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Evaluation of the effect of image noise on CT perfusion measurements using digital perfusion phantoms

  • Stephan Skornitzke
  • Jessica Hirsch
  • Hans-Ulrich Kauczor
  • Wolfram Stiller
Computed Tomography
  • 13 Downloads

Abstract

Objectives

To assess the influence of image noise on computed tomography (CT) perfusion studies, CT perfusion software algorithms were evaluated for susceptibility to image noise and results applied to clinical perfusion studies.

Methods

Digital perfusion phantoms were generated using a published deconvolution model to create time-attenuation curves (TACs) for 16 different combinations of blood flow (BF; 30/60/90/120 ml/100 ml/min) and flow extraction product (FEP; 10/20/30/40 ml/100 ml/min) corresponding to values encountered in clinical studies. TACs were distorted with Gaussian noise at 50 different strengths to approximate image noise, performing 200 repetitions for each noise level. A total of 160,000 TACs were evaluated by measuring BF and FEP with CT perfusion software, comparing results for the maximum slope and Patlak models with those obtained with a deconvolution model. To translate results to clinical practice, data of 23 patients from a CT perfusion study were assessed for image noise, and the accuracy of reported CT perfusion measurements was estimated.

Results

Perfusion measurements depend on image noise as means and standard deviations of BF and FEP over repetitions increase with increasing image noise, especially for low BF and FEP values. BF measurements derived by deconvolution show larger standard deviations than those performed with the maximum slope model. Image noise in the evaluated CT perfusion study was 26.46 ± 3.52 HU, indicating possible overestimation of BF by up to 85% in a clinical setting.

Conclusions

Measurements of perfusion parameters depend heavily upon the magnitude of image noise, which has to be taken into account during selection of acquisition parameters and interpretation of results, e.g., as a quantitative imaging biomarker.

Key Points

• CT perfusion results depend heavily upon the magnitude of image noise.

• Different CT perfusion models react differently to the presence of image noise.

• Blood flow may be overestimated by 85% in clinical CT perfusion studies.

Keywords

Tomography, x-ray computed Perfusion imaging Phantoms, imaging Software Artifacts 

Abbreviations

AIF

Arterial input function

ANCOVA

Analysis of covariance

BF

Blood flow

CT

Computed tomography

DICOM

Digital Imaging and Communications in Medicine

FEP

Flow extraction product

IRF

Impulse response function

TAC

Time-attenuation curve

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dr. Wolfram Stiller.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Hans-Ulrich Kauczor is the recipient of a research grant from Siemens Healthineers.

Otherwise, the remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Two of the authors have significant statistical expertise:

Dr. Jessica Hirsch (CHRESTOS Institute, Dortmund, Germany) and Dr. Stephan Skornitzke (Heidelberg University Hospital, Diagnostic & Interventional Radiology [DIR], Heidelberg, Germany) have significant statistical expertise and jointly performed the statistical evaluation for this study.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in:

Skornitzke S, Fritz F, Mayer P, Koell M, Hansen J, Pahn G, Hackert T, Kauczor HU, Stiller W. “Dual-energy CT iodine maps as an alternative quantitative imaging biomarker to abdominal CT perfusion: determination of appropriate trigger delays for acquisition using bolus tracking.” Br J Radiol 2018; 91: 20170351. doi: ​10.​1259/​bjr.​20170351.

Methodology

• not applicable/retrospective

• experimental

• performed at one institution

Supplementary material

330_2018_5709_MOESM1_ESM.docx (224 kb)
ESM 1 (DOCX 224 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Stephan Skornitzke
    • 1
  • Jessica Hirsch
    • 2
  • Hans-Ulrich Kauczor
    • 1
  • Wolfram Stiller
    • 1
  1. 1.Diagnostic & Interventional Radiology (DIR), Heidelberg University HospitalHeidelbergGermany
  2. 2.CHRESTOS InstitutDortmundGermany

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