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

Claes Nøhr Ladefoged; Flemming Littrup Andersen; Thomas Lund Andersen; Lasse Anderberg; Christian Engkebølle; Karine Madsen; Liselotte Højgaard; Otto Mølby Henriksen; Ian Law

doi:10.3389/fnins.2023.1142383

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

Front Neurosci. 2023 Apr 6:17:1142383. doi: 10.3389/fnins.2023.1142383. eCollection 2023.

Authors

Claes Nøhr Ladefoged¹, Flemming Littrup Andersen¹, Thomas Lund Andersen¹, Lasse Anderberg¹, Christian Engkebølle¹, Karine Madsen¹, Liselotte Højgaard¹, Otto Mølby Henriksen¹, Ian Law¹

Affiliation

¹ Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.

Abstract

Purpose: Conventional 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.

Methods: The 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.

Results: Ninety-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.

Conclusion: We 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.

Keywords: AI; DeepDixon; PET/MRI; attenuation correction; deep learning; glioma; post-surgery.