Direct Patlak Reconstruction from Dynamic PET Data Using the Kernel Method with MRI Information Based on Structural Similarity

Kuang Gong, Jinxiu Cheng-Liao, Guobao Wang, Kevin T. Chen, Ciprian Catana, Jinyi Qi

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Positron emission tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. 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 into image reconstruction. Previously, kernel learning has been successfully embedded into static and dynamic PET image reconstruction using either PET temporal or MRI information. Here, we combine both PET temporal and MRI information adaptively to improve the quality of direct Patlak reconstruction. We examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals. Computer simulations and hybrid real-patient data acquired on a simultaneous PET/MR scanner were used to evaluate the proposed methods. Results show that the method that combines PET temporal information and MRI spatial information adaptively based on the structure similarity index has the best performance in terms of noise reduction and resolution improvement.

Original languageEnglish (US)
Pages (from-to)955-965
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume37
Issue number4
DOIs
StatePublished - Apr 1 2018

Keywords

  • kernel method
  • MRI
  • Patlak direct reconstruction
  • Positron emission tomography
  • structure similarity

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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