ArticleFrontiers in oncology2023
Pelvic PET/MR attenuation correction in the image space using deep learning.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- Evaluating a radiotherapy deep learning synthetic CT algorithm for PET-MR attenuation correction in the pelvis.EJNMMI physics · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
Introduction: The five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction. Methods: A convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction. Dixon MR images and the four-class attenuation correction Results: In the voxel-based analysis, the proposed model reduced the median root mean squared percentage error from 12.1% and 8.6% for the four- and five-class Dixon-based AC methods, respectively, to 6.2%. The median absolute percentage error in the maximum standardized uptake value (SUV Conclusion: The proposed method reduces the voxel-based error and SUV
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