Particle swarm optimization for in vivo 3D ultrasound volume registration

As three-dimensional (3D) ultrasound is becoming more and more popular, there has been increased interest in using a position sensor to track the trajectory of the 3D ultrasound probe during the scan. One application is the improvement of image quality by fusion of multiple scans from different orientations. With a position sensor mounted on the probe, the clinicians face additional difficulties, for example, maintaining a line-of-sight between the sensor and the reference point. Therefore, the objective of this paper is to register the volumes using an automatic image-based registration technique. In this paper, we employ the particle swarm optimization (PSO) technique to calculate the six rigid-body transformation parameters (three for translation and three for rotation) between successive volumes of 3D ultrasound data. We obtain vertical and horizontal slices through the acquired volumes and then use an intensity-based similarity measure as a fitness function for each particle. We considered various settings in the PSO to find a set of parameters to give the best convergence. We found the visually acceptable registration when the initial orientations of the particles were confined to within a few degrees of the orientations obtained from position sensor

Three-dimensional ultrasound imaging

This review is about the development of three-dimensional (3D) ultrasonic medical imaging, how it works, and where its future lies. It assumes knowledge of two-dimensional (2D) ultrasound, which is covered elsewhere in this issue. The three main ways in which 3D ultrasound may be acquired are described: the mechanically swept 3D probe, the 2D transducer array that can acquire intrinsically 3D data, and the freehand 3D ultrasound. This provides an appreciation of the constraints implicit in each of these approaches together with their strengths and weaknesses. Then some of the techniques that are used for processing the 3D data and the way this can lead to information of clinical value are discussed. A table is provided to show the range of clinical applications reported in the literature. Finally, the discussion relating to the technology and its clinical applications to explain why 3D ultrasound has been relatively slow to be adopted in routine clinics is drawn together and the issues that will govern its development in the future explored

A study of similarity measures for in vivo 3D ultrasound volume registration

Most of the conventional ultrasound machines in hospitals work in two dimensions. However, there are some applications where doctors would like to be able to gather ultrasound data as a three-dimensional (3D) block rather than a two-dimensional (2D) slice. Two different types of 3D ultrasound have been developed to meet this requirement. One type involves a special probe that can record a fixed block of data, either by having an internal sweeping mechanism or by using electronic steering. The other type of 3D ultrasound uses a conventional 2D ultrasound probe together with a position sensor and is called freehand 3D ultrasound. A natural progression of the mechanically-swept 3D ultrasound system is to combine it with the free hand sensor. This results in an extended field of view. There are two major problems with using a position sensor. Firstly, line-of-sight needs to be maintained between the sensor and the reference point. Secondly, the multiple volumes rarely register because of tissue displacement and deformation. Therefore, the objective of this paper is to get rid of the inconvenient position sensor and to use an automatic image-based registration technique. We provide an experimental study of several intensity-based similarity measures for the registration of 3D ultrasound volumes. Rather than choosing a conventional voxel array to represent the 3D blocks, we use corresponding vertical and horizontal image slices from the blocks to be matched. This limits the amount of data thus making the calculation of the similarity measure less computationally expensive

Surface Interpolation From Sparse Cross-Sections Using Region Correspondence

The ability to estimate a surface from a set of cross-sections allows calculation of the enclosed volume and the display of the surface in three-dimensions (3-D). This process has increasingly been used to derive useful information from medical data. However, extracting the cross-sections (segmenting) can be very difficult, and automatic segmentation methods are not sufficiently robust to deal with all situations. Hence, it is an advantage if the surface reconstruction algorithm can work effectively on a small number of cross-sections. In addition, cross-sections of medical data are often quite complex. In this paper, an algorithm is presented which can interpolate a surface through sparse, complex cross-sections. This is an extension of maximal disc guided interpolation [25], which is itself based on shape based interpolation [8, 21]. The performance of this algorithm is demonstrated on various types of medical data (X-ray Computed Tomography, Magnetic Resonance Imaging and threedimen..