Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction


Autoři: Paul Blanc-Durand aff001;  Maya Khalife aff002;  Brian Sgard aff001;  Sandeep Kaushik aff003;  Marine Soret aff001;  Amal Tiss aff004;  Georges El Fakhri aff004;  Marie-Odile Habert aff001;  Florian Wiesinger aff006;  Aurélie Kas aff001
Působiště autorů: Nuclear Medicine Department, Groupe Hospitalier Pitié-Salpêtrière C. Foix, APHP, Paris, France aff001;  Centre de Neuroimagerie de Recherche (CENIR), Institut du Cerveau et de la Moëlle, Paris, France aff002;  GE Healthcare, Bangalore, India aff003;  Gordon Center for Medical Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America aff004;  Laboratoire d’Imagerie Biomédicale, Sorbonne Université, Paris, France aff005;  GE Healthcare, Munich, Germany aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223141

Souhrn

One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomplished by generating an MRI-based surrogate computed tomography (CT) from which AC-maps are derived. Nevertheless, all techniques currently implemented in clinical routine suffer from bias. We present here a convolutional neural network (CNN) that generated AC-maps from Zero Echo Time (ZTE) MR images. Seventy patients referred to our institution for 18FDG-PET/MR exam (SIGNA PET/MR, GE Healthcare) as part of the investigation of suspected dementia, were included. 23 patients were added to the training set of the manufacturer and 47 were used for validation. Brain computed tomography (CT) scan, two-point LAVA-flex MRI (for atlas-based AC) and ZTE-MRI were available in all patients. Three AC methods were evaluated and compared to CT-based AC (CTAC): one based on a single head-atlas, one based on ZTE-segmentation and one CNN with a 3D U-net architecture to generate AC maps from ZTE MR images. Impact on brain metabolism was evaluated combining voxel and regions-of-interest based analyses with CTAC set as reference. The U-net AC method yielded the lowest bias, the lowest inter-individual and inter-regional variability compared to PET images reconstructed with ZTE and Atlas methods. The impact on brain metabolism was negligible with average errors of -0.2% in most cortical regions. These results suggest that the U-net AC is more reliable for correcting photon attenuation in brain FDG-PET/MR than atlas-AC and ZTE-AC methods.

Klíčová slova:

Cerebellum – Computed axial tomography – Imaging techniques – Magnetic resonance imaging – Neural networks – Neuroimaging – Positron emission tomography – Mastoid process


Zdroje

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Článek vyšel v časopise

PLOS One


2019 Číslo 10

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