[en] Comparison of two medical images often requires image scaling as a pre-processing 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

[en] 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

[en] The PETRRA positron camera is a large-area (600 mm x 400 mm sensitive area) prototype system that has been developed through a collaboration between the Rutherford Appleton Laboratory and the Institute of Cancer Research/Royal Marsden Hospital. The camera uses novel technology involving the coupling of 10 mm thick barium fluoride scintillating crystals to multi-wire proportional chambers filled with a photosensitive gas. The performance of the camera is reported here and shows that the present system has a 3D spatial resolution of ∼7.5 mm full-width-half-maximum (FWHM), a timing resolution of ∼3.5 ns (FWHM), a total coincidence count-rate performance of at least 80-90 kcps and a randoms-corrected sensitivity of ∼8-10 kcps kBq^-1 ml. For an average concentration of 3 kBq ml^-1 as expected in a patient it is shown that, for the present prototype, ∼20% of the data would be true events. The count-rate performance is presently limited by the obsolete off-camera read-out electronics and computer system and the sensitivity by the use of thin (10 mm thick) crystals. The prototype camera has limited scatter rejection and no intrinsic shielding and is, therefore, susceptible to high levels of scatter and out-of-field activity when imaging patients. All these factors are being addressed to improve the performance of the camera. The large axial field-of-view of 400 mm makes the camera ideally suited to whole-body PET imaging. We present examples of preliminary clinical images taken with the prototype camera. Overall, the results show the potential for this alternative technology justifying further development