A new impedance imaging system and dipole basis reconstruction method.

A number of studies have pointed to a link between the presence of breast cancer and a larger than normal tissue electrical admittance in the affected area. This phenomenon could make electrical impedance-based imaging a practical asset to breast cancer screening. In this paper image reconstruction algorithms are developed and evaluated for use with an impedance-based imaging system constructed at The George Washington University. The system, which is targeted for use in breast cancer screening, is profiled. Computer simulation-based results for the two reconstruction approaches are shown and evaluated.

Discrimination of MR images of breast masses with fractal-interpolation function models.

RATIONALE AND OBJECTIVES: The authors evaluated the feasibility of using statistical fractal-dimension features to improve discrimination between benign and malignant breast masses at magnetic resonance (MR) imaging. MATERIALS AND METHODS: The study evaluated MR images of 32 malignant and 20 benign breast masses from archived data at the University of Pennsylvania Medical Center. The test set included four cases that were difficult to evaluate on the basis of border characteristics. All diagnoses had been confirmed at excisional biopsy. The fractal-dimension feature was computed as the mean of a sample space of fractal-dimension estimates derived from fractal interpolation function models. To evaluate the performance of the fractal-dimension feature, the classification effectiveness of five expert-observer architectural features was compared with that of the fractal dimension combined with four expert-observer features. Feature sets were evaluated with receiver operating characteristic analysis. Discrimination analysis used artificial neural networks and logistic regression. Robustness of the fractal-dimension feature was evaluated by determining changes in discrimination when the algorithm parameters were perturbed. RESULTS: The combination of fractal-dimension and expert-observer features provided a statistically significant improvement in discrimination over that achieved with expert-observer features alone. Perturbing selected parameters in the fractal-dimension algorithm had little effect on discrimination. CONCLUSION: A statistical fractal-dimension feature appears to be useful in distinguishing MR images of benign and malignant breast masses in cases where expert radiologists may have difficulty. The statistical approach to estimating the fractal dimension appears to be more robust than other fractal measurements on data-limited medical images.

Use of the fast Hartley transform for three-dimensional dose calculation in radionuclide therapy.

Effective radioimmunotherapy may depend on a priori knowledge of the radiation absorbed dose distribution obtained by trace imaging activities administered to a patient before treatment. A new, fast, and effective treatment planning approach is developed to deal with a heterogeneous activity distribution. Calculation of the three-dimensional absorbed dose distribution requires convolution of a cumulated activity distribution matrix with a point-source kernel; both are represented by large matrices (64 x 64 x 64). To reduce the computation time required for these calculations, an implementation of convolution using three-dimensional (3-D) fast Hartley transform (FHT) is realized. Using the 3-D FHT convolution, absorbed dose calculation time was reduced over 1000 times. With this system, fast and accurate absorbed dose calculations are possible in radioimmunotherapy. This approach was validated in simple geometries and then was used to calculate the absorbed dose distribution for a patient's tumor and a bone marrow sample.

Estimating fractal dimension with fractal interpolation function models.

Fractal dimension (fd) is a feature which is widely used to characterize medical images. Previously, researchers have shown that fd separates important classes of images and provides distinctive information about texture. We analyze limitations of two principal methods of estimating fd: box-counting (BC) and power spectrum (PS). BC is ineffective when applied to data-limited, low-resolution images; PS is based on a fractional Brownian motion (fBm) model-a model which is not universally applicable. We also present background information on the use of fractal interpolation function (FIF) models to estimate fd of data which can be represented in the form of a function. We present a new method of estimating fd in which multiple FIF models are constructed. The mean of the fd's of the FIF models is taken as the estimate of the fd of the original data. The standard deviation of the fd's of the FIF models is used as a confidence measure of the estimate. We demonstrate how the new method can be used to characterize fractal texture of medical images. In a pilot study, we generated plots of curvature values around the perimeters of images of red blood cells from normal and sickle cell subjects. The new method showed improved separation of the image classes when compared to BC and PS methods.

Modeling and simulation of illumination effects for evaluation of microvessels of the conjunctiva.

We present the development of a comprehensive model that was undertaken to determine the relationships between the components of an image and the light intensity values present in the image of the microvessels of translucent tissues such as the bulbar conjunctiva. Experiments were conducted during the modeling process by use of a cylindrical microvessel embedded in a diffuse medium (phantom) on a reflecting background to affirm model components and simulations. The three-dimensional model was reduced to a single illumination plane with four regions of interest and modeled as Lambertian radiators and surfaces. The modeling showed that the top of the cylinder and its immediate vicinity are diffuse reflectors of light from the source plus light reflected from the background. The limbus of the cylinder is a diffuse reflector of the source and background illumination and a specular reflector of background reflections that achieve a high grazing angle with the cylinder. The immediate vicinity of the cylinder receives direct illumination from the source, but the light is partially obscured by the cylinder. The region beyond the shadow of the cylinder is a diffuse reflector of the overhead light. The diffuse medium additionally reflects the source and also attenuates the illumination reaching the other compo- rents of the scene. The direct and reflected illumination at each region of the model was calculated by use of specific geometric relationships. To verify those calculations, we analyzed a video simulation for the effects of different illumination conditions and their contributing elements. Intensity values were calculated from the relative reflectivity data determined from the video signals. The illumination values at the points along the line at the meridian of the cylinder were due to its reflectivity and also that of the medium. Similarly, the values of points distant from the shadow of the cylinder were due to the reflectivity of the background and the medium. The excellent agreement between the model and the phantom provides a foundation for the detection and precise measurement of microvessel dimensions within a diffuse medium. The additional ability to compute relative depth, from a single view, also permits discrimination between neighboring microvessels in complex images.

Threshold estimation in single photon emission computed tomography and planar imaging for clinical radioimmunotherapy.

Thresholding is the most widely used organ or tumor segmentation technique used in single photon emission computed tomography (SPECT) and planar imaging for monoclonal antibodies. Selecting the optimal threshold requires a priori knowledge (volumes from CT or magnetic resonance) for the size and contrast level of the organ in question. Failure to select an optimal threshold leads to overestimation or underestimation of the volume and, subsequently, the organ-absorbed dose value in radio-immunotherapy. To investigate this threshold selection problem, we performed a phantom experiment using six lucite spheres ranging from 1 to 117 ml and filled with a uniform activity of 1 microCi/ml Tc-99m. These spheres were placed at the center and off-center locations of a Jasczsak phantom and scanned with a three-headed gamma camera in SPECT and planar modes. Target-nontarget (T:NT) ratios were changed by adding the appropriate activity to the background. A threshold search algorithm with an interpolative background correction was applied to sphere images. This algorithm selects a threshold that minimizes the difference between the true and measured volumes (SPECT) or areas (planar). It was found that for spheres equal to or larger than 20 ml [diameter (D) > 38 mm] and T:NT ratios higher than 5:1, mean thresholds at 42% for SPECT and 38% for planar imaging yielded minimum image segmentation errors, which is in agreement with current literature. However, for small T:NT ratios (< 5:1), the threshold values as high as 71% for SPECT and 85% for planar imaging were substantially different than those fixed thresholds for large spheres (D > 38 mm). Hence, the use of fixed thresholds in low contrasts and with tumor and organ sizes of clinical interest (25 < or = D < or = 50 mm) may result in limited volume estimation accuracy. Therefore, we have provided the investigator a method to obtain the threshold values in which the proper threshold can be selected based on the organ and tumor size and image contrast. By measuring and calibrating the proper threshold value derived through machine-specific phantom measurements, a more accurate volume and activity quantitation can be performed. This, in turn, will provide tumor-absorbed dose optimization and greater accuracy in the measurement of potentially subacute, toxic absorbed doses to normal organs for patients undergoing radioimmunotherapy.

Image staining and differential diagnosis of ultrasound scans based on the Mahalanobis distance.

The covariance matrices associated with each state of health or disease from a previous study are used as the basis of an image staining display technique for aid in quantitative differential diagnosis. A state of health or disease is chosen by the clinician: this selects the covariance matrix from the data base. A region of interest (ROI) is then scrolled through an abdominal B-scan. For each position of the ROI a point in the four-dimensional feature space is calculated. A natural measure of the distance of this point from the center of mass (multivariate mean) of the disease class is calculated in terms of the covariance matrix of this class; this measure is the Mahalanobis distance. The confidence level for acceptance or rejection of the hypothesized disease class is obtained from the probability distribution of this distance, the T(2) probability law. This confidence level is color coded and used as a color stain that overlays the original scan at that position. The variability of the calculated features is studied as a function of ROI size, or the spatial resolution of the color coded image, and it is found that for an ROI in the neighborhood of 4 cm(2) most of the variability due to the finite number of independent samples (speckles) is averaged out, leaving the "noise floor" associated with inter- and intra-patient variability. ROIs on the order of 1 cm(2) may result with technical advances in B-scan resolution. A small number of points on organ boundaries are entered by the user, to fit with arcs of ellipses to be used to switch between organ (liver and kidney) data bases as the ROI encounters the boundary. By selecting in turn various state-of-health or state-of-disease databases, such images of confidence levels may be used for quantitative differential diagnosis. The method is not limited to ultrasound, being applicable in principle to features obtained from any modality or multimodality combination.

A combined-transform coding (CTC) scheme for medical images.

A combined-transform coding (CTC) scheme is proposed to reduce the blocking artifact of conventional block transform coding and hence to improve the subjective performance. The proposed CTC scheme is described and its information-theoretic properties are investigated. Computer simulation results for a class of chest X-ray images are presented. A comparison between the CTC scheme and the conventional discrete cosine transform (DCT) and discrete Walsh-Hadamard transform (DWHT) demonstrates the performance improvement of the proposed scheme. In addition, combined coding can also be used in noiseless coding, yielding a slight improvement in the compression performance if it is used properly.

Objective analysis of ultrasound images by use of a computational observer.

The design, implementation, and testing of a computational observer method for objective evaluation of ultrasound images are presented. The method uses digitized ultrasound B-scan images of a test phantom (the contrast-detail phantom), and is able to calculate the detectability of a target (signal) from its background (background noise). A quantitative detectability index, based on the measured signal-to-noise ratio of the image data, that is measured for both the human and the computational observer on the same scale is generated. It is shown that the computational observer (CO) method may be a more useful, objective way of evaluating ulrasound images and imaging systems than methods that rely solely on human observers. It may also be applicable to other types (i.e. other than ultrasound) of imaging systems which produce noisy images. The relevance of the CO method when compared to human observer two-alternative-forced-choice (2AFC) readings of the same data by showing a high correlation between the CO detectability results and those of human observers for the same set of images. The method is (1) quantitative, (2) reproducible, (3) absolute, (4) takes into account, and can calculate the value of TPF and FPF for each target, for the given system, and (5) speeds up the evaluation of an image or imaging system (compared to using human observers), given the right conditions and equipment.

Three-dimensional simulations of multidetector point-focusing SPECT imaging.

We have applied an efficient algorithm for mathematically simulating the three-dimensional (3-D) response of a SPECT imaging system with a depth-dependent 3-D point spread function (3-DPSF). The input object whose reconstructed image is to be simulated is restricted to a binary map; more complex objects may be treated as linear combinations of binary maps. The 3-D convolution reduces to a sequence of additions of a 3-D line spread function (3-DLSF), appropriately translated, to the 3-D response. We have simulated the projection data from a multidetector SPECT system with point-focusing collimators. The simulated projection data were then reconstructed using the manufacturer's software. The objects simulated included simple geometrical solids such as spheres and cylinders, as well as the distribution of muscarinic cholinergic receptors in a realistic brain slice. The results of these simulations indicate the existence of significant qualitative and quantitative artifacts in reconstructed human brain images.

3-D computer simulations of resolution effects of multidetector point focusing SPECT imaging.

The authors present an efficient algorithm and the results of its application in simulating the three-dimensional (3-D) projection data resulting from a 3-D distribution of radioactivity. The algorithm was applied to a series of geometrical mathematical phantoms and to a realistic mathematical brain phantom. The authors simulated the projection data from a multidetector single-photon emission computed tomography (SPECT) system with point focusing collimators. The simulated projection data were then reconstructed using the manufacturer's software. The objects simulated included simple geometrical solids such as spheres and sheets, as well as the distribution of muscarinic cholinergic receptors in a realistic brain slice. Spheres were chosen as a model for brain structures such as caudate nucleus, thalamus, and cerebellum; sheets were selected as representing lateral cortical gray matter regions. The results of these simulations indicate the existence of significant qualitative and quantitative artifacts in reconstructed human brain images.

A methodology for modeling the distributions of medical images and their stochastic properties.

The probabilistic distribution properties of a set of medical images are studied. It is shown that the generalized Gaussian function provides a good approximation to the distribution of AP chest radiographs. Based on this result and a goodness-of-fit test, a generalized Gaussian autoregressive model (GGAR) is proposed. Its properties and limitations are also discussed. It is expected that the GGAR model will be useful in describing the stochastic characteristics of some classes of medical images and in image data compression and other applications.

A clinical evaluation of contrast-detail analysis for ultrasound images.

We report on the reproducibility of human observers' vanishing detection thresholds for visual targets in contrast-detail (C/D) analysis of ultrasound B-mode images. The images used in this study contain visual targets which are circular cross sections of constant-contrast conical structures in the C/D phantom. The vanishing threshold diameters for these targets vary as a function of the perceived size of the imaged target, target-to-background contrast, image noise content, and reproducibility of the decision levels of human observers for repeated observations. Our study indicates that the determination of absolute vanishing threshold diameter values for several targets of different contrast by human observers yields a high degree of error that is not predicted by existing theoretical assumptions based on a static threshold detector. We find that systematic error is introduced by the observers during the course of the experiment and that the levels of sensitivity of the observers differ widely at all times, and increase the amount of total observer error. These results suggest that, due to the large total observer error, C/D analysis may be impractical in a clinical environment, unless there is access to a team of observers specifically and extensively trained in this task. We suggest that a computer-based observer may be more reliable for the objective performance of contrast-detail analysis as a method for evaluating ultrasound image quality and comparison of imaging systems.

The entry-exit method of shadow boundary segmentation.

A new method has been developed for interpreting the shadows of arbitrarily shaped surfaces by segmenting and labeling the shadow boundary. The method is based on the fact that a linear projection of any light ray (the ray is assumed to originate at a single, distant source) across a shadow either enters or exits the shadow at its boundary. Hence, junctures of entry and exit segments form vertices that can be found directly for any given direction of illumination and view. Entry-exit vertices that are extremes of the boundary (which is normal to the axis of light) can be identified as junctures of specific profiles of the shadow-making object. These junctures, in turn identify the segments connected to them. The method assumes successful lower level extraction of shadow boundaries. When one object occludes part of another object's shadow, critical junctures occur, but these sometimes are not entry-exit vertices. These hidden junctures create ambiguities that must be dealt with in the context of neighboring segments. Certain a priori knowledge is helpful in this situation. The method may require knowledge of the surface or the object. The entry-exit method also provides a new link between the tasks of shadow boundary extraction and shape inference in the overall process of shadow interpretation. In conjunction with existing methods for the other tasks, the entry-exit method makes it possible to interpret arbitrarily shaped shadows.