Assessing the Transferability of Micro-CT–Based Plaque Segmentation to Clinical CT Using Super-Resolution: Limits and Emerging Perspectives

Non-invasive plaque characterization remains limited in peripheral arterial obstructive disease (PAOD), while ex vivo micro-computed tomography (micro-CT) enables quasi-histological assessment. This study evaluates whether plaque-related structural information—defined as compositional and morphological plaque features learned from micro-CT segmentation—can be transferred to clinical CT using a super-resolution (SR) framework. Popliteal artery segments from six patients with PAOD were analysed using micro-CT and histology. Annotated micro-CT images were used to train convolutional neural networks for plaque segmentation. Low-resolution clinical CT images were upsampled using a Laplacian pyramid super-resolution approach and segmentation models were applied without retraining. Performance was assessed using Dice scores on held-out micro-CT test data, and segmentation outputs on SR-CT images were qualitatively evaluated. Segmentation of calcified plaque components on micro-CT test images yielded Dice scores ranging from 0.58 to 0.67, indicating low-to-intermediate agreement. When applied to SR-CT images, segmentation revealed non-random identification of calcified structures in selected image sequences, with marked heterogeneity across slices. SR enables exploratory assessment of plaque information transfer but does not overcome the fundamental resolution gap. These findings define the current limits of CT-based plaque characterization and provide a framework for evaluating future imaging technologies.