Xian Li1, Lu-Ping Wang2, Li-Li Ou3, Xiao-Yan Huang1, Qing-Si Zeng4, Wen-Qi Wu5. 1. The Radiology Department of the First Affiliated Hospital of Guangzhou Medical University, Guangdong, Guangzhou, 51020, People's Republic of China. 2. The Urology Department of the First Affiliated Hospital of Guangzhou Medical University, Guangdong, Guangzhou, 51020, People's Republic of China. 3. The Guangdong Key Laboratory of Urology, the First Affiliated Hospital of Guangzhou Medical University, Minimally invasive Surgery center, Guangdong, Guangzhou, 51020, People's Republic of China. 4. The Radiology Department of the First Affiliated Hospital of Guangzhou Medical University, Guangdong, Guangzhou, 51020, People's Republic of China. zengqingsi2020@163.com. 5. The Urology Department of the First Affiliated Hospital of Guangzhou Medical University, Guangdong, Guangzhou, 51020, People's Republic of China. wwqwml@163.com.
Abstract
PURPOSE: To analyze various compositions of urinary stones using revolution spectral CT (rapid kV switching dual-energy CT) in vivo. METHODS: 202 patients with urinary stones underwent spectral CT before surgery. Zeff peak, overall scope and CT values were detected. Moreover, water/iodine attenuating material images were obtained. Removed stones were subjected to infrared spectroscopy after surgery. The results of infrared spectroscopy were compared with CT. RESULTS: 28 stones (14.08%) with single composition, 165 stones with two mixed compositions (81.68%), and 9 stones with three mixed compositions (4.46%) were observed. When Zeff peaks of stones with single/mixed compositions were summarized together, 146 peaks of calcium oxalate monohydrate, 119 peaks of calcium oxalate dihydrate, 55 peaks of carbapatite, 38 peaks of urate, 16 peaks of struvite, and 11 peaks of brushite were totally observed. 93.8% of calcium oxalate monohydrate had Zeff peaks between 13.3 and 14.0. 91.6% of calcium oxalate dihydrate had peaks between 12.0 and 13.3. For carbapatite, 90.9% of stones had peaks from 14.0 to 15.0. A total of 94.8% of urate had peaks between 7.0 and 11.0. 93.8% of struvite had peaks between 11.0 and 13.0, and 90.9% of brushite had peaks between 12.0 and 14.0. Moreover, densities of urate, struvite and brushite were low density in iodine-based images and high-density in water-based images. CONCLUSION: The in-vivo analysis of spectral CT in urinary stone revealed characteristics of different compositions, especially mixed compositions. An in-vivo predictive model may be constructed to distinguish stone compositions.
PURPOSE: To analyze various compositions of urinary stones using revolution spectral CT (rapid kV switching dual-energy CT) in vivo. METHODS: 202 patients with urinary stones underwent spectral CT before surgery. Zeff peak, overall scope and CT values were detected. Moreover, water/iodine attenuating material images were obtained. Removed stones were subjected to infrared spectroscopy after surgery. The results of infrared spectroscopy were compared with CT. RESULTS: 28 stones (14.08%) with single composition, 165 stones with two mixed compositions (81.68%), and 9 stones with three mixed compositions (4.46%) were observed. When Zeff peaks of stones with single/mixed compositions were summarized together, 146 peaks of calcium oxalate monohydrate, 119 peaks of calcium oxalate dihydrate, 55 peaks of carbapatite, 38 peaks of urate, 16 peaks of struvite, and 11 peaks of brushite were totally observed. 93.8% of calcium oxalate monohydrate had Zeff peaks between 13.3 and 14.0. 91.6% of calcium oxalate dihydrate had peaks between 12.0 and 13.3. For carbapatite, 90.9% of stones had peaks from 14.0 to 15.0. A total of 94.8% of urate had peaks between 7.0 and 11.0. 93.8% of struvite had peaks between 11.0 and 13.0, and 90.9% of brushite had peaks between 12.0 and 14.0. Moreover, densities of urate, struvite and brushite were low density in iodine-based images and high-density in water-based images. CONCLUSION: The in-vivo analysis of spectral CT in urinary stone revealed characteristics of different compositions, especially mixed compositions. An in-vivo predictive model may be constructed to distinguish stone compositions.
Authors: Tim Nestler; Kai Nestler; Andreas Neisius; Hendrik Isbarn; Christopher Netsch; Stephan Waldeck; Hans U Schmelz; Christian Ruf Journal: World J Urol Date: 2018-08-03 Impact factor: 4.226