Evaluation of the effect of image noise on CT perfusion measurements using digital perfusion phantoms
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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.
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.
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.
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.
• 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.
KeywordsTomography, x-ray computed Perfusion imaging Phantoms, imaging Software Artifacts
Arterial input function
Analysis of covariance
Digital Imaging and Communications in Medicine
Flow extraction product
Impulse response function
The authors state that this work has not received any funding.
Compliance with ethical standards
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.
Written informed consent was obtained from all subjects (patients) in this study.
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.
• not applicable/retrospective
• performed at one institution