S Molloi1, A Van Drie, F Wang. 1. Department of Radiological Sciences, Medical Sciences I, B-140, University of California, Irvine, CA 92697, USA. symolloi@uci.edu
Abstract
PURPOSE: To investigate the feasibility of an automated implementation of a beam equalization technique and to evaluate the experimental performance of the prototype system. MATERIALS AND METHODS: X-ray beam equalization involved the process of low-dose image acquisition, attenuator thickness calculation, mask generation, mask positioning, equalized image acquisition, and mask reshaping. The entire equalization process was performed in approximately 7 seconds. The equalized images were assessed both qualitatively and quantitatively by using a humanoid phantom and a swine animal model. The general image quality was assessed for the ability to visualize arterial branches and other anatomic structures. The level of equalization was quantitatively assessed by segmenting the images into an 8 x 8 matrix of square regions. RESULTS: The ratio of the root-mean-squared variance for the equalized and unequalized images from humanoid phantom and swine animal studies was 0.49 and 0.59, respectively. Furthermore, qualitative assessment of the images showed substantial improvement in image quality and visualization of arterial branches after beam equalization in phantom and animal studies. CONCLUSION: Automated area beam equalization is feasible and improves image quality in previously underpenetrated regions of the image.
PURPOSE: To investigate the feasibility of an automated implementation of a beam equalization technique and to evaluate the experimental performance of the prototype system. MATERIALS AND METHODS: X-ray beam equalization involved the process of low-dose image acquisition, attenuator thickness calculation, mask generation, mask positioning, equalized image acquisition, and mask reshaping. The entire equalization process was performed in approximately 7 seconds. The equalized images were assessed both qualitatively and quantitatively by using a humanoid phantom and a swine animal model. The general image quality was assessed for the ability to visualize arterial branches and other anatomic structures. The level of equalization was quantitatively assessed by segmenting the images into an 8 x 8 matrix of square regions. RESULTS: The ratio of the root-mean-squared variance for the equalized and unequalized images from humanoid phantom and swine animal studies was 0.49 and 0.59, respectively. Furthermore, qualitative assessment of the images showed substantial improvement in image quality and visualization of arterial branches after beam equalization in phantom and animal studies. CONCLUSION: Automated area beam equalization is feasible and improves image quality in previously underpenetrated regions of the image.