Skip to main content

Advertisement

SpringerLink
Log in

Liver detection algorithm: its efficacy for CT noise reduction in the liver

  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objectives

This study was performed to evaluate the efficacy of a novel computed tomography (CT) liver detection algorithm (LDA), which allows for targeted increase of radiation dose to the upper abdomen, on image quality of the liver.

Methods

We retrospectively evaluated the LDA by comparing 40 consecutive patients who had portal venous CT abdomen performed without use of the algorithm, to 40 patients in whom the algorithm was used. Image quality was assessed objectively by comparing the standard deviation (SD) of attenuation values in Hounsfield units (HU) of the abdominal organs. Qualitative analysis was performed by two blinded radiologists who independently graded the image quality of abdominal organs

Results

There was significant noise reduction in the liver (P < 0.001) and spleen (P < 0.001) in the LDA group compared to the conventional group. There was also a significant improvement in image quality of the liver (P < 0.001), kidney (P < 0.001), spleen (P < 0.001), pancreas (P < 0.001), and psoas (P = 0.005) in the LDA group compared to the conventional group. Overall dose between the two groups was similar.

Conclusions

This liver detection algorithm improves the subjective image quality of upper abdominal organs, in particular the liver, without increasing overall radiation dose.

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

Similar content being viewed by others

Abbreviations

CT:

Computed tomography

DLP:

Dose length product

HU:

Hounsfield units

LDA:

Liver detection algorithm

ROI:

Region of interest

SD:

Standard deviation

References

  1. Kalra MK, Maher MM, Blake MA, et al. (2004) Detection and characterization of lesions on low—radiation dose abdominal CT images postprocessed with noise reduction filters. Radiology 232(3):791–797

    Article  PubMed  Google Scholar 

  2. Funama Y, Awai K, Miyazaki O, et al. (2006) Improvement of low contrast detectability in low dose hepatic multidetector computed tomography using a novel adaptive filter: evaluation with a computer simulated liver including tumors. Invest Radiol 41:1–7

    Article  PubMed  Google Scholar 

  3. Baron RL (1994) Understanding and optimizing use of contrast material for CT of the liver. AJR Am J Roentgenol 163(2):323–331

    Article  CAS  PubMed  Google Scholar 

  4. Awai K, Takada K, Onishi H, Hori S (2002) Aortic and hepatic enhancement and tumor to liver contrast: analysis of the effect of different concentrations of contrast material at multi-detector row helical CT. Radiology 224:757–763

    Article  PubMed  Google Scholar 

  5. Kalpana MK, Chung JH, Wang J, et al. (2011) Image noise and liver lesion detection with MDCT: a phantom study. AJR Am J Roentgenol 197(2):437–441

    Article  Google Scholar 

  6. Schindera ST, Torrente JC, Ruder TD, et al. (2011) Decreased detection of hypovascular liver tumours with MCDT in obese patients: a phantom study. AJR Am J Roentgenol 196(6):772–776

    Article  Google Scholar 

  7. Baker ME, Dong F, Primak A, et al. (2012) Contrast-to-noise ratio and low-contrast object resolution on full- and low-dose MDCT: SAFIRE versus filtered back projection in a low-contrast object phantom and in the liver. AJR Am J Roentgenol 199(1):8–18

    Article  PubMed  Google Scholar 

  8. Mieville FA, Gudinchet F, Brunelle F, et al. (2013) Iterative reconstruction methods in two different MDCT scanners: Physical metrics and 4-alternative forced-choice detectability experiments—a phantom approach. Phys Med. 2012 Jan 2.

  9. Robinson PJ (2000) Imaging liver metastases: current limitations and future prospects. Br J Radiol 73(867):234–241

    Article  CAS  PubMed  Google Scholar 

  10. Kalra MK, Maher MM, Toth TL, et al. (2004) Techniques and applications of automatic tube current modulation for CT. Radiology 233(3):649–657

    Article  PubMed  Google Scholar 

  11. Friedman B, Leyendecker JR, Blaufox MD, et al. (2014) Expert Panel on urologic imaging. ACR Appropriateness criteria lower urinary tract symptoms: suspicion of benign prostatic hyperplasia. American College of Radiology [Online publication]. Available from: http://www.guideline.gov/content.aspx?id=48292. Accessed on 22 Dec 2014

  12. Andreotti RF, Lee SI, DeJesus Allison SO, et al. (2011) ACR Appropriateness criteria: acute pelvic pain in the reproductive age group. American College of Radiology [Online publication] Accessed 2014 October 22. Available from http://www.guideline.gov/content.aspx?id=32643. Accessed on Dec 2014

  13. Harris RD, Javitt MC, Glanc P, et al. Expert Panel Women’s Imaging. (2012) ACR Appropriateness Criteria clinically suspected adnexal mass. American College of Radiology [Online publication]. Available from http://www.guideline.gov/content.aspx?id=48292. Accessed on 22 Dec 2014

  14. Hodler RA Kubik-Huch, von Schuitness GK, Zollikofer CL (2014) Diseases of the Abdomen and Pelvis: diagnostic imaging and interventional techniques. Milan: Springer

    Google Scholar 

  15. McNamara MM, Lalani T, Camacho MA, et al. Expert Panel on Gastrointestinal Imaging. ACR Appropriateness Criteria left lower quadrant pain- suspected diverticulitis. American College of Radiology [Online publication]. Accessed Nov 2015

Download references

Author information

Authors and Affiliations

  1. Monash Imaging, Monash Health, 246 Clayton Road, Clayton, VIC, 3168, Australia

    Ashwini Devapalasundaram, Kenneth K. Lau, Larry Mak, Nicholas Ardley & Ahilan Kuganesan

  2. Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia

    Kenneth K. Lau

  3. Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia

    Eldho Paul

  4. Clinical Science, Phillips Healthcare, P. O. Box 312, Mont Albert, VIC, 3127, Australia

    Kevin Buchan

Authors
  1. Ashwini Devapalasundaram
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenneth K. Lau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eldho Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Larry Mak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nicholas Ardley
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kevin Buchan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ahilan Kuganesan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashwini Devapalasundaram.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Devapalasundaram, A., Lau, K.K., Paul, E. et al. Liver detection algorithm: its efficacy for CT noise reduction in the liver. Abdom Radiol 41, 493–499 (2016). https://doi.org/10.1007/s00261-015-0617-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-015-0617-3

Keywords

Search

Navigation