Key result
MSR-Net improves carotid PET inflammation segmentation over standard architectures, reaching a ~0.79 Dice coefficient.
Why the study?
An automated method for segmentation and uptake quantification of carotid atherosclerotic inflammation on 64Cu-CANF-comb PET was needed to account for partial-volume and tissue-fraction effects.
Does a two-stage multiscale residual network (MSR-Net) improve segmentation and uptake quantification of carotid atherosclerotic inflammation on 64Cu-CANF-comb PET compared with standard deep learning models and iterative Yang partial-volume correction?
Does a two-stage multiscale residual network (MSR-Net) improve segmentation and uptake quantification of carotid atherosclerotic inflammation on 64Cu-CANF-comb PET compared with standard deep learning models and iterative Yang partial-volume correction?
The two-stage MSR-Net framework significantly improves carotid PET atherosclerotic inflammation segmentation and uptake quantification over existing deep learning and partial-volume correction methods in simulated settings.
Answers are grounded in this paper and cite their sources.
May improve PET-based carotid inflammation assessment; leaves open prospective clinical validation before adoption.
Objective. To develop an automated method for segmentation and uptake quantification of carotid atherosclerotic inflammation on 64Cu-CANF-comb PET while accounting for partial-volume and tissue-fraction effects. Approach. Synthetic carotid PET images with known fractional-volume ground truth were generated by combining MR-derived high-resolution activity maps with GATE Monte Carlo simulation incorporating major PET physical processes and matched to the Biograph mMR acquisition and reconstruction. A two-stage network first localized the carotid vessel wall from T1-weighted MR and then estimated voxel-wise inflammation fraction from masked PET. Both stages used a multiscale residual (MSR) convolutional backbone. Performance was compared with U-Net, Transformer U-Net, DUCK-Net, and iterative Yang partial-volume correction. Main results. In a two-alternative forced-choice study, five nuclear-medicine physicians identified real images with 54% accuracy (54/100; Wilson 95% CI 0.44-0.63; exact binomial p = 0.48). On 184 simulated test slices from five held-out patients, the proposed method achieved a weighted DSC of 0.79, compared with 0.73 for U-Net, 0.69 for Transformer U-Net, and 0.75 for DUCK-Net, with corresponding HD values of 1.85, 2.29, 2.32, and 2.23 voxels. Patient-cluster bootstrap comparisons favored the proposed method for DSC and HD against all three baselines (Holm-corrected p ≤ 0.031). Overall ENRMSE was 0.24 versus 0.59, 0.68, and 0.59 for the three DL baselines and 0.57 for iterative Yang. In seven patients, corrected SUV showed a positive association with MR-derived lipid-rich/necrotic core with hemorrhage. Significance. The proposed framework improved carotid PET segmentation and uptake quantification under simulated conditions. The patient analysis is preliminary and requires external validation with biological reference standards before clinical deployment.
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Li et al. (2026) studied Carotid artery atherosclerotic inflammation (n=7). Two-stage multiscale residual network (MSR-Net) vs. U-Net, Transformer U-Net, DUCK-Net, and iterative Yang partial-volume correction was evaluated on Weighted Dice similarity coefficient (DSC) for carotid atherosclerotic inflammation segmentation (p=≤0.031). MSR-Net improved carotid PET inflammation segmentation (weighted DSC 0.79 vs 0.69-0.75 for U-Net, Transformer U-Net, and DUCK-Net; Holm-corrected p≤0.031) and lowered ENRMSE to 0.24.
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