Effect of various environments and computed tomography scanning parameters on renal volume measurements in vitro: A phantom study

Liu,Wangyan; Zhu,Yinsu; Tang,Lijun; Zhu,Xiaomei; Xu,Yi; Yang,Guanyu

doi:10.3892/etm.2016.3414

Effect of various environments and computed tomography scanning parameters on renal volume measurements in vitro: A phantom study

Authors:
- Wangyan Liu
- Yinsu Zhu
- Lijun Tang
- Xiaomei Zhu
- Yi Xu
- Guanyu Yang
View Affiliations

Affiliations: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China, Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, P.R. China
Published online on: June 1, 2016 https://doi.org/10.3892/etm.2016.3414
Pages: 753-758

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Kidney volume is an important parameter in clinical practice, and accurate assessment of kidney volume is vital. The aim of the present study was to evaluate the effect of various environments, tube voltages, tube currents and slice thicknesses on the accuracy of a novel segmentation software in determining renal volume on computed tomography (CT) images. The volumes of potatoes and porcine kidneys were measured on CT images and compared with the actual volumes, which were determined by a water displacement method. CT scans were performed under various situations, including different environments (air or oil); tube voltages/tube currents (80 kVp/200 mAs, 120 kVp/200 mAs, 120 kVp/100 mAs); and reconstructed slice thicknesses (0.75 or 1.5 mm). Percentage errors (PEs) relative to the reference standards were calculated. In addition, attenuation and image noise under different CT scanning parameters were compared. Student's t‑test was also used to analyze the effect of various conditions on image quality and volume measurements. The results indicated that the volumes measured in oil were closer to the actual volumes (P<0.05). Furthermore, attenuation and image noise significantly increased when using a tube voltage of 80 kVp, while the mean PEs between 120 and 80 kVp voltages were not significantly different. The mean PEs were greater when using a tube current of 100 mAs compared with a current of 200 mAs (P<0.05). In addition, the volumes measured on 1.5 mm slice thickness were closer to the actual volumes (P<0.05). In conclusion, different environments, tube currents and slice thicknesses may affect the volume measurements. In the present study, the most accurate volume measurements were obtained at 120 kVp/200 mAs and a slice thickness of 1.5 mm.

View Figures

View References

1	Yang M, Gao F, Liu H, Pang H, Zuo YP and Yong T: Prospectively estimating the recoverability of renal function after relief of unilateral urinary obstruction by measurement of renal parenchymal volume. Acad Radiol. 20:401–406. 2013. View Article : Google Scholar : PubMed/NCBI
2	Gupta S, Singh AH, Shabbir A, Hahn PF, Harris G and Sahani D: Assessing renal parenchymal volume on unenhanced CT as a marker for predicting renal function in patients with chronic kidney disease. Acad Radiol. 19:654–660. 2012. View Article : Google Scholar : PubMed/NCBI
3	Yano M, Lin MF, Hoffman KA, Vijayan A, Pilgram TK and Narra VR: Renal measurements on CT angiograms: Correlation with graft function at living donor renal transplantation. Radiology. 265:151–157. 2012. View Article : Google Scholar : PubMed/NCBI
4	Sharma N, O'Hara J, Novick AC, Lieber M, Remer EM and Herts BR: Correlation between loss of renal function and loss of renal volume after partial nephrectomy for tumor in a solitary kidney. J Urol. 179:1284–1288. 2008. View Article : Google Scholar : PubMed/NCBI
5	Sommer G, Bouley D, Frisoli J, Pierce L, Sandner-Porkristl D and Fahrig R: Determination of 3-dimensional zonal renal volumes using contrast-enhanced computed tomography. J Comput Assist Tomogr. 31:209–213. 2007. View Article : Google Scholar : PubMed/NCBI
6	Bakker J, Olree M, Kaatee R, de Lange EE, Moons KG, Beutler JJ and Beek FJ: Renal volume measurements: Accuracy and repeatability of US compared with that of MR imaging. Radiology. 211:623–628. 1999. View Article : Google Scholar : PubMed/NCBI
7	Partik BL, Stadler A, Schamp S, Koller A, Voracek M, Heinz G and Helbich TH: 3D versus 2D ultrasound: Accuracy of volume measurement in human cadaver kidneys. Invest Radiol. 37:489–495. 2002. View Article : Google Scholar : PubMed/NCBI
8	Gandy SJ, Armoogum K, Nicholas RS, McLeay TB and Houston JG: A clinical MRI investigation of the relationship between kidney volume measurements and renal function in patients with renovascular disease. Br J Radiol. 80:12–20. 2007. View Article : Google Scholar : PubMed/NCBI
9	Zöllner FG, Svarstad E, Munthe-Kaas AZ, Schad LR, Lundervold A and Rørvik J: Assessment of kidney volumes from MRI: Acquisition and segmentation techniques. AJR Am J Roentgenol. 199:1060–1069. 2012. View Article : Google Scholar : PubMed/NCBI
10	Rusinek H, Boykov Y, Kaur M, Wong S, Bokacheva L, Sajous JB, Huang AJ, Heller S and Lee VS: Performance of an automated segmentation algorithm for 3D MR renography. Magn Reson Med. 57:1159–1167. 2007. View Article : Google Scholar : PubMed/NCBI
11	Summerlin AL, Lockhart ME, Strang AM, Kolettis PN, Fineberg NS and Smith JK: Determination of split renal function by 3D reconstruction of CT angiograms: A comparison with gamma camera renography. AJR Am J Roentgenol. 191:1552–1558. 2008. View Article : Google Scholar : PubMed/NCBI
12	Widjaja E, Oxtoby JW, Hale TL, Jones PW, Harden PN and McCall IW: Ultrasound measured renal length versus low dose CT volume in predicting single kidney glomerular filtration rate. Br J Radiol. 77:759–764. 2004. View Article : Google Scholar : PubMed/NCBI
13	Lin DT, Lei CC and Hung SW: Computer-aided kidney segmentation on abdominal CT images. IEEE Trans Inf Technol Biomed. 10:59–65. 2006. View Article : Google Scholar : PubMed/NCBI
14	Rao M, Stough J, Chi YY, Muller K, Tracton G, Pizer SM and Chaney EL: Comparison of human and automatic segmentations of kidneys from CT images. Int J Radiat Oncol Biol Phys. 61:954–960. 2005. View Article : Google Scholar : PubMed/NCBI
15	Brown MS, Feng WC, Hall TR, Mcnitt-Gray MF and Churchill BM: Knowledge-based segmentation of pediatric kidneys in CT for measurement of parenchymal volume. J Comput Assist Tomogr. 25:639–648. 2001. View Article : Google Scholar : PubMed/NCBI
16	Ko JP, Rusinek H, Jacobs EL, Babb JS, Betke M, McGuinness G and Naidich DP: Small pulmonary nodules: Volume measurement at chest CT-phantom study. Radiology. 228:864–870. 2003. View Article : Google Scholar : PubMed/NCBI
17	Way TW, Chan HP, Goodsitt MM, Sahiner B, Hadjiiski LM, Zhou C and Chughtai A: Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: A phantom study. Phys Med Biol. 53:1295–1312. 2008. View Article : Google Scholar : PubMed/NCBI
18	Larici AR, Storto ML, Torge M, Mereu M, Molinari F, Maggi F and Bonomo L: Automated volumetry of pulmonary nodules on multidetector CT: Influence of slice thickness, reconstruction algorithm and tube current. Preliminary results. Radiol Med. 113:29–42. 2008.(In English, Italian). View Article : Google Scholar : PubMed/NCBI
19	Muto NS, Kamishima T, Harris AA, Kato F, Onodera Y, Terae S and Shirato H: Renal cortical volume measured using automatic contouring software for computed tomography and its relationship with BMI, age and renal function. Eur J Radiol. 78:151–156. 2011. View Article : Google Scholar : PubMed/NCBI
20	Heckel F, Meine H, Moltz JH, Kuhnigk JM, Heverhagen JT, Kiessling A, Buerke B and Hahn HK: Segmentation-based partial volume correction for volume estimation of solid lesions in CT. IEEE Trans Med Imaging. 33:462–480. 2014. View Article : Google Scholar : PubMed/NCBI
21	Kass M, Witkin A and Terzopoulos D: Snakes: Active contour models. Int J Comput Vision. 1:321–331. 1988. View Article : Google Scholar
22	Heckel F, Konrad O and Peitgen HO: Fast and smooth interactive segmentation of medical images using variational interpolation. Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine. VCBM. (Leipzig). 9–16. 2010.
23	Chul Kim J: Animal study of renal volume measurement on abdominal CT using digital image processing; Preliminary report. Clin Imaging. 28:135–137. 2004. View Article : Google Scholar : PubMed/NCBI
24	Cai W, Holalkere NS, Harris G, Sahani D and Yoshida H: Dynamic-threshold level set method for volumetry of porcine kidney in CT images in vivo and ex vivo assessment of the accuracy of volume measurement. Acad Radiol. 14:890–896. 2007. View Article : Google Scholar : PubMed/NCBI
25	Bevelacqua JJ: Practical and effective ALARA. Health Phys 98 Suppl. 2:S39–S47. 2010. View Article : Google Scholar
26	Dong F, Davros W, Pozzuto J and Reid J: Optimization of kilovoltage and tube current-exposure time product based on abdominal circumference: An oval phantom study for pediatric abdominal CT. AJR Am J Roentgenol. 199:670–676. 2012. View Article : Google Scholar : PubMed/NCBI
27	Nakaura T, Awai K, Oda S, Funama Y, Harada K, Uemura S and Yamashita Y: Low-kilovoltage, high-tube-current MDCT of liver in thin adults: Pilot study evaluating radiation dose, image quality and display settings. AJR Am J Roentgenol. 196:1332–1338. 2011. View Article : Google Scholar : PubMed/NCBI
28	Marin D, Nelson RC, Schindera ST, Richard S, Youngblood RS, Yoshizumi TT and Samei E: Low-tube-voltage, high-tube-current multidetector abdominal CT: Improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm-initial clinical experience. Radiology. 254:145–153. 2010. View Article : Google Scholar : PubMed/NCBI
29	Nakaura T, Nakamura S, Maruyama N, Funama Y, Awai K, Harada K, Uemura S and Yamashita Y: Low contrast agent and radiation dose protocol for hepatic dynamic CT of thin adults at 256-detector row CT: Effect of low tube voltage and hybrid iterative reconstruction algorithm on image quality. Radiology. 264:445–454. 2012. View Article : Google Scholar : PubMed/NCBI
30	Coppenrath E, Meindl T, Herzog P, Khalil R, Mueller-Lisse U, Krenn L, Reiser M and Mueller-Lisse UG: Dose reduction in multidetector CT of the urinary tract. Studies in a phantom model. Eur Radiol. 16:1982–1989. 2006. View Article : Google Scholar : PubMed/NCBI