Identification of reduced-order thermal therapy models using thermal MR images: theory and validation

In this paper, we develop and validate a method to identify computationally efficient site- and patient-specific models of ultrasound thermal therapies from MR thermal images. The models of the specific absorption rate of the transduced energy and the temperature response of the therapy target are identified in the reduced basis of proper orthogonal decomposition of thermal images, acquired in response to a mild thermal test excitation. The method permits dynamic reidentification of the treatment models during the therapy by recursively utilizing newly acquired images. Such adaptation is particularly important during high-temperature therapies, which are known to substantially and rapidly change tissue properties and blood perfusion. The developed theory was validated for the case of focused ultrasound heating of a tissue phantom. The experimental and computational results indicate that the developed approach produces accurate low-dimensional treatment models despite temporal and spatial noises in MR images and slow image acquisition rate.     

一种针对BMP格式图像的LSB数字隐藏方法

介绍了一种针对BMP格式图像的数字隐藏方法。该方法对图像LSB(Lest Significant Bit) 平面进行随机嵌入。具有良好的隐蔽性,非法用户从载密图片中很难恢复出隐藏信息,可用于保密通信中。     

An analytic approach to the LSA mode

The conditions necessary for operation in the limited space-charge accumulation (LSA) mode, and the efficiencies that can be obtained, have been calculated for a range of low-field mobilities in GaAs. The analysis follows the theory published by Copeland, but the expression chosen to describe the velocity field characteristics allows direct solution of the integrals that occur in the theory, and the extension of the results to include GaAs with differing values of low-field mobility. It has therefore been possible to present equations for the various parameters of interest, which are a function of low-field mobility. The results of the calculations indicate that efficiency increases with low-field mobility, but the range of values of electron concentration/frequency becomes more restricted as the mobility is increased.     

Morphometric analysis of white matter lesions in MR images: method and validation

The analysis of MR images is evolving from qualitative to quantitative. More and more, the question asked by clinicians is how much and where, rather than a simple statement on the presence or absence of abnormalities. The authors present a study in which the results obtained with a semiautomatic, multispectral segmentation technique are quantitatively compared to manually delineated regions. The core of the semiautomatic image analysis system is a supervised artificial neural network classifier augmented with dedicated preand postprocessing algorithms, including anisotropic noise filtering and a surface-fitting method for the correction of spatial intensity variations. The study was focused on the quantitation of white matter lesions in the human brain. A total of 36 images from six brain volumes was analyzed twice by each of two operators, under supervision of a neuroradiologist. Both the intra- and interrater variability of the methods were studied in terms of the average tissue area detected per slice, the correlation coefficients between area measurements, and a measure of similarity derived from the kappa statistic. The results indicate that, compared to a manual method, the use of the semiautomatic technique not only facilitates the analysis of the images, but also has similar or lower intra- and interrater variabilities.     

An analytic method for the design of a constant resistance multiplexer

The design of a constant resistance multiplexer composed of three reactance ladder networks connected either in series or in parallel is a very practical problem. In this paper, an improvement on Norton's method and a set of necessary and sufficient conditions on the realizability of a Butterworth or elliptic constant resistance multiplexer are presented. Two illustrative examples are given to show the design procedure.     

Estimating the bias field of MR images

The authors propose a modification of Wells et al. (ibid., vol. 15, no. 4, p. 429-42, 1996) technique for bias field estimation and segmentation of magnetic resonance (MR) images. They show that replacing the class other, which includes all tissue not modeled explicitly by Gaussians with small variance, by a uniform probability density, and amending the expectation-maximization (EM) algorithm appropriately, gives significantly better results. The authors next consider the estimation and filtering of high-frequency information in MR images, comprising noise, intertissue boundaries, and within tissue microstructures. The authors conclude that post-filtering is preferable to the prefiltering that has been proposed previously. The authors observe that the performance of any segmentation algorithm, in particular that of Wells et al. (and the authors' refinements of it) is affected substantially by the number and selection of the tissue classes that are modeled explicitly, the corresponding defining parameters and, critically, the spatial distribution of tissues in the image. The authors present an initial exploration to choose automatically the number of classes and the associated parameters that give the best output. This requires the authors to define what is meant by “best output” and for this they propose the application of minimum entropy. The methods developed have been implemented and are illustrated throughout on simulated and real data (brain and breast MR)     

Off-resonance correction of MR images

In magnetic resonance imaging (MRI), the spatial inhomogeneity of the static magnetic field can cause degraded images if the reconstruction is based on inverse Fourier transformation. This paper presents and discusses a range of fast reconstruction algorithms that attempt to avoid such degradation by taking the field inhomogeneity into account. Some of these algorithms are new, others are modified versions of known algorithms. Speed and accuracy of all these algorithms are demonstrated using spiral MRI.     

Reversible compression of MR images

Methods for reversible coding can be classified according to the organization of the source model as either static, semi-adaptive, or adaptive. Magnetic resonance (MR) images have different statistical characteristics in the foreground and the background and separation is thus a promising path for reversible MR image compression. A new reversible compression method, based on static source models for foreground and background separately, is presented. The method is nonuniversal and uses contextual information to exploit the fact that entropy and bit rate are reduced by increasing the statistical order of the model. This paper establishes a realistic level of expectation regarding the bit rate in reversible MR image compression, in general, and the bit rate using static modeling, in particular. The experimental results show that compression using the new method can give bit rates comparable to the best existing reversible methods.     

An analytic continuation method for the analysis and design ofdispersive materials

All materials, by nature, possess a frequency-dependent permittivity. This dispersion can be expressed in the form of the Kramers-Kronig relations by invoking the analytic consequences of causality in the upper half of the complex frequency plane. However, the Hilbert transform pair character of these relations makes them useful only when half of the answer is already known. In order to derive a more general form useful for both synthesis and analysis of arbitrary materials, it is necessary to analytically continue the permittivity function into the lower half plane. Requiring that the dielectric polarization be expressible in terms of equations of motion, in addition to obeying causality, conservation of energy and the second law of thermodynamics is sufficient to obtain the desired expression as a sum of special complex functions. In the appropriate limits, this sum reduces to the Debye relaxation and Lorentz resonance models of dielectrics, but it also contains phenomena not expressible in terms of those classical models. In particular, the classic problem of the existence of optical transparency in water is resolved     

一种基于AHP的教学质量评估方法

运用系统工程学的层次分析法（AHP）和模糊理论，并结合教师教学工作的特点，将定性分析与定量分析相结合，利用科学的定量手段刻画教学质量评价中的定性问题，从而克服了教学评估工作中的主观随意性，提高了模糊综合评判的可靠性和准确性。     

Developing an analytic methodology to accurately predict probing depth in integrated circuit structures

This work designs an analytic methodology for applying the probe-before-bump procedure to predict probing depth and proposes feasible probing design parameters to avoid excessive probing of the bump pad. Two kinds of multi-level wafers were used to implement the probing experiment, with a single touch down, and an overdrive of 70 μm, 100 μm, 130 μm, and 150 μm by using a vertical probe card. The Young’s modulus and hardness of the two multilevel structures are measured on which the first bump pads are produced by sputtering aluminum onto the SiO₂, while the second bump pads are produced by sputtering aluminum onto the copper, creating a pad metal of approximately 1-μm thickness by using the nanoindenter. The test results indicate that the Young’s modulus of the thin film material exceeds that of bulk material by 20–30 GPa. The difference between analytic and experimental probing depth ranges from 2.3% to 8.9%, revealing that the proposed novel analytic model is extremely accurate. Engineers or researchers can use the analytic methodology to accurately predict probing depth and acquire probing parameters that are accurate, cost effective, and efficient, thus eliminating the need to use focused ion beam (FIB) or other measurement instruments to determine the probing depth.     

An improved differential box-counting method to estimate fractal dimensions of gray-level images

The differential box-counting (DBC) method is one of the frequently used techniques to estimate the fractal dimension (FD) of a 2D gray-level image. This paper presents an improved DBC method based on the original one for improvement of the accuracy. By adopting the modifying box-counting mechanism, shifting box blocks in (x, y) plane and selecting appropriate grid box sizes, it can solve the two kinds of problems which the DBC has: over-counting boxes along z direction and under-counting boxes just at the border of two neighboring box blocks where there is a sharp gray-level abruption exits. The experiments using two sets of synthetic images and one set of real natural texture images demonstrate that the improved DBC method can solve the two kinds of problems perfectly, simultaneously, and can outperform other DBC methods in the accuracy.     

A comprehensive approach to the analysis of contrast enhanced cardiac MR images

Current magnetic resonance imaging (MRI) technology allows the determination of patient-individual coronary tree structure, detection of infarctions, and assessment of myocardial perfusion. Joint inspection of these three aspects yields valuable information for therapy planning, e.g., through classification of myocardium into healthy tissue, regions showing a reversible hypoperfusion, and infarction with additional information on the corresponding supplying artery. Standard imaging protocols normally provide image data with different orientations, resolutions and coverages for each of the three aspects, which makes a direct comparison of analysis results difficult. The purpose of this work is to develop methods for the alignment and combined analysis of these images. The proposed approach is applied to 21 datasets of healthy and diseased patients from the clinical routine. The evaluation shows that, despite limitations due to typical MRI artifacts, combined inspection is feasible and can yield clinically useful information.     

Knowledge-based interpretation of MR brain images

The authors have developed a method for fully automated segmentation and labeling of 17 neuroanatomic structures such as thalamus, caudate nucleus, ventricular system, etc. in magnetic resonance (MR) brain images. The authors' method is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimization technique to generate and evaluate image interpretation hypotheses in a feedback loop. The authors' method was trained in 20 individual T1-weighted MR images. Observer-defined contours of neuroanatomic structures were used as a priori knowledge. The method's performance was validated in eight MR images by comparison to observer-defined independent standards. The GA-based image interpretation method correctly interpreted neuroanatomic structures in all images from the test set. Computer-identified and observer-defined neuroanatomic structure areas correlated very well (r=0.99, y=0,95x-2.1). Border positioning errors were small, with a root mean square (rms) border positioning error of 1.5+/-0.6 pixels. The authors' GA-based image interpretation method represents a novel approach to image interpretation and has been shown to produce accurate labeling of neuroanatomic structures in a set of MR brain images.     

The information content of MR images

The theoretical information content, defined by C.E. Shannon (1948), is proposed as an objective measure of MR (magnetic resonance) image quality. This measure takes into account the contrast-to-noise ratio (CNR), scan resolution, and field of view. It is used to derive an optimum in the tradeoff problem between image resolution and CNR, and as a criterion to assess the usefulness of high-resolution (512(2)) MR images. The result tells that for a given total acquisition time, an optimum value of the resolution can be found. This optimum is very broad. To apply Shannon's theory on information constant to MR images, a model for the spatial spectral power density of these images is required. Such a model has been derived from experimental observations of ordinary MR images, as well as from theoretical considerations.     

Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization

We are developing methods to characterize atherosclerotic disease in human carotid arteries using multiple MR images having different contrast mechanisms (T1W, T2W, PDW). To enable the use of voxel gray values for interpretation of disease, we created a new method, local entropy minimization with a bicubic spline model (LEMS), to correct the severe (approximately 80%) intensity inhomogeneity that arises from the surface coil array. This entropy-based method does not require classification and robustly addresses some problems that are more severe than those found in brain imaging, including noise, steep bias field, sensitivity of artery wall voxels to edge artifacts, and signal voids near the artery wall. Validation studies were performed on a synthetic digital phantom with realistic intensity inhomogeneity, a physical phantom roughly mimicking the neck, and patient carotid artery images. We compared LEMS to a modified fuzzy c-means segmentation based method (mAFCM), and a linear filtering method (LINF). Following LEMS correction, skeletal muscles in patient images were relatively isointense across the field of view. In the physical phantom, LEMS reduced the variation in the image to 1.9% and across the vessel wall region to 2.5%, a value which should be sufficient to distinguish plaque tissue types, based on literature measurements. In conclusion, we believe that the correction method shows promise for aiding human and computerized tissue classification from MR signal intensities.     

一种改进的小波阈值图像去噪方法

介绍了小波阈值图像去噪的原理,并对常规的软、硬阈值函数在图像去噪中存在的缺陷进行分析,在软、硬阈值函数的基础上提出了一种改进的阈值函数。通过对含噪声图像分别采用常规的软、硬阈值函数和改进的阈值函数进行去噪处理,实验对比得出:当选取了合适的控制系数时,改进的阈值函数在图像去噪中不仅保留了常规软、硬阈值函数的去噪优越性,而且还克服了常规软、硬阈值函数存在的去噪缺陷,比常规的软、硬阈值函数去噪效果更好。     

An analytic nonlinear approach to sidelobe reduction

We propose an analytic technique for reducing sidelobe artifacts in synthetic aperture radar data. The technique, called leakage energy minimization (LEM), uses a spatially varying aperture function, the coefficients of which satisfy a simple optimality condition. The integrated sidelobe energy (or leakage energy) characterizing a given aperture weighting function is used as a metric for determining the likelihood of an aperture weighting. When applied to synthetic aperture radar data, the technique can effectively reduce sidelobe levels with negligible loss of resolution. The algorithm is computationally efficient, and its application requires no restrictions on the data sampling rate.     

A rapid look-up table method for reconstructing MR images from arbitrary K-space trajectories

Look-up tables (LUTs) are a common method for increasing the speed of many algorithms. Their use can be extended to the reconstruction of nonuniformly sampled k-space data using either a discrete Fourier transform (DFT) algorithm or a convolution-based gridding algorithm. A table for the DFT would be precalculated arrays of weights describing how each data point affects all of image space. A table for a convolution-based gridding operation would be a precalculated table of weights describing how each data point affects a small k-space neighborhood. These LUT methods were implemented in C++ on a modest personal computer system; they allowed a radial k-space acquisition sequence, consisting of 180 views of 256 points each, to be gridded in 36.2 ms, or, in approximately 800 ns/point. By comparison, a similar implementation of the gridding operation, without LUTs, required 45 times longer (1639.2 ms) to grid the same data. This was possible even while using a 4 x 4 Kaiser-Bessel convolution kernel, which is larger than typically used. These table-based computations will allow real time reconstruction in the future and can currently be run concurrently with the acquisition allowing for completely real-time gridding.