Adaptive spatial compounding for improving ultrasound images of the epidural space

Abstract

Epidural anesthesia can be a difficult procedure, especially for inexperienced physicians. The use of ultrasound imaging can help by depicting the location of the epidural space to choose the needle trajectory appropriately. Anatomical features in the lower back are not always clearly visible because of speckle poor reflection from structures at certain angles, and shadows from bony surfaces. Spatial compounding has the potential to reduce speckle and emphasize structures by averaging a number of images taken at different isonation angles. However, the beam-steered images are not perfectly aligned due to non-constant speed of sound causing refraction errors. This means compounding can blur features. A non-rigid registration method, called warping, shifts each block of pixels of the beam-steered images in order to find the best alignment to the reference image without beam-steering. By applying warping, the features become sharper after compounding. To emphasize features further, edge detection is also applied to the individual images in order to select the best features for compounding. The warping and edge detection parameters are calculated in real-time for each acquired image. In order to reduce computational complexity, linear prediction of the warping vectors is used. The algorithm is tested on a phantom of the lower back with a linear probe. Qualitative comparisons are made among the original plus combinations of compounding, warping, edge detection and linear prediction. The linear gradient and Laplacian of a Gaussian are used to quantitatively assess the visibility of the bone boundaries and ligamentum flavum on the processed images. The results show a significant improvement in quality.