Pelvic PET/MR attenuation correction in the image space using deep learning

Frontiers in Oncology

Oncology

Ingerid Skjei Knudtsen¹  Live Eikenes¹  Bendik Skarre Abrahamsen¹  Tone Frost Bathen²  Mattijs Elschot² 

[1] Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway;Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway;Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway;

关键词: PET/MR;    attenuation correction;    deep learning;    prostate cancer;    artificial intelligence frontiers;    MRAC;    pseudo-CT;   

DOI  :  10.3389/fonc.2023.1220009

 received in 2023-05-09, accepted in 2023-07-31,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionThe five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction.MethodsA convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction. Dixon MR images and the four-class attenuation correction µ-map were used as input to the models. CT and PET/MR examinations for 22 patients ([18F]FDG) were used for training and validation, and 17 patients were used for testing (6 [18F]PSMA-1007 and 11 [68Ga]Ga-PSMA-11). A quantitative analysis of PSMA uptake using voxel- and lesion-based error metrics was used to assess performance.ResultsIn the voxel-based analysis, the proposed model reduced the median root mean squared percentage error from 12.1% and 8.6% for the four- and five-class Dixon-based AC methods, respectively, to 6.2%. The median absolute percentage error in the maximum standardized uptake value (SUVmax) in bone lesions improved from 20.0% and 7.0% for four- and five-class Dixon-based AC methods to 3.8%.ConclusionThe proposed method reduces the voxel-based error and SUVmax errors in bone lesions when compared to the four- and five-class Dixon-based AC models.

【 授权许可】

Unknown   
Copyright © 2023 Abrahamsen, Knudtsen, Eikenes, Bathen and Elschot