iaea human health series publications - SEDIM

(a)Systems with ROI capability (may not be available for CR systems)(1) For each image, with the image displayed so that the contrast object is clearly visible (see Fig. 24), place anROI of approximately 80 mm 2 (~10 mm in diameter) over, and entirely contained within, the contrast object.For the aluminium square, an ROI of 45 mm 2 (7.5 mm in diameter) can be used. Record the MPV and labelthis value A; it will be used to calculate the SDNR.(2) In a region outside, but immediately adjacent to, the contrast object, record the MPV and standard deviationwithin an ROI of similar size to that used above as B and C, respectively.(3) Calculate the SDNR 15 as: SDNR = |A–B|/C.(4) For linear systems, plot the values of MPV (B), the variance (C 2 ) and SDNR versus the mAs. Perform a linearfit to the data and obtain the slope, intercept and correlation coefficients (R 2 ). For logarithmic systems, it maybe necessary to plot the MPV and variance against 1/mAs to obtain a straight line.(5) Some manufacturers intentionally add a pixel value offset to their image data. This value (B 0 ) should beobtained from the manufacturer’s technical documentation. Alternatively, the intercept obtained in theprevious step can be used as B 0 .(6) Calculate the value of (B–B 0 )/mAs for all values of the mAs and for the average value of this quantity.(b)CR systems without ROI capability(1) Plot the information using the axes as specified in Table 15, noting the values of the correlation coefficient(R 2 ).TABLE 15. EXPOSURE INDEX VERSUS mAs, BY MANUFACTURERManufacturer X-axis Y-axisFuji, Philips and Konica mAs S# × mAsAgfalog(mAs)mAslog(mAs)SALlogSAL/√ (mAs)PVIlogCarestream log(mAs) EI15Although CR systems are non-linear, the use of non-linearized pixel values will provide an acceptable approximation to theSDNR obtained from linearized data. Linearization is therefore not required.94