Shear rate has been linked to various arterial wall properties and diseases such as cell function, neointimal hyperplasia, post-stenotic dilation and progression of atherosclerotic plaque. An accurate noninvasive method of measuring blood flow near the arterial wall is needed to allow the study of wall shear rate in humans to progress. Current clinical vascular laboratories are limited to using the Hagen-Poiseuille formulation based on steady laminar flow through a rigid- walled tube, which is not realized in vivo. This project used data collected with an ultrasound scanner to approximate the magnitude of wall shear rate in two dimensions using the formal definition of the velocity gradient in a radial direction. Blood flow was measured in the common carotid artery of 20 subjects in both longitudinal and transverse directions. The results showed that the wall shear rate was a factor of approximately 1.5 to 2 times greater than the value calculated using the Hagen-Poiseuille formulation. A comparison of the longitudinal and transverse estimation methods showed very similar values for all of the calculated quantities. This comparison contributed to the conclusion that this image-based technique provides a more accurate assessment of wall shear rate, with the significant addition of a second dimension for analysis.
This paper presents results for the application of the orthogonal projection (OP) filter to AVIRIS hyperspectral images of the Lunar Crater Volcanic Field in Nevada. The OP filter is a special case of the simultaneous-diagonalization (SD) filter, developed to enhance a selected feature while suppressing other features and noise in an image scene. The SD filter applies to sets of images that are e spatially invariant (SI) and in which the individual features in the image scene and noise contribute linearly and additively (LA) to the recorded pixel image intensities. The OP filter uses the original LA SI image set and the signatures of the individual features to generate a new set of images in which the distinct features are separated. Applied to AVIRIS hyperspectral images, the OP filter performs spectral unmixing. The resulting filtered images are estimates of the spatial distributions, or endmembers, of the original image features, and can be used to reconstruct estimates of the original image. Since the number of individual features is much smaller than the number of images in the original set, this represents significant data compression. However, the filtered images are not perfect due to images are not perfect due to imaging system noise and imperfections in the fit of the LA SI model to the actual AVIRIS images. As a test of the accuracy of the compression method, this paper investigates the reconstruction of the original AVIRIS images from the small set of individual feature images.
Shear rate has been linked to endothelial and smooth muscle cell function, neointimal hyperplasia, poststenotic dilation and progression of atherosclerotic plaque. In vivo studies of shear rate have been limited in humans due to the lack of a truly accurate noninvasive method of measuring blood flow. In clinical vascular laboratories, the primary method of wall shear rate estimation is the scaled ratio between the center line systolic velocity and the local arterial radius. The present study compares this method with the shear rate calculated directly from data collected using a Doppler ultrasound scanner. Blood flow in the superficial femoral artery of 20 subjects was measured during three stages of distal resistance. Analysis and display programs were written for use with the MATLAB image processing software package. The experimental values of shear rate were calculated using the formal definition and then compared to the standard estimate. In all three states of distal resistance, the experimental values were significantly higher than the estimated values by a factor of approximately 1.57. These results led to the conclusion that the direct method of measuring shear rate is more precise and should replace the estimation model in the clinical laboratory.
This paper investigates the special properties and results involved in the application of the Karhunen-Loeve (KL) transformation, also called principal component analysis (PCA) or Hotelling transform, to linearly-additive, spatially-invariant (LA SI) image sequences such as arise in many medical imaging applications (SPECT temporal studies, multi-parameter MRI, etc.), multispectral remote sensing, and elsewhere. The special structure of LA SI image sequences provides some interesting results both for the KL analysis and for the resulting principal component images. Simulated images and mathematical results indicate the KL implications for the special structure of LA SI images, in relation to the statistical order and feature characteristics of the image sequence. Simulated image sequences are understood by the mathematical results and illustrate the characteristics of KL compression and reconstruction for LA SI images. The well-known and widely used KL transform is a general and powerful image compression technique based on the statistical variance of the image data. However, it does not explicitly acknowledge specific features or their individual characteristics in an image set. For LA SI images, this may be an important limitation in relation to other methods of analysis and compression for such images.
Multispectral image sequences are one example of a class of image sequences that can be characterized as being spatially invariant. In this class of image sequences, all features are positionally invariant in each image of a given sequence but have varying gray-scale properties. The various features of the scene contribute additively to each image of the sequence but the image formation processes associated with given features have characteristic signatures describing the manner in which they vary over the image sequence. Such sequences can be processed using the simultaneous diagonalization (SD) filter which will generate gray- scale maps of the different image formation processes. The SD filter is based on an explicit mathematical model and can be used to maximize SNR, perform segmentation and provide data compression. A unique property of this approach is that even if several image formation processes occupy a given pixel, they can still be isolated. The gray-scale map associated with each process provides an estimate of the magnitude of a given process at every spatial location in the image sequence. Data compression and noise reduction can be achieved using the same spatially-invariant linearly-additive model and a variation of the simultaneous diagonalization filter.
Many important imaging applications generate a sequence of images that are (or can be made to be) a spatially invariant image sequence with linearly additive contributions from the components that form the images. They include functional images in nuclear medicine, multiparameter MR imaging, multi-energy x-ray imaging for DR and CT, and multispectral satellite images. Recent results in the modelling and analysis of linearly additive spatially invariant image sequences are based on the inherent structure of such images, and can be used to achieve significant data compression for image storage and still provide good reconstruction. The technique is applied here to a human renogram, with compression of a very noisy 180-image sequence to a 4-image set. The resulting reconstruction illustrates the potential of the method.
A class of image sequences can be characterized as being spatially invariant and linearly additive based on their image formation processes. In these kinds of sequences, all features are positionally invariant in each image of a given sequence but have varying gray-scale properties. The various features of the scene contribute additively to each image of the sequence but the image-formation processes associated with given features have characteristic signatures describing the manner in which they vary over the image sequence. Examples of appropriate image sequences include multispectral image sequences, certain temporal image sequences, and NMR image sequences generated by modification of the excitation parameters. Note that image sequences can be formed using a variety of imaging modalities as long as the linearly additive and spatially invariant requirements are not violated. Features associated with different image-formation processes generally will have unique signatures that can be used to generate linear filters for isolating selected image-formation processes or for performing data compression. Starting with an explicit mathematical model, techniques are presented for generating optimal filters using simultaneous diagonalization for enhancement of desired image-formation processes and data compression with this class of image sequences. A unique property of this approach is that even if several image-formation processes occupy a given pixel, they can still be isolated.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.