%

Joint estimation of activity and attenuation for PET using pragmatic MR-based prior: application to clinical TOF PET/MR whole-body data for FDG and non-FDG tracers

et al. 2018

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Accurate and robust attenuation correction remains challenging in hybrid PET/MR particularly for torsos because it is difficult to segment bones, lungs and internal air in MR images. Additionally, MR suffers from susceptibility artifacts when a metallic implant is present. Recently, joint estimation (JE) of activity and attenuation based on PET data, also known as maximum likelihood reconstruction of activity and attenuation, has gained considerable interest because of (1) its promise to address the challenges in MR-based attenuation correction (MRAC), and (2) recent advances in time-of-flight (TOF) technology, which is known to be the key to the success of JE. In this paper, we implement a JE algorithm using an MR-based prior and evaluate the algorithm using whole-body PET/MR patient data, for both FDG and non-FDG tracers, acquired from GE SIGNA PET/MR scanners with TOF capability. The weight of the MR-based prior is spatially modulated, based on MR signal strength, to control the balance between MRAC and JE. Large prior weights are used in strong MR signal regions such as soft tissue and fat (i.e. MR tissue classification with a high degree of certainty) and small weights are used in low MR signal regions (i.e. MR tissue classification with a low degree of certainty). The MR-based prior is pragmatic in the sense that it is convex and does not require training or population statistics while exploiting synergies between MRAC and JE. We demonstrate the JE algorithm has the potential to improve the robustness and accuracy of MRAC by recovering the attenuation of metallic implants, internal air and some bones and by better delineating lung boundaries, not only for FDG but also for more specific non-FDG tracers such as Ga-DOTATOC andF-Fluoride.

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Joint estimation of activity image and attenuation sinogram using time-of-flight positron emission tomography data consistency condition filtering

Cited by 6 publications

References 30 publications

Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images

Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images

Attenuation correction in emission tomography using the emission data—A review

Joint estimation of activity and attenuation for PET using pragmatic MR-based prior: application to clinical TOF PET/MR whole-body data for FDG and non-FDG tracers

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