Nonlinear PET parametric image reconstruction with MRI information using kernel method

Kuang Gong; Guobao Wang; Kevin T. Chen; Ciprian Catana; Jinyi Qi

doi:10.1117/12.2254273

Nonlinear PET parametric image reconstruction with MRI information using kernel method

Kuang Gong, Guobao Wang, Kevin T. Chen, Ciprian Catana, Jinyi Qi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.

Original language	English (US)
Title of host publication	Medical Imaging 2017
Subtitle of host publication	Physics of Medical Imaging
Publisher	SPIE
Volume	10132
ISBN (Electronic)	9781510607095
DOIs	https://doi.org/10.1117/12.2254273
State	Published - Jan 1 2017
Event	Medical Imaging 2017: Physics of Medical Imaging - Orlando, United States Duration: Feb 13 2017 → Feb 16 2017

Other

Other	Medical Imaging 2017: Physics of Medical Imaging
Country	United States
City	Orlando
Period	2/13/17 → 2/16/17

Keywords

Direct parametric reconstruction
Dynamic PET
Kernel method
MRI prior

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
Electronic, Optical and Magnetic Materials
Biomaterials
Radiology Nuclear Medicine and imaging

Access to Document

10.1117/12.2254273

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Cite this

Gong, K., Wang, G., Chen, K. T., Catana, C., & Qi, J. (2017). Nonlinear PET parametric image reconstruction with MRI information using kernel method. In Medical Imaging 2017: Physics of Medical Imaging (Vol. 10132). [101321G] SPIE. https://doi.org/10.1117/12.2254273

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title = "Nonlinear PET parametric image reconstruction with MRI information using kernel method",

abstract = "Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.",

keywords = "Direct parametric reconstruction, Dynamic PET, Kernel method, MRI prior",

author = "Kuang Gong and Guobao Wang and Chen, {Kevin T.} and Ciprian Catana and Jinyi Qi",

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doi = "10.1117/12.2254273",

language = "English (US)",

volume = "10132",

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note = "Medical Imaging 2017: Physics of Medical Imaging ; Conference date: 13-02-2017 Through 16-02-2017",

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AU - Gong, Kuang

AU - Wang, Guobao

AU - Chen, Kevin T.

AU - Catana, Ciprian

AU - Qi, Jinyi

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.

AB - Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.

KW - Direct parametric reconstruction

KW - Dynamic PET

KW - Kernel method

KW - MRI prior

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