Nonrigid registration of 3-D free-hand ultrasound images of the breast

Abstract

Three-dimensional (3-D) ultrasound imaging of the breast enables better assessment of diseases than conventional two-dimensional (2-D) imaging. Free-hand techniques are often used for generating 3-D data from a sequence of 2-D slice images. However, the breast deforms substantially during scanning because it is composed primarily of soft tissue. This often causes tissue mis-registration in spatial compounding of multiple scan sweeps. To overcome this problem, in this paper, instead of introducing additional constraints on scanning conditions, we use image processing techniques. We present a fully automatic algorithm for 3-D nonlinear registration of free-hand ultrasound data. It uses a block matching scheme and local statistics to estimate local tissue deformation. A Bayesian regularization method is applied to the sample displacement field. The final deformation field is obtained by fitting a B-spline approximating mesh to the sample displacement field. Registration accuracy is evaluated using phantom data and similar registration errors are achieved with (0.19 mm) and without (0.16 mm) gaps in the data. Experimental results show that registration is crucial in spatial compounding of different sweeps. The execution time of the method on moderate hardware is sufficiently fast for fairly large research studies.

Toward a generic real-time compression correction framework for tracked ultrasound

“…A common approach is to utilize the intensity information in the ultrasound images to perform a nonrigid intensity-based registration with positional tracking of compressed images over a range of compression states [1,2]. One drawback of this method is that it requires a stream of ultrasound images, and intensity-based registration for ultrasound is a challenging task in practice.…”

Model-Based Correction of Tissue Compression for Tracked Ultrasound in Soft Tissue Image-Guided Surgery

“…However, this tissue deformation affects the geometry of the scanned objects and the resulting images. Soft tissue can undergo surface compression on the order of 1 cm during routine freehand imaging (Artignan et al 2004; Xiao et al 2002). This leads to incorrect estimates of the size and location of landmarks within the ultrasound images.…”

“…One method is to create a digital representation of the surface and then use a combination of Bayesian theory and prior knowledge of the surgical scene to create a deformation that matches the observed ultrasound data (King et al 2000), but this approach did not incorporate a physical model of tissue which could be used to provide more realistic priors. Another approach is to acquire B-mode or raw radiofrequency data from the ultrasound and use non-rigid image-based registration and positional tracking to correct for deformation (Treece et al 2005; Xiao et al 2002), but this approach requires a series of ultrasound images to provide sequential estimates of compression correction. There has also been work done to model tissue compression using data from a force transducer attached to the ultrasound probe along with a position sensor to drive a tissue model (Burcher et al 2001; Sun et al 2010).…”

Real-time image-based rigid registration of three-dimensional ultrasound

“…Typically scale space or sub-volume approaches are used for robustness and to improve computational efficiency (Krucker et al, 2002; Xiao et al, 2002; Pratikakis et al, 2003; Zikic et al, 2006; Ledesma-Carbayo et al, 2006). Some methods have used tracking or matching of features between images, where a dense deformation field is then found from interpolation or fitting a B-spline approximation to the feature displacements (Foroughi et al, 2006; Moradi et al, 2006).…”

“…These systems, along with the added expense of additional hardware, require careful calibration of the tracker to the ultrasound image (Mercier et al, 2005). Some of these systems also make use of image-based registration methods to refine the registration and account for other movements that cannot be tracked by the probe (Xiao et al, 2002; Gee et al, 2003; Poon and Rohling, 2006; Yao et al, 2009; Zhuang et al, 2010). For systems that do not use image-based refinement, to account for the displacement of anatomy due to respiration, either patients are put on breath-hold, images are respiration-gated (Makela et al, 2002), or respiration is tracked and accounted for using an additional tracker on the chest or abdomen (Wein et al, 2008).…”