Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes

TL;DR

This paper presents a CNN-based method for automatic adjustment of orthogonal planes in intraoperative flat panel CT volumes, reducing manual effort and improving alignment accuracy during surgical assessment.

Contribution

The study introduces a PoseNet CNN that regresses anatomical plane parameters without segmentation, optimizing rotation descriptions and constraints, achieving rapid and accurate plane alignment.

Findings

Median normal estimation accuracy of 5°

In-plane rotation accuracy of 6°

Position accuracy within 6 mm

Abstract

Flat panel computed tomography is used intraoperatively to assess the result of surgery. Due to workflow issues, the acquisition typically cannot be carried out in such a way that the axis aligned multiplanar reconstructions (MPR) of the volume match the anatomically aligned MPRs. This needs to be performed manually, adding additional effort during viewing the datasets. A PoseNet convolutional neural network (CNN) is trained such that parameters of anatomically aligned MPR planes are regressed. Different mathematical approaches to describe plane rotation are compared, as well as a cost function is optimized to incorporate orientation constraints. The CNN is evaluated on two anatomical regions. For one of these regions, one plane is not orthogonal to the other two planes. The plane's normal can be estimated with a median accuracy of 5{\deg}, the in-plane rotation with an accuracy of 6{\deg}, and the position with an accuracy of 6 mm. Compared to state-of-the-art algorithms the labeling effort for this method is much lower as no segmentation is required. The computation time during inference is less than 0.05 s.