Region-specific tritium enrichment, and not differential beta-absorption, is the major cause of 'quenching' in film autoradiography.

Tritium quenching refers to the situation in which estimates of tritium content generated by film autoradiography depend on the chemical composition of the tissue as well as on the concentration of the radioisotope. When analysing thin brain sections, for example, regions rich in lipid content generate reduced optical densities on x-ray film compared with lipid-poor regions even when the total tissue concentration of tritium in those regions is identical. We hypothesize that the dried thickness of regions within sections depends upon the relative concentrations and types of lipid within the regions. Areas low in white matter dry thinner than areas high in white matter, leading to a relative enrichment of tritium in the thinner regions. To test this model, a series of brain pastes were made with different concentrations of grey and white matter and impregnated with equal amounts of tritium. The thickness of dried sections was compared with percentage of white matter and apparent radioactive content as determined by autoradiogram analysis. The results demonstrated that thickness increased, and apparent radioactivity decreased, with higher percentages of white matter. In the second experiment, thickness measurements from dried sections were successfully used to correct the apparent radioisotope content of autoradiograms created from tritium containing white- and grey-matter tissue slices. We conclude that within-section thickness variation is the major physical cause for 'tritium quenching'.

Multimodality multidimensional image analysis of cortical and subcortical plasticity in the rat brain.

In this work, we developed and implemented a multimodality multidimensional imaging system which is capable of generating and displaying anatomical and functional images of selected structures and processes within a vertebrate's central nervous system (CNS). The functional images are generated from [14C]-2-deoxy-D-glucose (2DG) autoradiography whereas the anatomic images are derived from cytochrome oxidase (CO) histochemistry. This multi-modality imaging system has been used to study mechanisms underlying information processing in the rat brain. We have applied this technique to visualize and measure the plasticity (deformation) observed in the rat's whisker system due to neonatal lesioning of selected peripheral sensory organs. Application of this imaging system revealed detailed information about the shape, size, and directionality of selected cortical and subcortical structures. Previous 2-D imaging techniques were unable to deliver such holistic information. Another important issue addressed in this work is related to image registration problems. We developed an image registration technique which employs extrinsic fiduciary marks for alignment and is capable of registering images with subpixel accuracy. It uses the information from all available fiduciary marks to promote alignment of the sections and to avoid propagation of errors across a serial data set.

Three-dimensional reconstruction of activated columns from 2-[14C]deoxy-D-glucose data.

Three-dimensional (3-D) reconstruction of autoradiograms can provide new insights into the functional relationship of neural regions. To reach full potential, however, 3-D reconstruction must be both accurate and efficient. In this paper, we present a novel image matching algorithm that simultaneously aligns a set of serial sections and uses the method to reconstruct whisker barrels from the rat cerebral cortex. We initially compared several alignment techniques and found that our Multi-Set Registration (MSR) algorithm produced superior accuracy. This algorithm is based on a least-squares minimization technique and is able to simultaneously register a set of serial sections with subpixel precision (30-micron accuracy). We applied our new technique to the 3-D reconstruction of a series of autoradiograms. Our objective was to visualize and measure the 3-D metabolic (functional) shape of normal (control) and developmentally altered (plastic) C3 vibrissa columns in the first somatosensory area of the rat cerebral cortex. The plastic C3 metabolic column showed a nearly 450% increase in volume when compared to the control column. In addition, the lesion-altered C3 column-in contrast to the normal C3 column-displayed no central zone of high activity, and patches of higher metabolic activity were scattered throughout the columnar profile. This metabolic activity was not confined to the cylindrical column, but extended tangentially as radiating fingerlike projections toward neighboring barrels.

Numerical solution of the direct scattering problem through the transformed acoustical wave equation.

A transformation is applied to the acoustical wave equation to obtain a new equivalent form that does not contain gradients of the pressure. A new technique, based on the spectral method, is developed for the numerical solution of the direct time domain scattering problem. Modeling techniques for obtaining accurate solutions are discussed and numerical examples are presented.

Computation of the far-field time domain solution of the scattering problem.

This paper presents a new technique for calculating the time domain (transient) far-field scattered pressure. The scattering problem is divided in two steps; the first step evaluates the field distribution inside the scatterer, and the second step generates the far-field scattered pressure by 3-D Radon transform of these data for each time step and summing over time. The algorithm results in considerable saving in CPU time and memory by simplifying the calculation along the path from scatterer to receiver. This technique can also be used in two dimensions.

Fluorescence imaging for machine vision.

Suitable illumination is a crucial aspect in the successful solution of machine vision problems. In this research we used objective image evaluation techniques and found that fluorescence imaging is superior to conventional illumination for acquiring images of integrated circuit lead bonds. This is an interesting and surprising finding, since there was no a priori reason to expect that any part of the bond would contain fluorescent components. Consequently, fluorescence imaging should be considered as an option in designing machine vision systems, especially if conventional illumination systems do not produce images of adequate quality. In this research we discovered a novel and effective method for threshold selection.

Recognition of regions in brain sections.

This paper addresses the problem of region identification in sequential brain sections and presents a recognition system that finds and tracks region boundaries in those sections. The characteristics of the areas of interest are unique in one sense because they are not stationary. Some regions are hardly discernible. In others, parts of the boundary are missing or so completely blurred that parts of the background may be considered as an extension of the region itself. Moreover, outliers are likely to exist in many cases. Due to the unique properties of brain regions, the emphasis is on robustification and efficiency. The region segmentation problem was expressed as a multi-hypothesis test seeking boundaries that maximize a performance criterion which is general in terms of blur and noise. Boundary candidates are restricted to an adaptive search area around a reference boundary which is usually the outcome of the algorithm from the previous section. The search for the maximum criterion uses a fast first order dynamic programing (DP) procedure, reducing the processing time. Outlier rejection techniques are integrated with the multi-hypothesis test to compensate for both outliers and noise. The result is the reference for the next section. Experimental results on boundary detection are presented. The algorithm is successful in tracing boundaries when the contrast is smaller than the noise power, and when parts of the outlines are missing.

Database management in autoradiography.

Experiments in autoradiography involve the use of radiotracers to achieve a "functional mapping" between structures of the central nervous system and observed behavior in animals. A typical experiment produces 100-300 sections per animal. Computer systems such as DUMAS (Drexel's Unix based iMage Analysis System) are used to analyze these sections. Each section has two images associated with it--an autoradiographic image and a histological image. The latter is used to establish a correlation between anatomical structures and areas on the autoradiogram. User drawn outlines on the histological image are transferred to the autoradiographic image to obtain quantitative measures (such as average gray level). Existing systems do not take advantage of the fact that consecutive sections obtained from a brain are often similar. As a result, much of the effort involved with region outlining is repetitive. Also, the criteria for region selection varies not only across experiments, but also between users. This paper presents an approach to design an integrated database management system to manage both pictorial and quantitative data in autoradiography. Briefly, such a system is used to (a) store sets of reference outlines and images for use during the analysis of sections, (b) provide a bank of information to the user from across experiments, (c) provide an on-line help facility to the novice, and a reference guide to the expert. Based on specific requirements, we chose the relational model for data management. We developed a preliminary version of the database using INFORMIX-ESQL/C, which is a commercially available relational system. We also developed a graphics editor that is actively linked to the database. The results of our efforts have established the feasibility of using a commercially available relational system for autoradiographic data management.

The analytic and functional accuracy of a video densitometry system.

Applications using radiotracers and quantitative film autoradiography are increasing dramatically in the neurosciences. Microcomputer-based image analyzing systems with video input have been developed to provide rapid quantification of autoradiographic images on relatively inexpensive systems. However, there has been some question as to whether such systems can reliably produce high levels of densitometric accuracy, especially when compared to mechanical scanners which are standard in research requiring extreme fidelity of measurement. We report methods and results from tests done to determine the analytical and functional accuracy of the Drexel Unix-based Microcomputer image Analysis System (DUMAS), which is a video densitometric system designed to provide quantitative data from autoradiograms. Analytical accuracy was determined by measuring photometric uniformity, the optical density transfer function, temporal stability, geometric uniformity, and flare. In addition, data are provided on the resolution of the system at several magnifications. Functional accuracy, the accuracy of the estimates of mean isotope concentrations in diverse neural structures, was determined by comparing the results obtained on the DUMAS system with the results from analyzing the same [14C]2-deoxyglucose images with two different Optronics P1000 systems. Our results show that, provided care is taken in the choice of a camera and a light source, the analytic accuracy of videodensitometry is high. Its functional accuracy is also high in that measurements of radioisotope concentrations in diverse neural structures made on the DUMAS system agree closely with the measurements from a properly adjusted Optronics P1000 system. The rapidity and economy of videodensitometry is not, therefore, obtained at the sacrifice of densitometric accuracy. Given adequately tested hardware and provided that suitable checks on instrument calibration and adjustment are made, the errors in autoradiographic quantification due to the image analyzing system itself are minor in comparison to other sources of error, including, as we show, variations in the user's delineation of the boundaries of neural structures.

Noise limitations in X-ray computed tomography.

A lower bound for the statistical accuracy in X-ray computed tomography, which, under certain conditions, is independent of the reconstruction algorithm, is derived. An evaluation of this bound indicates that little improvement is possible over the performance of the convolutional algorithm.