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Future Trends of PET/MR and Utility of AI in Multi-Modal Imaging

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Hybrid PET/MR Neuroimaging

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Abstract

Since the first clinical simultaneous positron emission tomography/magnetic resonance imaging (PET/MR) units were developed in 2010 as predominantly research tools, PET/MR has been frequently utilized in clinical practice for the evaluation of neurological diseases, including brain tumors, neurodegenerative disorders, and epilepsy as well as head and neck cancers. Compared to PET/CT, PET/MR has the inherent advantages of MRI, including superior tissue contrast, capability of multiparametric images, and lack of ionizing radiation. Also, a single-session PET/MR reduces the transportation time between examination rooms, and for pediatric patients, it reduces the risk of sedation. However, there are also several disadvantages when shifting from PET/CT to PET/MR as follows:

  1. 1.

    Scan time. Although PET/MR reduces the number of imaging sessions, the average scan time of PET/MR (~1 h) is longer than that of PET/CT (30 mins) leading to a higher chance of motion artifacts and requiring sedation in pediatric patients.

  2. 2.

    Quantitative accuracy. Quantitative accuracy in PET relies on accurate attenuation correction (AC) maps and motion correction. AC is known as a challenge in PET/MR because there is no direct relationship between the PET attenuation coefficients and the intensity of the MR signal contrary to PET/CT.

Here we address recent advances in PET/MR in the evaluation of several neurological disorders. First, we discuss how recent major technological advances and trends provide solutions to the above issues. Second, we discuss recent progresses of artificial intelligence (AI) in coping with two common challenges in multi-modal imaging research: missing data and representation of multi-modal data.

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Author information

Authors and Affiliations

  1. Department of Radiology, University of North Carolina, Chapel Hill, NC, USA

    Sheng-Che Hung, Mingxia Liu, Pew-Thian Yap & Weili Lin

  2. Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA

    Sheng-Che Hung, Mingxia Liu, Pew-Thian Yap & Weili Lin

  3. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

    Dinggang Shen

  4. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

    Dinggang Shen & Mauricio Castillo

Authors
  1. Sheng-Che Hung
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  2. Mingxia Liu
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  3. Pew-Thian Yap
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  4. Dinggang Shen
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  5. Weili Lin
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  6. Mauricio Castillo
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Corresponding authors

Correspondence to Sheng-Che Hung or Mingxia Liu .

Editor information

Editors and Affiliations

  1. Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, The Feinstein Institutes for Medical Research, Manhasset, NY, USA

    Ana M. Franceschi

  2. Stony Brook University Hospital, Stony Brook, NY, USA

    Dinko Franceschi

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Hung, SC., Liu, M., Yap, PT., Shen, D., Lin, W., Castillo, M. (2022). Future Trends of PET/MR and Utility of AI in Multi-Modal Imaging. In: Franceschi, A.M., Franceschi, D. (eds) Hybrid PET/MR Neuroimaging. Springer, Cham. https://doi.org/10.1007/978-3-030-82367-2_9

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  • DOI: https://doi.org/10.1007/978-3-030-82367-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82366-5

  • Online ISBN: 978-3-030-82367-2

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