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

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...

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Published in:Journal of Digital Imaging Vol. 33; no. 5; pp. 1224 - 1242
Main Authors: Mecheter, Imene, Alic, Lejla, Abbod, Maysam, Amira, Abbes, Ji, Jim
Format: diagnostic images review tables/charts Journal Article
Published: Springer Nature Oct2020
Online Access:View this record in EBSCOhost
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      dt: Oct2020
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-020-00361-x
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        atl: MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation.
      aug:
        au:
          Mecheter, Imene
          Alic, Lejla
          Abbod, Maysam
          Amira, Abbes
          Ji, Jim
        affil: Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK
      sug:
        subj:
          Brain Radiography
          Tomography, Emission-Computed Methods
          Magnetic Resonance Imaging
          Image Processing, Computer Assisted Methods
          Machine Learning
          Deep Learning
      ab: 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.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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