Zero-TE MRI-based attenuation correction for bone components on chest [

objectiveIn positron emission tomography (PET)/magnetic resonance imaging (MRI), attenuation correction (AC) for PET of the head is achieved by MRI data to generate pseudo-computed tomography (CT) images. However, for the torso, AC becomes more challenging due to the complexity of separating bone components. Additionally, generating accurate MRI-based CT using deep learning poses significant difficulties for the chest, primarily because perfectly paired MRI and CT training data are hard to obtain owing to respiratory motion and body movements. We previously demonstrated that MRI-to-CT conversion can be achieved without deformation, even using unsupervised learning for zero echo time (ZTE) MRI and CT data from different individuals. Building on this foundation, our study aims to apply this approach to AC in chest PET/MRI and assess their quantitative accuracy, reproducibility, and external validity.

methodsThe datasets used included (1) training dataset (unpaired ZTE MRI and CT of PET/CT, n = 360 and 500, respectively); (2) test dataset (paired PET/MRI and PET/CT, n = 25 and 25, respectively); (3) repeatability assessment dataset (repeated PET/MRI, n = 15 × 2 scans for the same patient); and (4) external validation dataset (paired MRI component of PET/MRI and CT, n = 30 and 30, respectively, acquired at another institution). Unpaired training data were used to train the deep learning model of pseudo-CT generation from ZTE. The accuracy, repeatability, and reproducibility of the PET/MRI scans using ZTE- and deep learning-based AC (MRAC

resultsThe histogram correlation coefficients between MRAC

conclusionsUnsupervised learning with unpaired ZTE and CT data enabled pseudo-CT generation with bone components that closely matched CT-based attenuation maps. Integration into MR-based attenuation correction resulted in stable physiological uptake measurements in chest PET/MRI, supporting the feasibility of this approach.