Skip to main content
SpringerLink
Log in

Automated Detection of Z-Axis Coverage with Abdomen-Pelvis Computed Tomography Examinations

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

Excessive cephalocaudal anatomic (Z-axis) coverage can lead to unnecessary radiation exposure to a patient. In this study, an automated computing model was developed for identifying instances of potentially excessive Z-axis coverage with abdomen-pelvis examinations. Eight patient and imaging attributes including patient gender, age, height, weight, volume CT dose index (CTDIvol), dose length product (DLP), maximum abdomen width, and maximum abdomen thickness were used to build a feedforward neural network model to predict a target Z-axis coverage whether it is an excessive or non-excessive Z-axis coverage scans. 264 CT abdomen-pelvis exams were used to develop the model which is validated using 10-fold cross validation. The result showed that 244 out of 264 exams (92.4 %) correctly predicted Z-axis excessive coverage. The promising results indicate that this tool has the potential to be used for CT exams of the chest and colon, urography, and other site-specified CT studies having defined limited length.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Yu L, Liu X, Leng S, Kofler JM, Ramirez-Giraldo JC, Qu M, Christner J, Fletcher JG, McCollough CH: Radiation dose reduction in computed tomography: techniques and future perspective. Imaging Med 1:65–84, 2009

    Article  PubMed Central  PubMed  Google Scholar 

  2. Kalra MK, Maher MM, Toth TL, Kamath RS, Halpern EF, Saini S: Radiation from “extra” images acquired with abdominal and/or pelvic CT: effect of automatic tube current modulation1”. Radiology 232(2):409–414, 2004

    Article  PubMed  Google Scholar 

  3. Manduca A, Yu L, Trzasko JD, Khaylova N, Kofler JM, McCollough CM, Fletcher JG: Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med Phys 36(11):4911–4919, 2009

    Article  PubMed Central  PubMed  Google Scholar 

  4. Szucs-Farkas Z, Verdun FR, von Allmen G, Mini RL, Vock P: Effect of X-ray tube parameters, iodine concentration, and patient size on image quality in pulmonary computed tomography angiography: a chest-phantom-study. Investig Radiol 43(6):374–381, 2008

    Article  Google Scholar 

  5. Graser A, Johnson TR, Chandarana H, Macari M: Dual energy CT: preliminary observations and potential clinical applications in the abdomen. Eur Radiol 19(1):13–23, 2009

    Article  PubMed  Google Scholar 

  6. Liao EA, Quint LE, Goodsitt MM, Francis IR, Khalatbari S, Myles JD: Extra Z-axis coverage at CT imaging resulting in excess radiation dose: frequency, degree and contributory factors. J Comput Assist Tomogr 35(1):50, 2011

    Article  PubMed Central  PubMed  Google Scholar 

  7. Wang S, Pavlicek W, Roberts CC, Langer SG, Zhang M, Hu M, Morin RL, Schueler BA, Wellnitz CV, Wu T: An automated DICOM database capable of arbitrary data mining (including radiation dose indicators) for quality monitoring. J Digit Imaging 24(2):223–233, 2011

    Article  PubMed Central  PubMed  Google Scholar 

  8. Haibo H, Garcia EA: Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284, 2009

    Article  Google Scholar 

  9. Sandberg IW: Nonlinear dynamical systems: feedforward neural network perspectives. John Wiley & Sons, 2001

  10. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18, 2009

    Article  Google Scholar 

  11. McCollough CH: Automated data mining of exposure information for dose management and patient safety initiatives in medical imaging. Radiology 264(2):322–324, 2012

    Article  PubMed  Google Scholar 

  12. Hara AK, Wellnitz CV, Paden RG, Pavlicek W, Sahani DV: Reducing body CT radiation dose: beyond just changing the numbers. Am J Roentgenol 201(1):33–40, 2013

    Article  Google Scholar 

  13. Ikuta I, Warden GI, Andriole KP, Khorasani R, Sodickson A: Estimating patient dose from X-ray tube output metrics: automated measurement of patient size from CT images enables large-scale size-specific dose estimates. Radiology 270(2):472–480, 2014

    Article  PubMed Central  PubMed  Google Scholar 

  14. Cheng PM, Vachon LA, Duddalwar VA: Automated pediatric abdominal effective diameter measurements versus age-predicted body size for normalization of CT dose. J Digit Imaging 26(6):1151–1155, 2013

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, Informatics Decisions and System Engineering, Arizona State University, Tempe, AZ, USA

    Min Zhang, Can Cui & Teresa Wu

  2. Department of Radiology, Mayo Clinic, 13400 E. Shea Boulevard, Scottsdale, AZ, 85259, USA

    Min Zhang, Clinton Wellnitz & William Pavlicek

Authors
  1. Min Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Clinton Wellnitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Can Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William Pavlicek
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Teresa Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William Pavlicek.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, M., Wellnitz, C., Cui, C. et al. Automated Detection of Z-Axis Coverage with Abdomen-Pelvis Computed Tomography Examinations. J Digit Imaging 28, 362–367 (2015). https://doi.org/10.1007/s10278-014-9743-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-014-9743-7

Keywords

Search

Navigation