DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants

PurposeConventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET), which provides additional information on metabolic properties. In neuro-onc...

Full description

Bibliographic Details
Main Authors: Claes Nøhr Ladefoged, Flemming Littrup Andersen, Thomas Lund Andersen, Lasse Anderberg, Christian Engkebølle, Karine Madsen, Liselotte Højgaard, Otto Mølby Henriksen, Ian Law
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-04-01
Series:Frontiers in Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2023.1142383/full
_version_ 1797851355356856320
author Claes Nøhr Ladefoged
Flemming Littrup Andersen
Thomas Lund Andersen
Lasse Anderberg
Christian Engkebølle
Karine Madsen
Liselotte Højgaard
Otto Mølby Henriksen
Ian Law
author_facet Claes Nøhr Ladefoged
Flemming Littrup Andersen
Thomas Lund Andersen
Lasse Anderberg
Christian Engkebølle
Karine Madsen
Liselotte Højgaard
Otto Mølby Henriksen
Ian Law
author_sort Claes Nøhr Ladefoged
collection DOAJ
description PurposeConventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [18F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants.MethodsThe MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [18F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC.ResultsNinety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method.ConclusionWe evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.
first_indexed 2024-04-09T19:16:33Z
format Article
id doaj.art-27b259901cdb42e4aa3afff359cb66bf
institution Directory Open Access Journal
issn 1662-453X
language English
last_indexed 2024-04-09T19:16:33Z
publishDate 2023-04-01
publisher Frontiers Media S.A.
record_format Article
series Frontiers in Neuroscience
spelling doaj.art-27b259901cdb42e4aa3afff359cb66bf2023-04-06T05:05:43ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-04-011710.3389/fnins.2023.11423831142383DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implantsClaes Nøhr LadefogedFlemming Littrup AndersenThomas Lund AndersenLasse AnderbergChristian EngkebølleKarine MadsenLiselotte HøjgaardOtto Mølby HenriksenIan LawPurposeConventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [18F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants.MethodsThe MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [18F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC.ResultsNinety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method.ConclusionWe evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.https://www.frontiersin.org/articles/10.3389/fnins.2023.1142383/fullAIattenuation correctiondeep learningDeepDixongliomapost-surgery
spellingShingle Claes Nøhr Ladefoged
Flemming Littrup Andersen
Thomas Lund Andersen
Lasse Anderberg
Christian Engkebølle
Karine Madsen
Liselotte Højgaard
Otto Mølby Henriksen
Ian Law
DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants
Frontiers in Neuroscience
AI
attenuation correction
deep learning
DeepDixon
glioma
post-surgery
title DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants
title_full DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants
title_fullStr DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants
title_full_unstemmed DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants
title_short DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants
title_sort deepdixon synthetic ct for 18f fet pet mri attenuation correction of post surgery glioma patients with metal implants
topic AI
attenuation correction
deep learning
DeepDixon
glioma
post-surgery
url https://www.frontiersin.org/articles/10.3389/fnins.2023.1142383/full
work_keys_str_mv AT claesnøhrladefoged deepdixonsyntheticctfor18ffetpetmriattenuationcorrectionofpostsurgerygliomapatientswithmetalimplants
AT flemminglittrupandersen deepdixonsyntheticctfor18ffetpetmriattenuationcorrectionofpostsurgerygliomapatientswithmetalimplants
AT thomaslundandersen deepdixonsyntheticctfor18ffetpetmriattenuationcorrectionofpostsurgerygliomapatientswithmetalimplants
AT lasseanderberg deepdixonsyntheticctfor18ffetpetmriattenuationcorrectionofpostsurgerygliomapatientswithmetalimplants
AT christianengkebølle deepdixonsyntheticctfor18ffetpetmriattenuationcorrectionofpostsurgerygliomapatientswithmetalimplants
AT karinemadsen deepdixonsyntheticctfor18ffetpetmriattenuationcorrectionofpostsurgerygliomapatientswithmetalimplants
AT liselottehøjgaard deepdixonsyntheticctfor18ffetpetmriattenuationcorrectionofpostsurgerygliomapatientswithmetalimplants
AT ottomølbyhenriksen deepdixonsyntheticctfor18ffetpetmriattenuationcorrectionofpostsurgerygliomapatientswithmetalimplants
AT ianlaw deepdixonsyntheticctfor18ffetpetmriattenuationcorrectionofpostsurgerygliomapatientswithmetalimplants