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 proceedingConference contribution

3 Citations (Scopus)

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 languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationPhysics of Medical Imaging
PublisherSPIE
Volume10132
ISBN (Electronic)9781510607095
DOIs
StatePublished - Jan 1 2017
EventMedical Imaging 2017: Physics of Medical Imaging - Orlando, United States
Duration: Feb 13 2017Feb 16 2017

Other

OtherMedical Imaging 2017: Physics of Medical Imaging
CountryUnited States
CityOrlando
Period2/13/172/16/17

Fingerprint

Positron emission tomography
Computer-Assisted Image Processing
Magnetic resonance
image reconstruction
Image reconstruction
Positron-Emission Tomography
magnetic resonance
positrons
tomography
Magnetic Resonance Imaging
Imaging techniques
cardiology
neurology
Cardiology
Oncology
multipliers
Neurology
compartments
Image quality
learning

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

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

Nonlinear PET parametric image reconstruction with MRI information using kernel method. / Gong, Kuang; Wang, Guobao; Chen, Kevin T.; Catana, Ciprian; Qi, Jinyi.

Medical Imaging 2017: Physics of Medical Imaging. Vol. 10132 SPIE, 2017. 101321G.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gong, K, Wang, G, Chen, KT, 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, Medical Imaging 2017: Physics of Medical Imaging, Orlando, United States, 2/13/17. https://doi.org/10.1117/12.2254273
Gong K, Wang G, Chen KT, Catana C, Qi J. Nonlinear PET parametric image reconstruction with MRI information using kernel method. In Medical Imaging 2017: Physics of Medical Imaging. Vol. 10132. SPIE. 2017. 101321G https://doi.org/10.1117/12.2254273
Gong, Kuang ; Wang, Guobao ; Chen, Kevin T. ; Catana, Ciprian ; Qi, Jinyi. / Nonlinear PET parametric image reconstruction with MRI information using kernel method. Medical Imaging 2017: Physics of Medical Imaging. Vol. 10132 SPIE, 2017.
@inproceedings{906b5ac9968f471a8e14d908161c1213,
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",
year = "2017",
month = "1",
day = "1",
doi = "10.1117/12.2254273",
language = "English (US)",
volume = "10132",
booktitle = "Medical Imaging 2017",
publisher = "SPIE",

}

TY - GEN

T1 - Nonlinear PET parametric image reconstruction with MRI information using kernel method

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

UR - http://www.scopus.com/inward/record.url?scp=85020429584&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020429584&partnerID=8YFLogxK

U2 - 10.1117/12.2254273

DO - 10.1117/12.2254273

M3 - Conference contribution

VL - 10132

BT - Medical Imaging 2017

PB - SPIE

ER -