Adaptive spatial compounding for improving ultrasound images of the epidural space on human subjects

TLDR: The development of an updated compounding algorithm and results from a clinical study show a significant improvement in quality when median-based compounding with warping is used to align the set of beam-steered images and combine them.

Abstract: Administering epidural anesthesia can be a difficult procedure, especially for inexperienced physicians. The use of ultrasound imaging can help by showing the location of the key surrounding structures: the ligamentum flavum and the lamina of the vertebrae. The anatomical depiction of the interface between ligamentum flavum and epidural space is currently limited by speckle and anisotropic reflection. Previous work on phantoms showed that adaptive spatial compounding with non-rigid registration can improve the depiction of these features. This paper describes the development of an updated compounding algorithm and results from a clinical study. Average-based compounding may obscure anisotropic reflectors that only appear at certain beam angles, so a new median-based compounding technique is developed. In order to reduce the computational cost of the registration process, a linear prediction algorithm is used to reduce the search space for registration. The algorithms are tested on 20 human subjects. Comparisons are made among the reference image plus combinations of different compounding methods, warping and linear prediction. The gradient of the bone surfaces, the Laplacian of the ligamentum flavum, and the SNR and CNR are used to quantitatively assess the visibility of the features in the processed images. The results show a significant improvement in quality when median-based compounding with warping is used to align the set of beam-steered images and combine them. The improvement of the features makes detection of the epidural space easier.

Figures

Table 1. Maximum Laplacian of a slice at ligamentum flavum for a human subject in set 1 (this analysis should be done for each set of ultrasound transducer parameters)

Figure 1. Epidural needle insertion, midline approach. The epidural space is circled.

Table 2. Gradient and Laplacian at regions of interest for human subjects for several methods

Figure 3. Images for subject 15 and 5; the region in the white rectangle is used for quantitative evaluation of features near the epidural space, the structure being pointed to is the ligamentum flavum doublet and the circled structure is the lamina or bone boundary.

Table 4. Computational cost of spatial compounding with warping for different parts of the algorithm. CPU time is calculated using a P4 3.0GHz with 1GB RAM, and a warping search region of ±4×±4 (set 2 with image size 636 x 359) and ±8×±2 (set 1 with image size 726 x 423) pixels.

Figure 2. a) Ultrasound image of the lumbar spine with speckle noise and shadowing. A sagittal paramedian plane is used. b) Outlines of the reference and two beam steered images are shown. Spatial compounding uses positive and negative isonation angles to produce a set of beam-steered images that are subsequently combined.