Development of Automatic Assessment Framework for Spine Deformity Using Freehand 3-D Ultrasound Imaging System.

A 3-D ultrasound (US) imaging technique has been studied to facilitate the diagnosis of spinal deformity without radiation. The objective of this article is to propose an assessment framework to automatically estimate spinal deformity in US spine images. The proposed framework comprises four major components, a US spine image generator, a novel transformer-based lightweight spine detector network, an angle evaluator, and a 3-D modeler. The principal component analysis (PCA) and discriminative scale space tracking (DSST) method are first adopted to generate the US spine images. The proposed detector is equipped with a redundancy queries removal (RQR) module and a regularization item to realize accurate and unique detection of spine images. Two clinical datasets, a total of 273 images from adolescents with idiopathic scoliosis, are used for the investigation of the proposed framework. The curvature is estimated by the angle evaluator, and the 3-D mesh model is established by the parametric modeling technique. The accuracy rate (AR) of the proposed detector can be achieved at 99.5%, with a minimal redundancy rate (RR) of 1.5%. The correlations between automatic curve measurements on US spine images from two datasets and manual measurements on radiographs are 0.91 and 0.88, respectively. The mean absolute difference (MAD) and standard deviation (SD) are 2.72° ± 2.14° and 2.91° ± 2.36° , respectively. The results demonstrate the effectiveness of the proposed framework to advance the application of the 3-D US imaging technique in clinical practice for scoliosis mass screening and monitoring.

Improvement of 3-D Ultrasound Spine Imaging Technique Using Fast Reconstruction Algorithm.

Three-dimensional (3-D) freehand ultrasound (US) imaging has been applied to the investigation of spine deformity. However, it is a challenge for the current 3-D imaging reconstruction algorithms to achieve a balance between image quality and computation time. The objectives of this article are to implement a new fast reconstruction algorithm that can fulfill the request of immediate demonstration and processing for high-quality 3-D spine imaging, and to evaluate the reliability and accuracy of scoliotic curvature measurement when using the algorithm. The fast dot-projection (FDP) algorithm was applied for voxel-based nearest neighbor (VNN), multiple plane interpolation (MPI), and pixel nearest neighbor (PNN) protocols to reduce the reconstruction time. The 3-D image volume was reconstructed from the datasets acquired from scoliotic subjects. The computational cost, image characteristics, and statistical analyses of curve measurements were compared and evaluated among different reconstruction protocols. The results illustrated that the 3-D spine images using the FDP-MPI4 algorithm showed higher brightness (20%), contrast (14%), and signal-to-noise ratio (SNR) (26%) than FDP-VNN. The measurement performed by trainee rater exhibited significant improvement in measurement reliability and accuracy using FDP-MPI4 in comparison with FDP-VNN ( ), and the intraclass correlation coefficient (ICC) of interrater measurement increased from 0.88 to 0.96. The FDP-PNN method could acquire and reconstruct spine images simultaneously and present the results in 1-2 min, which showed the potential to provide the approximate real-time visualization for fast screening.