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Attenuation correction for human PET/MRI studies
1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States of America.
Abstract:
Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.
Insights
Accurate attenuation correction is crucial for combined positron emission tomography and magnetic resonance imaging (PET/MRI). This study reviews current and advanced methods, including deep learning, to improve PET/MRI quantitative accuracy.
Area of Science:
- Medical Imaging
- Radiochemistry
- Biophysics
Background:
- Attenuation correction is a key challenge in integrated PET/MRI systems.
- Standard CT-based methods are unavailable, necessitating MR-based approaches.
- Accurate correction requires addressing bone, lung, hardware, motion, and artifacts.
Purpose of the Study:
- To review standard and advanced MR-based attenuation correction techniques for PET/MRI.
- To evaluate their impact on PET data interpretation and quantification.
- To discuss future directions for improving attenuation correction.
Main Methods:
- Review of manufacturer-implemented MR-based attenuation correction (MRAC) techniques.
- Description of advanced MRAC methods, including deep learning approaches.
- Analysis of challenges: bone, lung, hardware, motion, artifacts, and data processing.
Main Results:
- Standard MRAC methods have limitations impacting PET quantification.
- Advanced methods, particularly deep learning, show promise in reducing bias.
- Ongoing research focuses on refining techniques for greater accuracy.
Conclusions:
- MR-based attenuation correction is essential for quantitative PET/MRI.
- Deep learning offers significant potential for improving accuracy.
- Further development is needed to overcome remaining challenges in PET/MRI attenuation correction.
