Work

Torso synthetic CT generation by integrating deep learning and segmentation for FDG-PET/MR attenuation correction

Year: 2025

Type: article

Abstract: Introduction. Positron Emission Tomography/Magnetic Resonance ( PET/MR ) offers benefits over PET/CT including simultaneous PET and MR acquisition, intrinsic spatial registration accuracy, MR-based functional information, and superior soft tissue contrast. However, accurate attenuation correction ( AC ) for PET remains challenging as MR signals do not directly correspond to attenuation. Using deep learning algorithms that learn complex relationships, we generate synthetic CT ( sCT ) from MR for AC. Our novel method for AC, merges deep learning with threshold-based segmentation, to produce an AC map for the entire torso from Dixon MR images, which heretofore has not been demonstrated. Method . Twenty-nine prospectively collected, paired FDG-PET/CT and MR datasets were used for training and validation using the U-net Residual Network conditional Generative Adversarial Network integrated with tissue segmentation ( URcGAN mod ) from Dixon MR data. Our application focused on torso (base of the skull to mid-thigh) AC, a common but challenging field of view (FOV). Performance was compared to that of 4 previously published methods. Results . Using 15 paired datasets for training and 14 independent datasets for testing, the URcGAN mod generates an accurate torso sCT with a mean absolute difference of 32 ± 4 HU per voxel. When applied for AC for FDG images, and considering evaluable (SUV ≥ 0.1 g/ml) voxels across all regions of interest, absolute values of the differences were within 4.4% from those determined using the measured CT for AC. Reproducibility was excellent with less than 3.5% standard deviation. The results demonstrate the accuracy and precision of URcGAN mod method for torso sCT generation for quantitatively accurate MR-based AC ( MRAC ), exceeding the comparison methods. Conclusion . Combining deep learning and segmentation enhances MRAC accuracy in torso FDG-PET/MR, improves SUV accuracy throughout the torso, achieves less than 4.4% SUV error, and outperforms comparison methods. Given the excellent sCT and SUV accuracy and precision, our proposed method warrants further studies for quantitative longitudinal multicenter trials.

Source: Biomedical Physics & Engineering Express

DOI: 10.1088/2057-1976/ae13ff