Vision 20/20: magnetic resonance imaging-guided attenuation correction in PET/MRI: challenges, solutions, and opportunities
Open accessAbstract
Attenuation correction is an essential component of the long chain of data correction techniques required to achieve the full potential of quantitative positron emission tomography (PET) imaging. The development of combined PET/magnetic resonance imaging (MRI) systems mandated the widespread interest in developing novel strategies for deriving accurate attenuation maps with the aim to improve the quantitative accuracy of these emerging hybrid imaging systems. The attenuation map in PET/MRI should ideally be derived from anatomical MR images; however, MRI intensities reflect proton density and relaxation time properties of biological tissues rather than their electron density and photon attenuation properties. Therefore, in contrast to PET/computed tomography, there is a lack of standardized global mapping between the intensities of MRI signal and linear attenuation coefficients at 511 keV. Moreover, in standard MRI sequences, bones and lung tissues do not produce measurable signals owing to their low proton density and short transverse relaxation times. MR images are also inevitably subject to artifacts that degrade their quality, thus compromising their applicability for the task of attenuation correction in PET/MRI. MRI-guided attenuation correction strategies can be classified in three broad categories: (i) segmentation-based approaches, (ii) atlas-registration and machine learning methods, and (iii) emission/transmission-based approaches. This paper summarizes past and current state-of-the-art developments and latest advances in PET/MRI attenuation correction. The advantages and drawbacks of each approach for addressing the challenges of MR-based attenuation correction are comprehensively described. The opportunities brought by both MRI and PET imaging modalities for deriving accurate attenuation maps and improving PET quantification will be elaborated. Future prospects and potential clinical applications of these techniques and their integration in commercial systems will also be discussed.
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Deep learning is applied in PET-MRI attenuation correction.
“Deep learning is applied in radiotherapy [89], in PET-MRI attenuation correction [90, 91], in radiomics [92, 93]”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [91]
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Deep learning-based synthetic-CT generation in radiotherapy and PET: A review
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Deep learning‐guided joint attenuation and scatter correction in multitracer neuroimaging studies
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Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques
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Applications of PET‐MRI in musculoskeletal disease
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Comparison of atlas-based techniques for whole-body bone segmentation
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Hybrid Imaging: Instrumentation and Data Processing
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One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI
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Advances in simultaneous PET/MR for imaging neuroreceptor function
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Whole‐body bone segmentation from MRI for PET/MRI attenuation correction using shape‐based averaging
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A review of PET attenuation correction methods for PET-MR
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Improving PET/MR brain quantitation with template-enhanced ZTE
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The Promise of Hybrid PET\/MRI: Technical advances and clinical applications
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Novel Quantitative PET Techniques for Clinical Decision Support in Oncology
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MR-guided joint reconstruction of activity and attenuation in brain PET-MR
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PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques
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Attenuation correction using deep learning for brain perfusion SPECT images
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Deep learning with multiresolution handcrafted features for brain MRI segmentation
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Metal artifact correction strategies in MRI-based attenuation correction in PET/MRI
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Towards quantitative small-animal imaging on hybrid PET/CT and PET/MRI systems
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A deep learning-based whole-body solution for PET/MRI attenuation correction
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CT synthesis from MRI images based on deep learning methods for MRI-only radiotherapy
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Practical issues and limitations of brain attenuation correction on a simultaneous PET-MR scanner
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Assessment of deep learning-based PET attenuation correction frameworks in the sinogram domain
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Recent Advances in Positron Emission Tomography/Magnetic Resonance Imaging Technology
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Total Body PET/CT: Clinical Value and Future Aspects of Quantification in Static and Dynamic Imaging
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Integration of PET/MR Hybrid Imaging into Radiation Therapy Treatment
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Advancements in hybrid imaging techniques: Enhancing diagnostic accuracy with PET/MRI and PET/CT
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Synthetic CT Generation via Variant Invertible Network for Brain PET Attenuation Correction
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Ghorbanzadeh, M., Hosseini, S. A., Vahdat, B. V., Mirzaiy, H., Akhavanallaf, A., & Arabi, H. (2024) · Radiation Physics and Chemistry · doi:10.1016/j.radphyschem.2024.112140 · Cited by 5 · see citing works
- Authors
- Mehranian, A., Arabi, H., & Zaidi, H.
- Affiliations
- University Hospital of Geneva (CH)
- University of Geneva (CH)
- Corresponding author
- Zaidi, Habib
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2016-02-08
- Volume
- 43
- Issue
- 3
- Pages
- 1130-1155
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- OpenAlex
- W2267700533
- Cited by
- 152
- References
- 163
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 14 · 2023: 5 · 2022: 19 · 2021: 17 · 2020: 28 · 2019: 17 · 2018: 18 · 2017: 13 · 2016: 10
- FWCI
- 3.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Medical Physics (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- Swiss National Foundation (SNSF 31003A-149957)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- PET/MRI, attenuation correction, PET quantification, linear attenuation coefficient, atlas registration, machine learning, MRI artifacts, hybrid imaging systems
- MeSH terms
- Machine Learning, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Artifacts, Positron-Emission Tomography, Whole Body Imaging
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