Adaptive tetrahedral interpolation for reconstruction of uneven freehand 3D ultrasound

Abstract

Objective. Freehand 3D ultrasound volume reconstruction has received considerable attention in medical research because it can freely perform spatial imaging at a low cost. However, the uneven distribution of the original ultrasound images in space reduces the reconstruction effect of the traditional method. Approach. An adaptive tetrahedral interpolation algorithm is proposed to reconstruct 3D ultrasound volume data. The algorithm adaptively divides the unevenly distributed images into numerous tetrahedrons and interpolates the voxel value in each tetrahedron to reconstruct 3D ultrasound volume data. Main results. Extensive experiments on simulated and clinical data confirm that the proposed method can achieve more accurate reconstruction than six benchmark methods. Specifically, the averaged interpolation error at the gray level can be reduced by 0.22–0.82, and the peak signal-to-noise ratio and the mean structure similarity can be improved by 0.32–1.83 dB and 0.01–0.05, respectively. Significance. With the parallel implementation of the algorithm, one 3D ultrasound volume data with size 279×279×276 can be reconstructed from 100 slices 2D ultrasound images with size 200×200 at 1.04 seconds. Such a quick and accurate approach has practical value in medical research.

Three-dimensional ultrasound image reconstruction based on 3D-ResNet in the musculoskeletal system using a 1D probe: ex vivo and in vivo feasibility studies

“…Moreover, the classic KR was based on a fixed-size kernel to traverse each vacant voxel and estimate it according to the known voxel values within the kernel range using a complex kernel function (Chen et al 2014). However, the size of the kernel was an important global parameter and was difficult to set to achieve the best overall reconstruction effect (Wang et al 2023).…”