A Recurrent Neural-Network-Based Real-Time Dynamic Model for Soft Continuum Manipulators.

This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.

A novel method for planning liver resections using deformable Bézier surfaces and distance maps.

BACKGROUND AND OBJECTIVE: For more than a decade, computer-assisted surgical systems have been helping surgeons to plan liver resections. The most widespread strategies to plan liver resections are: drawing traces in individual 2D slices, and using a 3D deformable plane. In this work, we propose a novel method which requires low level of user interaction while keeping high flexibility to specify resections. METHODS: Our method is based on the use of Bézier surfaces, which can be deformed using a grid of control points, and distance maps as a base to compute and visualize resection margins (indicators of safety) in real-time. Projection of resections in 2D slices, as well as computation of resection volume statistics are also detailed. RESULTS: The method was evaluated and compared with state-of-the-art methods by a group of surgeons (n=5, 5-31 years of experience). Our results show that theproposed method presents planning times as low as state-of-the-art methods (174 s median time) with high reproducibility of results in terms of resected volume. In addition, our method not only leads to smooth virtual resections easier to perform surgically compared to other state-of-the-art methods, but also shows superior preservation of resection margins. CONCLUSIONS: Our method provides clinicians with a robust and easy-to-use method for planning liver resections with high reproducibility, smoothness of resection and preservation of resection margin. Our results indicate the ability of the method to represent any type of resection and being integrated in real clinical work-flows.

Surface reconstruction for planning and navigation of liver resections.

Computer-assisted systems for planning and navigation of liver resection procedures rely on the use of patient-specific 3D geometric models obtained from computed tomography. In this work, we propose the application of Poisson surface reconstruction (PSR) to obtain 3D models of the liver surface with applications to planning and navigation of liver surgery. In order to apply PSR, the introduction of an efficient transformation of the segmentation data, based on computation of gradient fields, is proposed. One of the advantages of PSR is that it requires only one control parameter, allowing the process to be fully automatic once the optimal value is estimated. Validation of our results is performed via comparison with 3D models obtained by state-of-art Marching Cubes incorporating Laplacian smoothing and decimation (MCSD). Our results show that PSR provides smooth liver models with better accuracy/complexity trade-off than those obtained by MCSD. After estimating the optimal parameter, automatic reconstruction of liver surfaces using PSR is achieved keeping similar processing time as MCSD. Models from this automatic approach show an average reduction of 79.59% of the polygons compared to the MCSD models presenting similar smoothness properties. Concerning visual quality, on one hand, and despite this reduction in polygons, clinicians perceive the quality of automatic PSR models to be the same as complex MCSD models. On the other hand, clinicians perceive a significant improvement on visual quality for automatic PSR models compared to optimal (obtained in terms of accuracy/complexity) MCSD models. The median reconstruction error using automatic PSR was as low as 1.03±0.23mm, which makes the method suitable for clinical applications. Automatic PSR is currently employed at Oslo University Hospital to obtain patient-specific liver models in selected patients undergoing laparoscopic liver resection.

Assessment of 3D motion increases the applicability of accelerometers for monitoring left ventricular function.

OBJECTIVES: Miniaturized accelerometers attached to the epicardium have been shown to provide useful clinical information. However, attachment of such a sensor has been cumbersome due to requirement of aligning the three sensor axes with the cardiac coordinate axes, limiting clinical utility. We propose a new method to process the three-dimensional (3D) accelerometer signal that does not require such alignment. METHODS: In 20 open-chest pigs, miniaturized 3D accelerometers were fixated on the epicardium in apical and basal regions of left ventricle. Accelerations in circumferential, longitudinal and radial directions were measured and a 3D velocity vector was calculated. Systolic velocity along the 3D vector and velocities in circumferential, longitudinal and radial directions were compared with the positive time derivate of left ventricular pressure during changes in global left ventricular function (epinephrine, esmolol and fluid loading) and to strain echocardiography during left anterior descending artery occlusion. RESULTS: Distinct changes in all accelerometer velocities were observed during alterations on global and regional left ventricular function. Accelerometer 3D and circumferential systolic velocities in apical region best reflected left ventricular function during interventions on global function by correlating significantly with the positive time derivate of left ventricular pressure, r = 0.83 and r = 0.86, respectively. The accelerometer 3D velocity also demonstrated equally good capacity as circumferential velocity in discriminating coronary occlusion from interventions on global left ventricular function with sensitivity/specificity of 0.90/0.83 and 0.90/0.86, respectively. CONCLUSIONS: Accelerometer 3D systolic velocity showed very good correspondence to changes in global and regional left ventricular function. Our results demonstrate that by the use of the accelerometer 3D motion vector, no alignment of the sensor with the cardiac coordinate axes was required. This increases potential clinical applicability of the accelerometer in cardiac surgery.