Quantization improvement in MRI using dual quantizers

The quantization of magnetic resonance imaging (MRI) data can cause information loss due to quantizer/data mismatch. The authors address a method for improved quantization as well as techniques for measuring the improvement in such methods. A dual quantizer scheme is described and simulated which is fast and more accurately quantizes MRI data than conventional methods. The approach is to use two quantizers, one for the high-level data and one for the low-level data. This adaptive, dual quantization scheme is simple and provides significant improvements in image quality, especially for three-dimensional (3-D) acquisition. Results are given which show how well the low frequencies are represented and indicate the increased fidelity of the high-frequency components. These results show a significant improvement in signal-to-noise ratio as well as in detection tasks for both noiseless data and data which include varying amounts of system noise.     

Iterative ultrasonic signal and image deconvolution for estimation of the complex medium response

The ill‐conditioned inverse problem of estimating ultrasonic medium responses by deconvolution of RF signals is investigated. The primary difference between the proposed method and others is that the medium response function is assumed to be complex‐valued rather than restricted to being real‐valued. Derived from the complex medium model, complex Wiener filtering is presented, and a Hilbert transform related limitation to inverse filtering type methods is discussed. We introduce a nonparametric iterative algorithm, the least squares method with point count regularization (LSPC). The algorithm is successfully applied to simulated and experimental data and demonstrates the capability of recovering both the real and imaginary parts of the medium response. The simulation results indicate that the LSPC method can outperform Wiener filters and improve the resolution of the ultrasound system by factors as high as 3.7. Experimental results using a single element transducer and a conventional medical ultrasound system with a linear array transducer show that despite the errors in pulse estimation and the noise in the RF signals, excellent results can be obtained, demonstrating the stability and robustness of the algorithm. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 266–277, 2005     

Ultrasound measurement of brachial flow-mediated vasodilator response

Brachial artery flow-mediated vasodilation is increasingly used as a measure of endothelial function. High resolution ultrasound provides a noninvasive method to observe this flow-mediated vasodilation by monitoring the diameter of the artery over time following a transient flow stimulus. Since hundreds of ultrasound images are required to continuously monitor brachial diameter for the 2-3 min during which the vasodilator response occurs, an automated diameter estimation is desirable. However, vascular ultrasound images suffer from structural noise caused by the constructive and destructive interference of the backscattered signals, and the true boundaries of interest that define the diameter are frequently obscured by the multiple-layer structure of the vessel wall. These problems make automated diameter estimation strategies based on the detection of the vessel wall boundary difficult. We obtain a robust automated measurement of the vasodilator response by automatically locating the artery using a variable window method, which gives both the lumen center and width. The vessel wall boundary is detected by a global constraint deformable model, which is insensitive to the structural noise in the boundary area. The ambiguity between the desired boundary and other undesired boundaries is resolved by a spatiotemporal strategy. Our method provides excellent reproducibility both for interreader and intrareader analyzes of percent change in diameter, and has been successfully used in analyzing over 4000 brachial flow-mediated vasodilation scans from several medical centers in the United States.     

Using Convex Set Techniques for Combined Pixel and Frequency Domain Coding of Time-Varying Images

Projection onto convex sets has recently been suggested as an approach to time-varying image compression. In this paper, an image coding technique based on such an approach is developed. In particular, pixel and frequency domain information is combined in order to improve image quality over standard transform coding at given bit rates. The use of nonintersecting convex sets is shown to be optimum for two sets and a given error measure. Convex sets and projections are discussed in general and then in the context of image compression. Simulation results are presented for single frame images as well as for image sequences.     

Statistical models of partial volume effect

Statistical models of partial volume effect for systems with various types of noise or pixel value distributions are developed and probability density functions are derived. The models assume either Gaussian system sampling noise or intrinsic material variances with Gaussian or Poisson statistics. In particular, a material can be viewed as having a distinct value that has been corrupted by additive noise either before or after partial volume mixing, or the material could have nondistinct values with a Poisson distribution as might be the case in nuclear medicine images. General forms of the probability density functions are presented for the N material cases and particular forms for two- and three-material cases are derived. These models are incorporated into finite mixture densities in order to more accurately model the distribution of image pixel values. Examples are presented using simulated histograms to demonstrate the efficacy of the models for quantification. Modeling of partial volume effect is shown to be useful when one of the materials is present in images mainly as a pixel component.