Mamba-MS-Unet: 3D State-Space Modeling for High-Fidelity PET Synthesis from CT In Lung Cancer
Abstract
Purpose
To develop a clinically adaptable 3D deep learning framework for synthesizing quantitative PET images from routine CT volumes, enabling reliable metabolic assessment for lung cancer while reducing reliance on additional PET scans.
Methods
We developed a multi-scale 3D UNet integrating selective state-space modeling (Mamba-MS-UNet) to synthesize PET from CT volumes. The Mamba module, embedded in the encoder, efficiently captures long-range 3D spatial dependencies with low computational overhead. A hierarchical SUV-aware loss function was employed to preserve voxel-level quantitative accuracy in tumor regions. The model was trained and internally validated on PET/CT data from Center 1 using standardized preprocessing. External validation was performed on independent datasets from Center 2 and Center 3. At Center 2, volumetric structural fidelity and voxel-wise agreement between synthetic and real PET were evaluated using structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). At Center 3, radiomics-based classification of lung adenocarcinoma versus squamous cell carcinoma was performed using (i) real PET + CT + pathology and (ii) synthetic PET + CT + pathology to assess preservation of discriminative metabolic information.
Results
On Center 2, synthetic PET demonstrated high agreement with real PET, achieving an SSIM of 0.95 ± 0.02 and a PSNR of 32.70 ± 1.87 dB, reflecting high volumetric structural fidelity and strong voxel-level similarity. On Center 3, pathological subtype classification using synthetic PET achieved an AUC of 0.721, which was identical to that obtained using real PET (AUC = 0.721), indicating preservation of clinically relevant metabolic features.
Conclusion
Mamba-MS-UNet enables high-fidelity 3D PET synthesis from CT with improved structural consistency and quantitative SUV reliability. This framework offers a practical low-cost, low-radiation alternative for metabolic imaging in lung cancer and demonstrates strong potential for supporting downstream quantitative analyses and radiotherapy planning.