OrthoFusion: A Super-Resolution Algorithm to Fuse Orthogonal CT Volumes

TL;DR

OrthoFusion is a new algorithm that improves the resolution of CT scans, helping to create clearer images and reduce the need for additional scans.

Contribution

OrthoFusion introduces a novel super-resolution algorithm that enhances CT volumes with improved accuracy and reduced segmentation time.

Findings

OrthoFusion improves structural similarity and accuracy of bone model geometries.

It reduces segmentation time and enhances radiographic image similarity in biplane videoradiography.

The algorithm is generalizable and reduces the need for additional scans.

Abstract

OrthoFusion, an intuitive super-resolution algorithm, is presented in this study to enhance the spatial resolution of clinical CT volumes. The efficacy of OrthoFusion is evaluated, relative to high-resolution CT volumes (ground truth), by assessing image volume and derived bone morphological similarity, as well as its performance in specific applications in 2D-3D registration tasks. Results demonstrate that OrthoFusion significantly reduced segmentation time, while improving structural similarity of bone images and relative accuracy of derived bone model geometries. Moreover, it proved beneficial in the context of biplane videoradiography, enhancing the similarity of digitally reconstructed radiographs to radiographic images and improving the accuracy of relative bony kinematics. OrthoFusion’s simplicity, ease of implementation, and generalizability make it a valuable tool for researchers and clinicians seeking high spatial resolution from existing clinical CT data. This study opens new avenues for retrospectively utilizing clinical images for research and advanced clinical purposes, while reducing the need for additional scans, mitigating associated costs and radiation exposure.

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