Automated carotid artery atherosclerotic inflammation segmentation in PET using MSR-Net.

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Abstract

Abstract
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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