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Patient-specific Region-of-Interest Fluoroscopy Device for X-ray Dose Reduction¹

¹ From the Department of Radiological Sciences, University of California, Medical Sciences I, B-140, Irvine, CA 92697. Received November 12, 2001; revision requested January 2, 2002; final revision received May 2; accepted May 22. Supported in part by grant R01 HL57338 awarded by the National Heart, Lung, and Blood Institute and the United States Department of Health and Human Services. Address correspondence to S.M. (e-mail: symolloi@uci.edu).

View larger version (23K): [in a new window]   Figure 1. Flowchart of the system used for ROI fluoroscopy shows its three components: the x-ray and image acquisition system, the ROI fluoroscopy system, and the user interface.

View larger version (41K): [in a new window]   Figure 2. Left graph shows an ROI as an operator would draw it. The image is segmented into a 16 x 16 matrix of identical squares. Right graph shows the ROI defined by the operator as translated by the computer to the shape of the filter. The straight line, representing the shape of the filter as created by the computer, approximates the dotted line, which represents the ROI hand-drawn by the operator. Each square in the graph corresponds to a filter-forming piston.

View larger version (146K): [in a new window]   Figure 3. (a) A compensated anteroposterior radiograph without transition zone correction obtained in the "midthorax" of a humanoid chest phantom. Artifacts (arrows) are visible at the edge of the ROI. (b) A diagrammatic view of an ROI-periphery transition zone in an image demonstrates the running average interpolation algorithm. x Is the pixel for which the value is to be corrected; the line connecting a and b is the profile across the edge of the ROI; Lb, Lx, and La are the three profiles parallel to the edge of the ROI; and T is a constant number of pixels that can be set by the operator.

View larger version (18K): [in a new window]   Figure 3. (a) A compensated anteroposterior radiograph without transition zone correction obtained in the "midthorax" of a humanoid chest phantom. Artifacts (arrows) are visible at the edge of the ROI. (b) A diagrammatic view of an ROI-periphery transition zone in an image demonstrates the running average interpolation algorithm. x Is the pixel for which the value is to be corrected; the line connecting a and b is the profile across the edge of the ROI; Lb, Lx, and La are the three profiles parallel to the edge of the ROI; and T is a constant number of pixels that can be set by the operator.

View larger version (35K): [in a new window]   Figure 4. A diagrammatic view of the image compensation process shows the different masks and operations applied to the raw ROI image. The operations shown are convolution (), subtraction (-), and multiplication (x). The final compensation mask (MCR) is recalculated every few seconds to account for patient motion. Mb is a convoluted mask with value b in the periphery and zero in the ROI. MTZ is the mask that corrects for the artifacts in the transition zone.

View larger version (23K): [in a new window]   Figure 5. Scatterplot shows relationship between the average gray level inside the ROI and that in the periphery. The data points represent the measurements obtained with different steps of the step phantom. Linear regression (r = 1.0, P < .001), which is represented by the solid line, yielded the CR and the intercept b.

View larger version (39K): [in a new window]   Figure 6a. Scatterplots show the results of plotting (a) CR and (b) the compensation intercept b against x-ray tube potential for the 5.6-mm () and 4.0-mm () filters. The CRs were fitted by using a binomial function. Linear regression analyses were applied to the compensation intercept data. The solid and dashed lines represent the fitted results. These scatterplots show that use of a quadratic regression and a linear regression can provide good prediction of the CR (r = 0.99, P < .001) and the intercept b (r = 0.98, P < .001), respectively.

View larger version (42K): [in a new window]   Figure 6b. Scatterplots show the results of plotting (a) CR and (b) the compensation intercept b against x-ray tube potential for the 5.6-mm () and 4.0-mm () filters. The CRs were fitted by using a binomial function. Linear regression analyses were applied to the compensation intercept data. The solid and dashed lines represent the fitted results. These scatterplots show that use of a quadratic regression and a linear regression can provide good prediction of the CR (r = 0.99, P < .001) and the intercept b (r = 0.98, P < .001), respectively.

View larger version (31K): [in a new window]   Figure 7. Scatterplot shows results of linear regression analyses of beam stop size versus scatter glare fraction. For different types of ROIs, the linear correlation coefficient r varies from 0.98 to 0.99, and P varies from .10 to .12. The scatter glare fraction was measured without an ROI filter () and with ROI filters of 4.0 mm () and 5.6 mm () in peripheral thickness. The extrapolation of the graph to the beam stop size of zero indicates that the contributions of scatter and veiling glare to the intensity of the image were 35%, 25%, and 21% with no ROI filter, with the 4.0-mm ROI filter, and with the 5.6-mm ROI filter, respectively.

View larger version (134K): [in a new window]   Figure 8a. Images of a humanoid chest phantom acquired (a) without and (b) with a 5.6-mm-thick filter in the beam. (b) The original ROI fluoroscopy image shows reduced gray level in the periphery and improved contrast inside the ROI. (c) The same image as in b after application of our compensation technique. With this technique, the peripheral gray level was restored and the image quality inside the ROI was preserved. Although there is a higher level of noise in the periphery because of reduced x-ray exposure, the anatomic structures shown in the periphery are still sufficiently recognizable. (d) Image of the compensation mask itself.

View larger version (86K): [in a new window]   Figure 8b. Images of a humanoid chest phantom acquired (a) without and (b) with a 5.6-mm-thick filter in the beam. (b) The original ROI fluoroscopy image shows reduced gray level in the periphery and improved contrast inside the ROI. (c) The same image as in b after application of our compensation technique. With this technique, the peripheral gray level was restored and the image quality inside the ROI was preserved. Although there is a higher level of noise in the periphery because of reduced x-ray exposure, the anatomic structures shown in the periphery are still sufficiently recognizable. (d) Image of the compensation mask itself.

View larger version (145K): [in a new window]   Figure 8c. Images of a humanoid chest phantom acquired (a) without and (b) with a 5.6-mm-thick filter in the beam. (b) The original ROI fluoroscopy image shows reduced gray level in the periphery and improved contrast inside the ROI. (c) The same image as in b after application of our compensation technique. With this technique, the peripheral gray level was restored and the image quality inside the ROI was preserved. Although there is a higher level of noise in the periphery because of reduced x-ray exposure, the anatomic structures shown in the periphery are still sufficiently recognizable. (d) Image of the compensation mask itself.

View larger version (25K): [in a new window]   Figure 8d. Images of a humanoid chest phantom acquired (a) without and (b) with a 5.6-mm-thick filter in the beam. (b) The original ROI fluoroscopy image shows reduced gray level in the periphery and improved contrast inside the ROI. (c) The same image as in b after application of our compensation technique. With this technique, the peripheral gray level was restored and the image quality inside the ROI was preserved. Although there is a higher level of noise in the periphery because of reduced x-ray exposure, the anatomic structures shown in the periphery are still sufficiently recognizable. (d) Image of the compensation mask itself.