Background

Previously, we demonstrated the effectiveness of phase symmetry (PS) features for segmentation and localisation of bone fractures in 3D ultrasound for the purpose of orthopedic fracture reduction surgery. We recently proposed a novel real-time image-processing method of bone surface extraction from local phase features of clinical 3D B-mode ultrasound data. We are presenting a computational study and outline planned future developments for integration into a computer aided orthopedic surgery framework.

Previously, we demonstrated the effectiveness of phase symmetry (PS) features for segmentation and localisation of bone fractures in 3D ultrasound for the purpose of orthopaedic fracture reduction surgery. We recently proposed a novel real-time image-processing method of bone surface extraction from local phase features of clinical 3D B-mode ultrasound data. We are presenting a computational study and outline of planned future developments for integration into a computer aided orthopaedic surgery framework.

Our image-processing pipeline was implemented on three platforms: (1) using an existing PS extraction C++ algorithm on a dual processor machine with two Xeon x5472 CPUs @ 3GHz with 8GB of RAM, (2) using our proposed method implemented in MATLAB running on the same machine as in (1), and (3) CUDA implementation of our method implemented on a professional GPU (Nvidia Tesla c2050).

We ran these three implementations 20 times each on 128×128×128 scans of the iliac crest in live subjects and repeated the processing for 15 combinations of filter parameters. On average, the C++ implementation took 1.93s per volume, the MATLAB implementation 1.28s, and the GPU implementation 0.08s. Overall, our GPU implementation is between 15 and 25 times faster than the state-of-the-art method.

Implementing our algorithm on a professional grade GPU produced dramatic computational improvements, enabling full 3D datasets to be processed in an average time of under 100ms, which, if proven in a clinical system, would allow for near real time computation. We are currently implementing our algorithm on an open research sonography platform (Ultrasonix Medical Corporation). High-powered graphic cards can easily be integrated into the open architecture of this system, thus enabling GPU computation on diagnostic medical and research ultrasound devices.

We intend to use this platform within a surgical environment for accurate and automatic detection of fractures and as an integral part of our developing computer aided surgery pipeline, in which we use PS features to register intra-operative ultrasound to pre-operative computed tomography images.