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Proceedings ArticleDOI

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

06 Mar 2008-Vol. 6920, Iss: 37, pp 157-168

TL;DR: 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.

AbstractAdministering 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.

Summary (4 min read)

1.1 Epidural anesthesia in obstetrics

  • Anesthesia is injected into the epidural space as shown on Fig. 1 and a nerve block for the lower body is then provided.
  • The catheter is then inserted through the epidural needle shaft into the epidural space.
  • The ultrasound group achieved a success rate of 84% in the first 10 attempts whereas the control group had a success rate of 60%.
  • Which is around 7mm and much smaller than the epidural depth of adults (20-90mm), it shows the potential benefit of using ultrasound to visualize the lumbar region during an epidural needle insertion procedure.

1.2 Ultrasound imaging of lumbar region

  • Ultrasound is noninvasive, harmless at low power, portable, accurate and cost effective.
  • Ultrasound of the lumbar region shows an image filled with speckle and artifacts which can impede detection of important features such as the ligamentum flavum and other hard to detect structures.
  • The received echo is based on ultrasound reflections from large-scale (relative to wavelength) structures, such as bone (i.e. specular reflection) and reflections from small-scale structures, such as cells (i.e. random scattering).
  • If the specular reflection is strong enough, such as a bone, it casts shadows in the beam direction and structures underneath are obstructed.
  • Terms of Use: http://spiedl.org/terms with respect to the reflectors.

1.3 Image processing techniques

  • Many post-processing methods employ filters to reduce speckle.
  • Examples are the diffusion filter 10 , the adaptive weighted median filter11 , homogeneous region growing mean filter, and aggressive region growing filter 12 .
  • Frequency compounding captures several images at the same location at different transmission frequencies, decorrelating the speckle patterns among images.
  • Spatial compounding is now popular among commercial manufacturers and will be presented in the next section.

2.1 Spatial compounding

  • Spatial compounding uses beam steering19 20 21 , which captures several frames by sending the ultrasound pulses at different angles of incidence (see Fig. 2(b) for an illustration of the principle).
  • The application of spatial compounding to these images reduced speckle noise and improved the boundary continuity.
  • Spatial compounding also has other benefits, such as the possibility of enhancing structures that are only visible at certain beam angles.
  • Certain weak but important features, such as a biopsy needle 9 24 only appear at certain Proc. of SPIE Vol.
  • The structure of main interest in this work is the epidural space, which is immediately under the ligamentum flavum and initial experience suggests it is only clear at certain beam angles.

2.2 Registration techniques

  • Conventional spatial compounding still suffers from blurring due to misalignment of features.
  • The speed of sound varies by as much as 14% in soft tissue25 and the resulting distortion (including refraction) causes the apparent positions of structures to be slightly different under different angles of incidence.
  • Re-alignment of the features using an additional block-matching non-rigid registration was previously proposed by their group to properly align the structures of each image 13 .
  • The result was a sharper ultrasound image.
  • Building on those results, the warping/compounding method is extended here to improve visibility of the ligamentum flavum in vivo, and therefore the epidural space.

2.2.1 Similarity measures

  • Registration performance is highly dependent on the similarity measure used as a cost function for finding the best alignment.
  • A good similarity measure will yield a single strong peak upon best alignment.
  • Previous literature used several methods such as sum of absolute differences (SAD), mean squared error (MSE) 27 , normalized covariance (NCOV), normalized crosscorrelation (NCC), entropy of the difference image and mutual information28 .
  • Mutual information29 30 is a very popular similarity measure for registration of multimodality images but is too easily affected by artifacts.
  • Since the two images to be registered are quite similar, the means are assumed to be small enough so that the NCC and NCOV will yield similar results.

2.2.2 Interpolation and mapping

  • The beam-steered images are divided into blocks and each block registered to the reference image.
  • Once the individual warping vectors have been found for each block, each pixel is assigned a warping vector by smooth interpolation.
  • Many interpolation techniques are well known and have been compared on operations such as resizing and rotation31 32 33 .
  • Popular interpolation techniques are placed in order of performance as follows: nearest neighbour, linear, cubic and cubic B-spline.
  • Inverse mapping does not encounter the problem of holes and overlapping like in forward mapping as each pixel has only one associated value.

2.3 Linear prediction techniques

  • In order to further reduce computational cost, a coarse to fine or multi-resolution approach is often used where lower resolution blocks are registered and then higher resolution blocks are registered using a smaller search region 13 .
  • The top of the image is often noisy with poor resolution so it is not suitable as the basis for finding the initial warping vectors.
  • It is not guaranteed to be the location with strong feature content and may contain shadows.
  • The Canny edge detector is used to detect areas containing edges, and then the block with the highest count is assumed to be the best starting candidate.

2.4 Median-based compounding

  • Since the features of interest do not appear on all frames, taking the average may not highlight weak anisotropic reflectors.
  • Accordingly, any edges are weighted more than homogeneous regions.
  • Many gradient calculation methods can be used.
  • This parameter should be set according to each type of image since speckle scale depends on probe characteristics such as frequency and depth setting.

2.5 Finding the right parameters

  • The first choice is the number of beam-steered images and the number of degrees between each image.
  • Too small an angle between each image and the speckle noise pattern will be highly correlated; too large and there will be few images within the range of angles that provide good image quality.

2.5.1 Warping parameters

  • The second parameter to choose is the size of the blocks.
  • The blocks must be large enough so that a block contains significant anatomical features, therefore making the registration more accurate, and small enough to produce a different warping vector for each block as each block is associated with a different refraction error.
  • A small search region size means that the best registration may not be found.
  • Quantitative measures are computed on the regions of interest in each image and the parameters giving the best results are chosen.
  • Table 1 shows the maximum Laplacian at the ligamentum flavum as the measure of interest.

3.1 Experimental setting

  • This study was approved by the Ethical Review Boards of the University of British Columbia and British Columbia Children’s and Women’s Health Centre and written informed consent was obtained from all subjects.
  • Subjects who had contraindictions to neuraxial anesthesia or who could not communicate in English were excluded.
  • Each subject was scanned in the sitting position with L3/L4 or L2/L3 interspaces identified using surface landmarks and confirmed by ultrasound.
  • Pre-scan converted B-mode images showing the ligamentum flavum and laminas were captured over the range of beam steering angles.
  • The sonographer finds the best image and then the beam-steered frames are acquired.

3.2 Qualitative evaluation

  • The authors now compare the images before and after compounding with different compounding methods.
  • Simple compounding (Fig. 4(b) and 5(b)) averages out most speckle, however, the ligamentum flavum and the bone boundaries are blurred.
  • Using warping (Fig. 4(c) and 5(c)) sharpens the compounded image and the ligamentum flavum is seen as a doublet again.
  • Fig. 5 shows a case where the doublet is a very faint structure, compounding indeed loses the depiction of the doublet by blurring.

3.3 Quantitative measures

  • There are two structures of interest in this clinical application: the ligamentum flavum and the lamina which is the bone seen in the images.
  • Ideally, the authors would see a set of sharp lines, therefore the Laplacian of the leading line is taken as the quantitative measure.
  • The quantitative measures are calculated on all 20 sets of images and results are compiled in Table 2.
  • The features are difficult to discern due to speckle, as shown in Figs. 4(a) and 5(a).
  • Here the compounding methods show a large improvement.

3.4 Computational cost

  • The different methods presented above have various individual computational costs which are summarized in Table 4.
  • Spatial compounding alone adds very little extra cost.
  • Terms of Use: http://spiedl.org/terms frame rate down to two frames per second which makes it impractical for real-time implementation.

4.1 Summary

  • One usually relies on the lamina which is a stronger reflector to predict where the ligamentum flavum is.
  • The images are heavily affected by speckle noise.
  • Using warping makes edges clearer and thus makes the skin-to-epidural depth easier to measure.
  • The choice of parameters will not only affect the intelligibility of the compounded image but also affects the computational cost.
  • The median-based compounding yields sharper edges and certain details on surrounding tissues can be resolved but at a very high cost.

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Figures (8)
Citations
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Journal ArticleDOI
TL;DR: Paramedian ultrasound can be used to estimate the midline depth to the epidural space, but the surrogate measures are not sufficiently correlated with the Depth of the Epidural space to recommend them as a replacement for the actual depth.
Abstract: BACKGROUND: Ultrasound is receiving growing interest for improving the guidance of needle insertion in epidural anesthesia. Defining a paramedian ultrasound scanning technique would be helpful for correctly identifying the vertebral level. Finding surrogate measures of the depth of the epidural space may also improve the ease of scanning. METHODS: We examined 20 parturients with pre-epidural ultrasound in the paramedian plane, and the predicted depth was compared with the actual midline depth. The actual depth was also compared with subject biometrics, depth of transverse process, and thickness of lumbar fat. RESULTS: The scanning technique allowed the depth of the epidural space to be measured in all subjects. The depth measured in ultrasound was strongly correlated to the actual depth (R 2 = 0.8 and 95% limits of agreement of -14.8 to 5.2 mm), unlike patient biometrics (R 2 < 0.25), the depth of the neighboring transverse processes (R 2 = 0.35 and 95% limits of agreement of -13.8 to 19.1 mm), or the thickness of overlying fat (R 2 = 0.66). The duration of the ultrasound scan was 10 min at the beginning of the trial and 3 min for the last subjects. CONCLUSIONS: Paramedian ultrasound can be used to estimate the midline depth to the epidural space. The surrogate measures are not sufficiently correlated with the depth to the epidural space to recommend them as a replacement for the actual depth to the epidural space measurement.

50 citations


Cites background from "Adaptive spatial compounding for im..."

  • ...The possible correlation with fat thickness was initially thought to be helpful with automatic image processing of the features to emphasize the epidural space.(13,14) This is being investigated further because speed of sound variations can cause image distortion through compression and refraction....

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Proceedings ArticleDOI
23 Jun 2008
TL;DR: This paper investigates reconstruction of the acoustic impedance from ultrasound images for the first time by combining multiple images to improve the estimation and uses phase information to determine regions of high reflection from an ultrasound image.
Abstract: Reflection of sound waves, due to acoustic impedance mismatch at the interface of two media, is the principal physical property which allows visualization with ultrasound. In this paper, we investigate reconstruction of the acoustic impedance from ultrasound images for the first time. Similar to spatial compounding, we combine multiple images to improve the estimation. We use phase information to determine regions of high reflection from an ultrasound image. We model the physical imaging process with an emphasis on the reflection of sound waves. The model is used in computing the acoustic impedance (up to a scale) from areas of high reflectivity. The acoustic impedance image can either be directly visualized or be used in simulation of ultrasound images from an arbitrary point of view. The experiments performed on in-vitro and in-vivo data show promising results.

13 citations


Journal ArticleDOI
TL;DR: A significant improvement in quality is shown when using warping with adaptive median-based compounding, and linear prediction is used to find the warping vectors and decrease computational cost.
Abstract: Ultrasound imaging can help in choosing the needle trajectory for epidural anesthesia but anatomical features are not always clear. Spatial compounding can emphasize structures; however, features in the beam-steered images are not aligned due to varying speeds of sound. A non-rigid registration method, called warping, shifts pixels of the beam-steered images to best match the reference image. Linear prediction is used to find the warping vectors and decrease computational cost. An adaptive median-based combination technique for compounding is also investigated. The algorithms are tested on a spine phantom and human subjects. The results show a significant improvement in quality when using warping with adaptive median-based compounding.

11 citations


Patent
08 Dec 2014
Abstract: An image compounding apparatus acquires, via ultrasound, pixel-based images (126-130) of a region of interest for, by compounding, forming a composite image of the region. The image includes composite pixels (191) that spatially correspond respectively to pixels of the images. Further included is a pixel processor for beamforming with respect to a pixel from among the pixels, and for assessing, with respect to the composite pixel and from the data acquired (146), amounts of local information content of respective ones of the images. The processor determines, based on the assessment, weights for respective application, in the forming, to the pixels, of the images, that spatially correspond to the composite pixel. In some embodiments, the assessing commences operating on the data no later than upon the beamforming. In some embodiments, brightness values are assigned to the spatially corresponding pixels; and, in spatial correspondence, the maximum and the mean values are determined. They are then utilized in weighting the compounding.

2 citations


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TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.

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Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >

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TL;DR: A new information-theoretic approach is presented for finding the pose of an object in an image that works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation.
Abstract: A new information-theoretic approach is presented for finding the pose of an object in an image. The technique does not require information about the surface properties of the object, besides its shape, and is robust with respect to variations of illumination. In our derivation few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and may foreseeably be used in a wide variety of imaging situations. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images, aligning a complex 3D object model to real scenes including clutter and occlusion, tracking a human head in a video sequence and aligning a view-based 2D object model to real images. The method is based on a formulation of the mutual information between the model and the image. As applied here the technique is intensity-based, rather than feature-based. It works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation. Additionally, it has an efficient implementation that is based on stochastic approximation.

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TL;DR: An overview is presented of the medical image processing literature on mutual-information-based registration, an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on a specific application.
Abstract: An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.

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Book
10 Aug 1990
TL;DR: This book is intended to be a practical guide for eclectic scientists and engineers who find themselves in need of implementing warping algorithms and comprehending the underlying concepts.
Abstract: From the Publisher: This book is intended to be a practical guide for eclectic scientists and engineers who find themselves in need of implementing warping algorithms and comprehending the underlying concepts.

1,449 citations


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    [...]