Experimental Validation of an Elastic Registration Algorithm for Ultrasound Images

Abstract

Ultrasound is a favorable tool for intra-operative surgical guidance due to its fast imaging speed and non-invasive nature. However, deformations of the anatomy caused by breathing, heartbeat, and movement of the patient make it difficult to track the location of anatomical landmarks during intra-operative ultrasound-guided interventions. While elastic registration can be used to compensate for image misalignment, its adaptation for clinical use has only been gradual due to the lack of standardized guidelines to quantify the performance of different registration techniques. Evaluation of elastic registration algorithms is a difficult task since the point to point correspondence between images is usually unknown. This poses a major challenge in the validation of non-rigid registration techniques for performance comparisons. Current validation guidelines for non-rigid registration algorithms exist for the comparison of techniques for magnetic resonance images of the brain. These frameworks provide users with standardized brain datasets and performance measures based on brain region alignment, intensity differences between images, and inverse consistency of transformations. These metrics may not all be suitable for ultrasound registration algorithms due to the different properties of the imaging modalities. Furthermore, other metrics are required for validating the registration performance on different anatomical images with large deformations such as the liver. This work presents a validation framework dedicated for ultrasound elastic registration algorithms. Quantitative validation metrics are evaluated for ultrasound images. These include a simulation technique to measure registration accuracy, a segmentation algorithm to extract anatomical landmarks to measure feature overlap, and a technique to measure the alignment of images using similarity metrics. An extensive study of an ultrasound temporal registration algorithm is conducted using the proposed validation framework. Experiments are performed on a large database of 2D and 3D US images of the carotid artery and the liver to assess the performance of this algorithm. In addition, two graphical user interfaces which integrate the image registration and segmentation techniques have been developed to visualize the performance of these algorithms on ultrasound images captured in real time. In the future, these interfaces may be used to enhance ultrasound examination.