Freehand 3D ultrasound imaging toward midfacial bone surface reconstruction for intraoperative image registration.

Abstract

<h4>Background</h4>Image-guided surgery is a critical technique in maxillofacial surgery. The foundation of image-guided surgery is image registration. Traditional image registration methods have limitations in terms of invasiveness, complexity, and unsatisfied accuracy. Freehand 3D ultrasound (US) imaging using a tracked 2D US probe may offer a non-invasive, real-time, and accurate alternative. Purpose This study aims to develop a novel freehand 3D US imaging framework for midfacial bone surface reconstruction and registration with preoperative 3D data (e.g., computed tomography), enabling accurate intraoperative surgical navigation in maxillofacial surgery.<h4>Methods</h4>First, a customized stereo camera is used to track the pose of a 2D US probe during the freehand US scanning toward the midfacial bone surface. Then, a short-term dense concatenate network (STDC) is employed to segment the bone surface from the US image. The segmented pixels with spatial information form a coarse 3D volume in real time. The 3D volume's voxels are then converted to a coarse point cloud. A template matching denoising technique is utilized to remove noisy and outlier points, followed by a self-supervised Freehand 3D Ultrasound Neural Surface Reconstruction network (FUNSR) to reconstruct the point cloud to a smooth surface mesh. Finally, the resulting fine bone surface is registered with preoperative 3D data for quantitative evaluation. A total of 1000 zygomatic ultrasound images (split into 700 training, 150 validation, and 150 test images) were used to train the segmentation network. The reconstruction network was trained with self-supervision. The reconstruction accuracy of the network was validated using surface registration error (SRE), and the registration accuracy was verified using target registration error (TRE). Method performance improvement was evaluated using t-tests and analysis of variance, with Tamhane's T2 test applied for multiple comparison correction to control the false discovery rate. Cohen's effect sizes were calculated to quantify performance differences.<h4>Results</h4>In the phantom experiment, the average SRE was 0.387 ±$\pm$ 0.034 mm, and the average TRE was 0.802 ±$\pm$ 0.177 mm. Compared with registration using only voxel reconstruction results (SRE = 1.301 ±$\pm$ 0.133 mm, TRE = 1.155 ±$\pm$ 0.359 mm), the accuracy was improved (Cohen's d = 9.416 for SRE, Cohen's d = 1.247 for TRE, and p<$p<$ 0.01 for both). Also, the accuracy remained uniform across various regions of the midface ( p=$p =$ 0.918). When using only local region reconstruction for registration, the decrease in overall accuracy is relatively minor ( p=$p =$ 0.025). In the volunteer trials, the average SRE was 0.445 ±$\pm$ 0.099 mm. Compared with the fundamental framework of our method (SRE = 0.955 ±$\pm$ 0.204 mm), the proposed template matching denoising and surface reconstruction components further enhance the registration accuracy ( p<$p<$ 0.001, Cohen's d >$>$ 2.0).<h4>Conclusions</h4>The proposed freehand 3D US imaging framework could offer a noninvasive, accurate, and quasi-real-time solution for midfacial bone surface reconstruction and image registration in maxillofacial surgery.