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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Bibliographic Details
Published in:Journal of digital imaging 2020-10, Vol.33 (5), p.1224-1241
Main Authors: Mecheter, Imene, Alic, Lejla, Abbod, Maysam, Amira, Abbes, Ji, Jim
Format: Article
Language:English
Subjects:
Attenuation
Brain
Clustering
Computed tomography
Deep learning
Image classification
Image processing
Image segmentation
Imaging
Learning algorithms
Literature reviews
Machine learning
Magnetic resonance imaging
Medical imaging
Medicine
Medicine & Public Health
Neuroimaging
Positron emission
Positron emission tomography
Radiology
Review
Tomography
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Summary: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.
ISSN:0897-1889
1618-727X
DOI:10.1007/s10278-020-00361-x