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 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 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

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

Fingerprint Dive into the research topics of 'Nonlinear PET parametric image reconstruction with MRI information using kernel method'. Together they form a unique fingerprint.

Cite this