Elastography using multi-stream GPU: an application to online tracked ultrasound elastography, in-vivo and the da Vinci Surgical System

Robot-Guided Ultrasonography in Surgical Interventions

INTRODUCTION

The introduction of robotic-guided procedures in surgical techniques has brought an increase in the accuracy and control of resections. Surgery has evolved as a technique since the development of laparoscopy, which has added to the visualisation of the peritoneal cavity from a different perspective. Multi-armed robot associated with real-time intraoperative imaging devices brings important manoeuvrability and dexterity improvements in certain surgical fields.

MATERIALS AND METHODS

The present study is designed to synthesise the development of imaging techniques with a focus on ultrasonography in robotic surgery in the last ten years regarding abdominal surgical interventions.

RESULTS

All studies involved abdominal surgery. Out of the seven studies, two were performed in clinical trials. The other five studies were performed on organs or simulators and attempted to develop a hybrid surgical technique using ultrasonography and robotic surgery. Most studies aim to surgically identify both blood vessels and nerve structures through this combined technique (surgery and imaging).

CONCLUSIONS

Ultrasonography is often used in minimally invasive surgical techniques. This adds to the visualisation of blood vessels, the correct identification of tumour margins, and the location of surgical instruments in the tissue. The development of ultrasound technology from 2D to 3D and 4D has brought improvements in minimally invasive and robotic surgical techniques, and it should be further studied to bring surgery to a higher level.

Accelerating 3-D GPU-based Motion Tracking for Ultrasound Strain Elastography Using Sum-Tables: Analysis and Initial Results

Now, with the availability of 3-D ultrasound data, a lot of research efforts are being devoted to developing 3-D ultrasound strain elastography (USE) systems. Because 3-D motion tracking, a core component in any 3-D USE system, is computationally intensive, a lot of efforts are under way to accelerate 3-D motion tracking. In the literature, the concept of Sum-Table has been used in a serial computing environment to reduce the burden of computing signal correlation, which is the single most computationally intensive component in 3-D motion tracking. In this study, parallel programming using graphics processing units (GPU) is used in conjunction with the concept of Sum-Table to improve the computational efficiency of 3-D motion tracking. To our knowledge, sum-tables have not been used in a GPU environment for 3-D motion tracking. Our main objective here is to investigate the feasibility of using sum-table-based normalized correlation coefficient (ST-NCC) method for the above-mentioned GPU-accelerated 3-D USE. More specifically, two different implementations of ST-NCC methods proposed by Lewis et al. and Luo-Konofagou are compared against each other. During the performance comparison, the conventional method for calculating the normalized correlation coefficient (NCC) was used as the baseline. All three methods were implemented using compute unified device architecture (CUDA; Version 9.0, Nvidia Inc., CA, USA) and tested on a professional GeForce GTX TITAN X card (Nvidia Inc., CA, USA). Using 3-D ultrasound data acquired during a tissue-mimicking phantom experiment, both displacement tracking accuracy and computational efficiency were evaluated for the above-mentioned three different methods. Based on data investigated, we found that under the GPU platform, Lou-Konofaguo method can still improve the computational efficiency (17–46%), as compared to the classic NCC method implemented into the same GPU platform. However, the Lewis method does not improve the computational efficiency in some configuration or improves the computational efficiency at a lower rate (7–23%) under the GPU parallel computing environment. Comparable displacement tracking accuracy was obtained by both methods.

A GPU-Accelerated 3-D Coupled Subsample Estimation Algorithm for Volumetric Breast Strain Elastography

Our primary objective of this paper was to extend a previously published 2-D coupled subsample tracking algorithm for 3-D speckle tracking in the framework of ultrasound breast strain elastography. In order to overcome heavy computational cost, we investigated the use of a graphic processing unit (GPU) to accelerate the 3-D coupled subsample speckle tracking method. The performance of the proposed GPU implementation was tested using a tissue-mimicking phantom and in vivo breast ultrasound data. The performance of this 3-D subsample tracking algorithm was compared with the conventional 3-D quadratic subsample estimation algorithm. On the basis of these evaluations, we concluded that the GPU implementation of this 3-D subsample estimation algorithm can provide high-quality strain data (i.e., high correlation between the predeformation and the motion-compensated postdeformation radio frequency echo data and high contrast-to-noise ratio strain images), as compared with the conventional 3-D quadratic subsample algorithm. Using the GPU implementation of the 3-D speckle tracking algorithm, volumetric strain data can be achieved relatively fast (approximately 20 s per volume [2.5 cm ×2.5 cm ×2.5 cm]).