Prediction of standard-dose brain PET image by using MRI and low-dose brain [<sup>18</sup>F]FDG PET images

Jiayin Kang, Yaozong Gao, Feng Shi, David S. Lalush, Weili Lin, Dinggang Shen

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [<sup>18</sup>F]FDG PET image by using a low-dose brain [<sup>18</sup>F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [<sup>18</sup>F]FDG PET image by low-dose brain [<sup>18</sup>F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [<sup>18</sup>F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results:The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [<sup>18</sup>F]FDG PET image and substantially enhanced image quality of low-dose brain [<sup>18</sup>F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [<sup>18</sup>F]FDG PET image using low-dose brain [<sup>18</sup>F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [<sup>18</sup>F]FDG PET can be well-predicted using MRI and low-dose brain [<sup>18</sup>F]FDG PET.

Original languageEnglish
Pages (from-to)5301-5309
Number of pages9
JournalMedical Physics
Volume42
Issue number9
DOIs
Publication statusPublished - 2015 Sep 1

Keywords

  • brain [<sup>18</sup>F]FDG PET prediction
  • positron emission tomography (PET)
  • regression forest
  • [<sup>18</sup>F]FDG

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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