MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Imene Mecheter*, Lejla Alic, Maysam Abbod, Abbes Amira, Jim Ji

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

1 Citation (Scopus)
19 Downloads (Pure)

Abstract

Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.

Original languageEnglish
Pages (from-to)1224-1241
Number of pages18
JournalJournal of Digital Imaging
Volume33
Issue number5
Early online date30 Jun 2020
DOIs
Publication statusPublished - 1 Oct 2020

Keywords

  • Deep learning
  • Image segmentation
  • Machine learning
  • MR image-based attenuation correction
  • PET/MR
  • MR
  • PET

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