In:
Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-8-24)
Abstract:
The 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. Methods A 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 ([ 18 F]FDG) were used for training and validation, and 17 patients were used for testing (6 [ 18 F]PSMA-1007 and 11 [ 68 Ga]Ga-PSMA-11). A quantitative analysis of PSMA uptake using voxel- and lesion-based error metrics was used to assess performance. Results In 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 (SUV max ) in bone lesions improved from 20.0% and 7.0% for four- and five-class Dixon-based AC methods to 3.8%. Conclusion The proposed method reduces the voxel-based error and SUV max errors in bone lesions when compared to the four- and five-class Dixon-based AC models.
Type of Medium:
Online Resource
ISSN:
2234-943X
DOI:
10.3389/fonc.2023.1220009
DOI:
10.3389/fonc.2023.1220009.s001
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2023
detail.hit.zdb_id:
2649216-7
Permalink
