RT Journal Article
SR Electronic
T1 Evaluation of Deep Learning-based Approaches to Segment Bowel Air Pockets and Generate Pelvis Attenuation Maps from CAIPIRINHA-accelerated Dixon MR Images
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP jnumed.120.261032
DO 10.2967/jnumed.120.261032
A1 Hasan Sari
A1 Ja Reaungamornrat
A1 Onofrio Catalano
A1 Javier Vera-Olmos
A1 David Izquierdo-Garcia
A1 Manuel Antonio Morales
A1 Angel Torrado-Carvajal
A1 Sheung Chee Thomas Ng
A1 Norberto Malpica
A1 Ali Kamen
A1 Ciprian Catana
YR 2021
UL http://jnm.snmjournals.org/content/early/2021/07/22/jnumed.120.261032.abstract
AB Attenuation correction (AC) remains a challenge in pelvis PET/MR imaging. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvis attenuation maps (μ-maps). However, these methods often misclassify air pockets in the digestive tract, which can introduce bias in the reconstructed PET images. The aims of this work were to develop deep learning-based methods to automatically segment air pockets and generate pseudo-CT images from CAIPIRINHA-accelerated MR Dixon images. Methods: A convolutional neural network (CNN) was trained to segment air pockets using 3D CAIPIRINHA-accelerated MR Dixon datasets from 35 subjects and was evaluated against semi-automated segmentations. A separate CNN was trained to synthesize pseudo-CT μ-maps from the Dixon images. Its accuracy was evaluated by comparing the deep learning-, model- and CT-based μ-maps using data from 30 of the subjects. Finally, the impact of different μ-maps and air pocket segmentation methods on the PET quantification was investigated. Results: Air pockets segmented using the CNN agreed well with semi-automated segmentations, with a mean Dice similarity coefficient of 0.75. Volumetric similarity score between two segmentations was 0.85 ± 0.14. The mean absolute relative change (RCs) with respect to the CT-based μ-maps were 2.6% and 5.1% in the whole pelvis for the deep learning and model-based μ-maps, respectively. The average RC between PET images reconstructed with deep learning and CT-based μ-maps was 2.6%. Conclusion: We presented a deep learning-based method to automatically segment air pockets from CAIPIRINHA-accelerated Dixon images with comparable accuracy to semi-automatic segmentations. We also showed that the μ-maps synthesized using a deep learning-based method from CAIPIRINHA-accelerated Dixon images are more accurate than those generated with the model-based approach available on integrated PET/MRI scanner.