Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data

Clinical computed tomography (CT) datasets are increasingly common in skeletal research, yet archived retrospective datasets with thick-slice, anisotropic reconstructions are more commonly available to researchers than the original high-resolution scans. Models reconstructed from these suboptimal scans can produce distorted measurements and irregular surfaces. This study evaluates a super-resolution reconstruction (SRR) framework for generating high-resolution skeletal models from multiple orthogonal, thick-slice CT stacks. Archived CT scans of long bones from 33 individuals (0–16 years) were collected from National Taiwan University Hospital. For each individual, 3D models were generated from both the original thick-slice stacks and SRR-processed volumes. Linear measurements were compared with those taken directly from high-resolution picture archiving and communication system (PACS) renderings, and geometric similarity was quantified by signed Hausdorff distance and Dice similarity coefficient. SRR-reconstructed models showed lower measurement error and greater agreement with the PACS rendering than the thick-slice models. Surface geometry was generally consistent across model types, with localized deviations concentrated at metaphyseal and epiphyseal regions. SRR processing also produced smoother, more anatomically plausible surfaces with a clearer separation of fusing elements. These results demonstrate that SRR can improve virtual skeletal model quality from suboptimal clinical imaging, particularly for applications where anatomically precise surfaces are beneficial.