Externally triggered gating of nuclear medicine acquisitions: a useful method for partitioning data

Physiological gating in nuclear medicine image acquisition was introduced over 30 years ago to subdivide data from the beating heart into short time frames to minimize motion blurring and permit evaluation of contractile parameters. It has since been widely applied in planar gamma camera imaging, SPECT, positron tomography (PET) and anatomical modalities such as x-ray CT and MRI, mostly for cardiac or respiratory investigations. However, the gating capability of gamma cameras and PET scanners can be employed to produce multiply partitioned, statistically independent projection data that can be used in various ways such as to study the effect of varying total acquired counts or time, or administered radioactivity, on image quality and multiple observations for statistical image analyses. Externally triggered gating essentially provides,something for nothing’ as no data are lost and a ‘non-gated’ data set is easily synthesized post hoc, and there are few reasons for not acquiring the data in this manner (e.g., slightly longer processing time, extra disk space, etc). We present a number of examples where externally triggered gating and partitioning of image data has been useful

Image intensity normalisation by maximising the Siddon line integral in the joint intensity distribution space

This paper presents a novel data-driven method for image intensity normalisation, which is a prerequisite step for any kind of image comparison. The method involves a novel application of the Siddon algorithm that was developed initially for fast reconstruction of tomographic images and is based on a linear normalisation model with either one or two parameters. The latter are estimated by maximising the line integral, computed using the Siddon algorithm, in the 2D joint intensity distribution space of image pairs. The proposed normalisation method, referred to as Siddon Line Integral Maximisation (SLIM), was compared with three other methodologies, namely background ratio (BAR) scaling, linear fitting and proportional scaling, using a large number of synthesised datasets. SLIM was also compared with BAR normalisation when applied to phantom data and two clinical examples. The new method was found to be more accurate and less biased than its counterparts for the range of characteristics selected for the synthesised data. These findings were in agreement with the results from the analysis of the experimental and clinical data. (C) 2009 Elsevier B.V. All rights reserved

Scaling images using their background ratio. An application in statistical comparisons of images

Comparison of two medical images often requires image scaling as a preprocessing step. This is usually done with the scaling-to-the-mean or scaling-to-the-maximum techniques which, under certain circumstances, in quantitative applications may contribute a significant amount of bias. In this paper, we present a simple scaling method which assumes only that the most predominant values in the corresponding images belong to their background structure. The ratio of the two images to be compared is calculated and its frequency histogram is plotted. The scaling factor is given by the position of the peak in this histogram which belongs to the background structure. The method was tested against the traditional scaling-to-the-mean technique on simulated planar gamma-camera images which were compared using pixelwise statistical parametric tests. Both sensitivity and specificity for each condition were measured over a range of different contrasts and sizes of inhomogeneity for the two scaling techniques. The new method was found to preserve sensitivity in all cases while the traditional technique resulted in significant degradation of sensitivity in certain cases

Statistical pixelwise inference models for planar data analysis: an application to gamma-camera uniformity monitoring

In this paper two tests based on statistical models are presented and used to assess, quantify and provide positional information of the existence of bias and/or variations between planar images acquired at different times but under similar conditions. In the first test a linear regression model is fitted to the data in a pixelwise fashion, using three mathematical operators. In the second test a comparison using z-scoring is used based on the assumption that Poisson statistics are valid. For both tests the underlying assumptions are as simple and few as possible. The results are presented as parametric maps of either the three operators or the z-score. The z-score maps can then be thresholded to show the parts of the images which demonstrate change. Three different thresholding methods (naive, adaptive and multiple) are presented: together they cover almost all the needs for separating the signal from the background in the z-score maps. Where the expected size of the signal is known or can be estimated, a spatial correction technique (referred to as the reef correction) can be applied. These tests were applied to flood images used for the quality control of gamma camera uniformity. Simulated data were used to check the validity of the methods. Real data were acquired from four different cameras from two different institutions using a variety of acquisition parameters. The regression model found the bias in all five simulated cases and it also found patterns of unstable regions in real data where visual inspection of the flood images did not show any problems. In comparison the z-map revealed the differences in the simulated images from as low as 1.8 standard deviations from the mean, corresponding to a differential uniformity of 2.2% over the central field of view. In all cases studied, the reef correction increased significantly the sensitivity of the method and in most cases the specificity as well. The two proposed tests can be used either separately or in combination and are capable of showing trends and/or the magnitude of difference between images acquired under similar conditions with high positional and statistical precision. In addition to gamma camera quality control, they could be applied to any pair (or set) of registered planar images to detect subtle changes, e.g. a set of scintigrams or conventional radiographs of a patient before, during and after treatment

Saving costs in cancer patient management through molecular imaging

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A Strategic Approach for Intellectual Capital Management in European Universities. Guidelines for Implementation

Public Sector is one of the least addressed areas of Intellectual Capital (IC) research. Universities represent an interesting area of investigation because they are considered critical players in the knowledge-based society. The book aims to develop a more general, flexible and comprehensive IC management model enlarging the wide spectrum of strategic management approach inside the University settings. The Guidelines for the implementation of IC management Systems have been developed within a series of Mutual Learning Workship involving participants from across Europe