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

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


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.

Introduction

Kidney volume is an important parameter that must be considered for the selection of appropriate management, surgery planning and evaluation of disease status in patients with kidney disease (15). Therefore, precise kidney volumetry has been receiving increasing attention clinically. Typically, kidney size or volume measurement is evaluated using various cross-sectional imaging modalities, including ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) (612). US is an easily used and low-cost technique. Using this method, the renal volume is usually calculated by measuring the three axes of the kidney (length, width and thickness) and then applying these values to an ellipsoid formula (6). However, the shape of the kidney varies greatly in the majority of cases, and as a result, errors in volume calculations by US may occur (6,7). MRI provides outstanding sensitivity for the discrimination of soft tissues that facilitates the segmentation of the kidney (8). However, various factors, such as spatial resolution, artifacts and signal non-uniformities, can affect the accuracy in image segmentation by MRI (9,10). With the advancements in multidetector CT (MDCT), improvements of spatial resolution and time resolution, the ability to assess organ volume using three-dimensional (3D) metrics has become feasible (11,12).

Segmentation of the kidney using CT images is an essential step in renal volumetry. Existing image segmentation methods used in organ volumetry are classified into three categories as follows: The thresholding and adaptive region-growing method (13), deformable model (14), and the knowledge-based method (15).

In the current study, a novel segmentation software based on deformable model was constructed in order to measure kidney volume. However, image segmentation may be affected by a number of factors, including slice thicknesses, tube voltages, tube currents and surrounding structures (1618). The aim of the present study was to investigate the effect of various environments, tube voltages, tube currents and slice thicknesses on the accuracy of renal volume measurements in vitro.

Materials and methods

Phantom

A phantom was used as an in vitro model of the kidney. Considering the dimensions, shape and density of human kidneys, a total of 12 potatoes and 12 porcine kidneys were selected to simulate human kidneys (similar size and shape to human kidneys). A similar technique has been previously described (19). All surrounding tissues were removed as completely as possible without damaging the integrity of the porcine kidneys.

Water displacement method

Each object was immersed in a container filled with water and the displaced water was measured using a graduated cylinder in order to determine the volume of the object. The volume measured by water displacement method was considered as the actual volume. The measurement was repeated three times, and the mean value was determined for each object.

CT image acquisition

Objects were exposed to the air or immersed in maize germ oil (Xiwang Foodstuffs Co., Ltd., Shangdong, China) in order to simulate the abdomen and surrounding fat of kidney. All CT examinations were performed using a first-generation of dual-source CT scanner with 32 rows of detectors (Somatom Definition AS; Siemens, Munich, Germany). The following scan parameters were used: Tube voltage/tube current parameters included 80 kVp/200 mAs, 120 kVp/200 mAs and 120 kVp/100 mAs; beam collimation, 0.6 mm; and rotation time, 0.33 sec. Images were reconstructed at 0.75 mm and 1.5 mm slice thicknesses, and a medium soft-tissue convolution kernel (B30f) was used (20). Volume measurement by CT and by water displacement method was completed within 2 h.

Volume measurement software

The segmentation method was divided into six steps, as illustrated in Fig. 1, which demonstrates the potato segmentation process. Initially, a rectangular region of interest (ROI) was drawn manually to select the object (potato or porcine kidney) in the 3D CT dataset (CT axial images; Fig. 1A and B). A seed point, as shown by an asterisk in Fig. 1C, was defined in the object in order to initialize a 3D region growing. The seed point served as a trigger point. When the operator entered a seed point in the ROI, the measurement process was initialized. Region growing starts from the seed point inside the ROI and continues until no more pixels satisfy the criteria (similar attenuation) for inclusion in that region. A fixed low threshold (20HU) was applied to extract the object as both potato and porcine kidney CT values are >20HU, regardless of the various situations used during the CT scan in the present study. Next, morphological operators were performed in order to fill the small holes in the extracted regions in the 2D image. This processing was performed in the 2D axial and sagittal slices consecutively (Fig. 1D). An initial binary 3D mask (red regions shown in Fig. 1D) of the object was obtained in this step. Subsequently, a deformable contour method (also known as the ‘Snake’ model) (21) was performed in each 2D axial slice of the 3D mask image obtained at the previous step. A stack of contours were generated to depict the shape of the object, as shown in Fig. 1E. Once the contours were checked, a 3D region representing the shape of the object was reconstructed by interpolating an implicit surface (22) based on the extracted contours (Fig. 1F and G). The volume of the 3D region (determined using the software tools used in reconstruction) was considered to be the volume of the object.

Image quality analysis

Measurements of the objects' CT attenuation and image noise were performed by a single radiologist with 3 years of experience in abdominal CT scanning. The CT attenuation of the potatoes and porcine renal parenchyma were measured in a circular ROI of ~3 cm2. The CT attenuation of oil was also measured in a circular ROI of ~3 cm2. Image noise was defined as the standard deviation of CT attenuation of the objects. Contrast noise rate was calculated with the formula: (CT attenuation of kidney - CT attenuation of oil)/image noise. In order to minimize the bias from a single measurement, the mean of three measurements of each ROI was calculated.

Statistical analysis

Percentage error (PE) was used to evaluate the measurement accuracy. The PE and absolute PE were calculated as follows: PE = (VCT - VWD) / VWD ×100%, where VCT is the object volume measured on the CT scan and VWD is the object volume measured by the water displacement method. Mean PE values were calculated for the 24 objects under various technique parameters. To assess the difference of the mean PE values and image quality under various situations, Student's t-test was used. The results were considered as statistically significant when the P-value was <0.05. Data were analyzed using SPSS version 13.0 software (SPSS, Inc., Chicago, IL, USA).

Results

PE analysis

Segmentation and volume calculation were successfully performed for all objects. As determined by the water displacement method, the actual volumes of the 12 potatoes ranged between 124 and 420 ml, while the actual volumes of the 12 porcine kidneys ranged between 88 and 163 ml.

The comparison of the mean PEs between the air and oil CT measurements is listed in Table I. Volumes measured in the oil were closer to the actual volumes compared with those measured in the air, regardless of the various slice thicknesses and scan parameters used (P<0.001 for all). The minimal mean PE of objects on volumetric measurements was found to be 0.45±1.92% in the oil, obtained when 120 kVp tube voltage, 200 mAs tube current and 1.5 mm slice thickness were used.

Table I.

Comparison of the mean PEs between measurements performed in air and oil.

Table I.

Comparison of the mean PEs between measurements performed in air and oil.

Slice thicknessScan doseaPEs (air), %PEs (oil), %P-value
0.75 mm80 kVp/200 mAs −3.22±2.23 0.88±1.83<0.001
120 kVp/200 mAs −3.33±2.33 0.80±1.92<0.001
120 kVp/100 mAs −3.15±2.33 0.99±1.85<0.001
1.5 mm80 kVp/200 mAs −3.72±2.31 0.50±1.88<0.001
120 kVp/200 mAs −3.85±2.39 0.45±1.92<0.001
120 kVp/100 mAs −3.69±2.42 0.60±1.91<0.001

a Scan dose indicates the tube voltage/tube current used in the experiments. Data are displayed as the mean ± standard deviation. PE, percentage error.

The various technique parameters were then compared for measurements performed in the oil. Further comparison of the mean PEs of object volumetric measurements in the air was not performed due to the higher accuracy of measurements obtained in the oil environment, according to the aforementioned results. Table II shows the mean PEs of objects on volumetric measurement obtained at different tube voltages. The results indicated that no statistically significant differences in PEs between the 120 and 80 kVp tube voltages, regardless of the different slice thickness (P>0.05 for both). Therefore, in this study, different tube voltages have very little influence on volume measurements. Similarly, Table III shows the comparison of the mean PEs between different tube currents. At slice thicknesses of 0.75 and 1.5 mm, the PEs were significantly larger when a current of 100 mAs was used, compared with the use of a 200 mAs current (P<0.001). Hence, it was concluded that lower tube current would reduce the accuracy of volume measurements in this study. In addition, the mean PEs of the two slice thicknesses were compared at different tube voltages and currents. Table IV shows that the volumes measured using a slice thickness of 1.5 mm were closer to the actual volumes, in comparison with measurements at 0.75 mm thickness (P<0.05). In this study, a volume measured at 1.5 mm thickness is more accurate than a volume measured at 0.75 mm thickness, regardless of what tube voltages/currents are applied.

Table II.

Comparison of the mean PEs between tube voltages of 120 and 80 kVp at different slice thickness.

Table II.

Comparison of the mean PEs between tube voltages of 120 and 80 kVp at different slice thickness.

PE at each tube voltage, %

Slice thickness120 kVp80 kVpP-value
0.75 mm 0.80±1.92 0.88±1.830.240
1.5 mm 0.45±1.92 0.50±1.880.421

[i] Based on computed tomography measurements performed in oil. Data are displayed as the mean ± standard deviation. PE, percentage error.

Table III.

Comparison of the mean PEs between tube currents of 200 and 100 mAs at different slice thickness.

Table III.

Comparison of the mean PEs between tube currents of 200 and 100 mAs at different slice thickness.

PE at each tube current, %

Slice thickness200 mAs100 mAsP-value
0.75 mm 0.80±1.92 0.99±1.85   0.002
1.5 mm 0.45±1.92 0.60±1.91<0.001

[i] Based on computed tomography measurements performed in oil. Data are displayed as the mean ± standard deviation. PE, percentage error.

Table IV.

Comparison of the mean PEs between slice thickness of 0.75 and 1.5 mm at different voltages/currents.

Table IV.

Comparison of the mean PEs between slice thickness of 0.75 and 1.5 mm at different voltages/currents.

PE at each thickness, %

Tube voltage/tube current0.75 mm1.5 mmP-value
80 kVp/200 mAs 0.88±1.83 0.50±1.88<0.001
120 kVp/200 mAs 0.80±1.92 0.45±1.92<0.001
120 kVp/100 mAs 0.99±1.85 0.60±1.91<0.001

[i] Based on computed tomography measurements performed in oil. Data are displayed as the mean ± standard deviation. PE, percentage error.

Image quality analysis

Results regarding image quality are listed in Tables V and VI, including the CT attenuation, image noise and contrast noise rate. Mean attenuations for all objects were higher at a voltage of 80 kVp compared with 120 kVp, when slice thickness was 0.75 mm (70.59 HU vs. 66.81 HU, respectively; P<0.001) or 1.5 mm (69.67 HU vs. 65.59 HU, respectively; P<0.001). In addition, the mean image noise was increased at a tube voltage of 80 kVp compared with that at 120 kVp, at both slice thicknesses (P<0.001). Regarding the comparison between measurements at a tube current of 200 and 100 mAs, the mean attenuation was almost the same between the two currents (P>0.05), whereas the image noise was slightly increased at lower tube current (P<0.001 and P<0.007 at slice thicknesses of 0.75 and 1.5 mm, respectively). At the same slice thickness, tube voltages had no effect on contrast noise rate (P>0.05), whereas tube currents had a significant influence on contrast noise rate (P<0.05) in this study. Therefore, higher tube currents produce higher contrast noise rates.

Table V.

Comparison of CT attenuation, image noise and CNR between tube voltages of 120 and 80 kVp.

Table V.

Comparison of CT attenuation, image noise and CNR between tube voltages of 120 and 80 kVp.

Slice thickness120 kVp80 kVpP-value
0.75 mm
  CT attenuation (HU) 66.81±17.25 70.59±16.34<0.001
  Image noise 8.83±3.12 10.70±2.69<0.001
  CNR 23.89±6.82 22.61±4.92   0.070
1.5 mm
  CT attenuation (HU) 65.59±16.91 69.67±15.91<0.001
  Image noise 8.18±3.30 9.28±2.94<0.001
  CNR 26.60±9.01 25.81±6.27   0.359

[i] CNR, contrast noise rate; HU, Hounsfield unit. Data are displayed as the mean ± standard deviation.

Table VI.

Comparison of CT attenuation, image noise and CNR between tube currents of 200 and 100 mAs.

Table VI.

Comparison of CT attenuation, image noise and CNR between tube currents of 200 and 100 mAs.

Slice thickness200 mAs100 mAsP-value
0.75 mm
  CT attenuation (HU) 66.81±17.25 66.90±17.02   0.730
  Image noise 8.83±3.12 9.67±2.93<0.001
  CNR 23.90±6.82 21.22±5.09<0.001
1.5 mm
  CT attenuation (HU) 65.59±16.91 66.00±16.78   0.133
  Image noise 8.18±3.30 8.66±3.04   0.007
  CNR 26.60±9.01 24.13±6.36   0.009

[i] CNR, contrast noise rate; HU, Hounsfield unit. Data are displayed as the mean ± standard deviation.

Discussion

The segmentation software used in the present study demonstrated good performance on measuring the object volume in vitro. The mean PEs of objects on volumetric measurements were <1% when the surrounding environment was oil. Brown et al (15) previously reported a mean difference of 1.3% between the in vivo CT-based volume of porcine kidneys and the in vitro volume of water displacement. In addition, Chul Kim et al (23) performed 3-mm thickness CT scanning in 2 porcine kidneys, and reported a 2.9% difference between the volume measurement by the in vivo CT scan and the in vitro volume measured by water displacement. Cai et al (24) performed ex vivo CT scanning of 8 pig kidneys, and the mean PE of volume measurement using the dynamic-threshold level set method was 3.38±2.51%, when compared with reference standards determined by fluid displacement. Typically, higher PEs may be observed in vivo in comparison with those in vitro due to inhomogeneity of the surrounding tissues. Similar to the PEs obtained by the aforementioned previous studies, the mean PEs in the present study were acceptable and negligible. Furthermore, the observers needed ~2 min to segment a potato or a porcine kidney using the segmentation software constructed in the present study. A deformable contour method (Snake) was applied to develop the segmentation method used in the present study, due to the accuracy and the computational efficiency of the Snake method (21). The 2D contours in axial images can be extracted within several seconds on a normal personal computer platform, and the segmentation results can be easily corrected by adjusting the 2D contours manually.

The environments surrounding the object can affect the segmentation process (18). Thus, in the present study, objects were directly exposed to the air or immersed in oil for in vitro CT scanning. The results showed that the object volume measured in oil was closer to the actual volume, as observed by water displacement, and the volume measured in the air consistently underestimated the volumes. The boundary of the object segmented by the software was determined by the different attenuation between object and surrounding environment. Since the air attenuation is significantly lower compared with that of oil, the CT value at the object boundary decreases much faster when the objects is exposed in the air (20). Therefore, the cut-off point on the object boundary determined in the air by the software of the current study tended to be reduced when the same threshold value was used.

A tube voltage/current of 120 kVp/200 mAs are the standard parameters for abdominal CT imaging used at the First Affiliated Hospital of Nanjing Medical University (Nanjing, China). For CT scans in thin adult patients and children, a low energy kVp protocol (80 kVp/200 mAs) and a low mAs protocol (120 kVp/100 mAs) is typically selected, according to the ‘as-low-as reasonably achievable’ rule (25). Dong et al (26) demonstrated that a low-energy technique for pediatric patients was possible with the similar image quality. In addition, Nakaura et al (27) showed that a low kilovoltage with high tube current improved the image quality and reduced the radiation dose. However, the effect of low kilovoltage and low tube current on segmentation of solid organ has not been widely evaluated. A decrease in the X-ray tube voltage resulted in reduced amount of penetrating radiation, and thus an increase in object attenuation and image noise (2830). Numerous studies have focused on the lower kilovoltage application in iodinated contrast agent CT scanning, since the lower kilovoltage imaging resulted in evidently higher attenuation of iodine (28,29). However, in the present study, the object density increased by only ~5 HU at 80 kVp, compared with CT images at 120 kVp, while the image noise was slightly increased simultaneously. These two factors had adverse effects on the object segmentation performed using the segmentation software of the current study in vitro. Therefore, the accuracy of volume measurements at 80 and 120 kVp was similar in the present study.

A decrease in the X-ray tube current leads to an increase in image noise without affecting the CT attenuation (18,28,29), which was also demonstrated by the present study results. Image noise is certainly a factor that strongly affects the segmentation process, particularly especially at the determination of the initial contour of objects for the deformable model application (18). The current results showed that the volume determined by the software were more accurate at the higher tube current. In addition, the 3D region representing the shape of the object obtained using the Snake model was closer to the actual value at thinner slice thickness due to isometric characteristics (17). However, the best performance of the software in the current study was obtained at a higher slice thickness. The minimal mean PE was observed at a slice thickness of 1.5 mm, irrespective of the tube voltages or currents used. This result may be explained by an increased image noise at the 0.75 mm thickness.

The present study also presents several limitations. The absence of surrounding tissues, which have varying densities, resulted in easier distinction of the object from the background. Porcine kidney examination in vivo could have evaluated the semiautomatic segmentation software under a conditions that are more similar to humans. In addition, the density of potatoes and porcine kidneys ex vivo was relatively fixed (~70 HU for potatoes and ~50 HU for porcine kidneys). Therefore, the threshold for volume segment did not require modulation in order to avoid omitting kidney volume; by contrast, the attenuation of kidney is variable in patients, particularly when enhanced CT is performed in different phases.

In conclusion, the segmentation software described in the present study is a promising quantitative method for renal volume measurements on CT scans. However, different environments, tube voltages/currents and slice thicknesses can affect the performance of this segmentation software. The mean PE of object volumetric measurements was minimal when performed in oil with a tube voltage of 120 kVp, a tube current of 200 mAs and a slice thickness of 1.5 mm. Extension of this segmentation software to human beings and validation of the current findings should be investigated in future studies.

Acknowledgements

This study was supported by a grant from the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (no. JX10231801).

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

Related Articles

Journal Cover

August-2016
Volume 12 Issue 2

Print ISSN: 1792-0981
Online ISSN:1792-1015

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Liu W, Zhu Y, Tang L, Zhu X, Xu Y and Yang G: Effect of various environments and computed tomography scanning parameters on renal volume measurements in vitro: A phantom study. Exp Ther Med 12: 753-758, 2016
APA
Liu, W., Zhu, Y., Tang, L., Zhu, X., Xu, Y., & Yang, G. (2016). Effect of various environments and computed tomography scanning parameters on renal volume measurements in vitro: A phantom study. Experimental and Therapeutic Medicine, 12, 753-758. https://doi.org/10.3892/etm.2016.3414
MLA
Liu, W., Zhu, Y., Tang, L., Zhu, X., Xu, Y., Yang, G."Effect of various environments and computed tomography scanning parameters on renal volume measurements in vitro: A phantom study". Experimental and Therapeutic Medicine 12.2 (2016): 753-758.
Chicago
Liu, W., Zhu, Y., Tang, L., Zhu, X., Xu, Y., Yang, G."Effect of various environments and computed tomography scanning parameters on renal volume measurements in vitro: A phantom study". Experimental and Therapeutic Medicine 12, no. 2 (2016): 753-758. https://doi.org/10.3892/etm.2016.3414