ArticleMagnetic resonance in medicine2022
MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Deriving tissue physical densities based on Dixon magnetic resonance images and tissue composition prior knowledge for voxel-based internal dosimetry.EJNMMI physics · 2025Article
- A dual-stage framework for segmentation of the brain anatomical regions with high accuracy.Magma (New York, N.Y.) · 2025Article
- Targeted radioligand therapy: physics and biology, internal dosimetry and other practical aspects duringTheranostics · 2025Review
- Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET.IEEE transactions on medical imaging · 2024Article
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Attenuation correction for PET/MRI to measure tracer activity surrounding total knee arthroplasty.European journal of hybrid imaging · 2022Article
- A deep learning-based whole-body solution for PET/MRI attenuation correction.EJNMMI physics · 2022Article
- MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.Magnetic resonance in medicine · 2022Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe compare the performance of three commonly used MRI-guided attenuation correction approaches in torso PET/MRI, namely segmentation-, atlas-, and deep learning-based algorithms.
methodsTwenty-five co-registered torso
resultsThe deep learning approach outperformed both atlas- and segmentation-based methods resulting in less than 4% SUV bias across 25 patients compared to the segmentation-based method with up to 20% SUV bias in bony structures and the atlas-based method with 9% bias in the lung. The deep learning-based method exhibited superior performance. Yet, in case of sever truncation and metallic-artifacts in the input MRI, this approach was outperformed by the atlas-based method, exhibiting suboptimal performance in the affected regions. Conversely, for abnormal anatomies, such as a patient presenting with one lung or small malignant lesion in the lung, the deep learning algorithm exhibited promising performance compared to other methods.
conclusionThe deep learning-based method provides promising outcome for synthetic CT generation from MRI. However, metal-artifact and body truncation should be specifically addressed.
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