MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation
Autor: | Jim Ji, Maysam F. Abbod, Lejla Alic, Abbes Amira, Imene Mecheter |
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Rok vydání: | 2020 |
Předmět: |
Computer science
Review Machine learning computer.software_genre 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Neuroimaging medicine Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Segmentation Cluster analysis MR image-based attenuation correction Image segmentation Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Deep learning Brain Magnetic resonance imaging MR Magnetic Resonance Imaging Computer Science Applications PET/MR PET Positron emission tomography Positron-Emission Tomography Artificial intelligence business computer Correction for attenuation 030217 neurology & neurosurgery |
Zdroj: | Journal of Digital Imaging |
ISSN: | 1618-727X |
Popis: | 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. |
Databáze: | OpenAIRE |
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