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Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients

BACKGROUND: Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncolog...

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Autores principales: Olin, Anders B., Hansen, Adam E., Rasmussen, Jacob H., Jakoby, Björn, Berthelsen, Anne K., Ladefoged, Claes N., Kjær, Andreas, Fischer, Barbara M., Andersen, Flemming L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927520/
https://www.ncbi.nlm.nih.gov/pubmed/35294629
http://dx.doi.org/10.1186/s40658-022-00449-z
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author Olin, Anders B.
Hansen, Adam E.
Rasmussen, Jacob H.
Jakoby, Björn
Berthelsen, Anne K.
Ladefoged, Claes N.
Kjær, Andreas
Fischer, Barbara M.
Andersen, Flemming L.
author_facet Olin, Anders B.
Hansen, Adam E.
Rasmussen, Jacob H.
Jakoby, Björn
Berthelsen, Anne K.
Ladefoged, Claes N.
Kjær, Andreas
Fischer, Barbara M.
Andersen, Flemming L.
author_sort Olin, Anders B.
collection PubMed
description BACKGROUND: Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncology to derive MRI-based attenuation maps of head and neck cancer patients and evaluated its performance on PET AC. METHODS: Eleven head and neck cancer patients, referred for radiotherapy, underwent CT followed by PET/MRI with acquisition of Dixon MRI. Both scans were performed in radiotherapy position. PET AC was performed with three different patient-specific attenuation maps derived from: (1) Dixon MRI using a deep learning network (PET(Deep)). (2) Dixon MRI using the vendor-provided atlas-based method (PET(Atlas)). (3) CT, serving as reference (PET(CT)). We analyzed the effect of the MRI-based AC methods on PET quantification by assessing the average voxelwise error within the entire body, and the error as a function of distance to bone/air. The error in mean uptake within anatomical regions of interest and the tumor was also assessed. RESULTS: The average (± standard deviation) PET voxel error was 0.0 ± 11.4% for PET(Deep) and −1.3 ± 21.8% for PET(Atlas). The error in mean PET uptake in bone/air was much lower for PET(Deep) (−4%/12%) than for PET(Atlas) (−15%/84%) and PET(Deep) also demonstrated a more rapidly decreasing error with distance to bone/air affecting only the immediate surroundings (less than 1 cm). The regions with the largest error in mean uptake were those containing bone (mandible) and air (larynx) for both methods, and the error in tumor mean uptake was −0.6 ± 2.0% for PET(Deep) and −3.5 ± 4.6% for PET(Atlas). CONCLUSION: The deep learning network for deriving MRI-based attenuation maps of head and neck cancer patients demonstrated accurate AC and exceeded the performance of the vendor-provided atlas-based method both overall, on a lesion-level, and in vicinity of challenging regions such as bone and air.
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spelling pubmed-89275202022-04-01 Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients Olin, Anders B. Hansen, Adam E. Rasmussen, Jacob H. Jakoby, Björn Berthelsen, Anne K. Ladefoged, Claes N. Kjær, Andreas Fischer, Barbara M. Andersen, Flemming L. EJNMMI Phys Original Research BACKGROUND: Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncology to derive MRI-based attenuation maps of head and neck cancer patients and evaluated its performance on PET AC. METHODS: Eleven head and neck cancer patients, referred for radiotherapy, underwent CT followed by PET/MRI with acquisition of Dixon MRI. Both scans were performed in radiotherapy position. PET AC was performed with three different patient-specific attenuation maps derived from: (1) Dixon MRI using a deep learning network (PET(Deep)). (2) Dixon MRI using the vendor-provided atlas-based method (PET(Atlas)). (3) CT, serving as reference (PET(CT)). We analyzed the effect of the MRI-based AC methods on PET quantification by assessing the average voxelwise error within the entire body, and the error as a function of distance to bone/air. The error in mean uptake within anatomical regions of interest and the tumor was also assessed. RESULTS: The average (± standard deviation) PET voxel error was 0.0 ± 11.4% for PET(Deep) and −1.3 ± 21.8% for PET(Atlas). The error in mean PET uptake in bone/air was much lower for PET(Deep) (−4%/12%) than for PET(Atlas) (−15%/84%) and PET(Deep) also demonstrated a more rapidly decreasing error with distance to bone/air affecting only the immediate surroundings (less than 1 cm). The regions with the largest error in mean uptake were those containing bone (mandible) and air (larynx) for both methods, and the error in tumor mean uptake was −0.6 ± 2.0% for PET(Deep) and −3.5 ± 4.6% for PET(Atlas). CONCLUSION: The deep learning network for deriving MRI-based attenuation maps of head and neck cancer patients demonstrated accurate AC and exceeded the performance of the vendor-provided atlas-based method both overall, on a lesion-level, and in vicinity of challenging regions such as bone and air. Springer International Publishing 2022-03-16 /pmc/articles/PMC8927520/ /pubmed/35294629 http://dx.doi.org/10.1186/s40658-022-00449-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Research
Olin, Anders B.
Hansen, Adam E.
Rasmussen, Jacob H.
Jakoby, Björn
Berthelsen, Anne K.
Ladefoged, Claes N.
Kjær, Andreas
Fischer, Barbara M.
Andersen, Flemming L.
Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients
title Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients
title_full Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients
title_fullStr Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients
title_full_unstemmed Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients
title_short Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients
title_sort deep learning for dixon mri-based attenuation correction in pet/mri of head and neck cancer patients
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927520/
https://www.ncbi.nlm.nih.gov/pubmed/35294629
http://dx.doi.org/10.1186/s40658-022-00449-z
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