Luuk J Oostveen, Kirsten Boedeker, Daniel Shin, Craig K Abbey, Ioannis Sechopoulos
{"title":"Perceptual thresholds for differences in CT noise texture.","authors":"Luuk J Oostveen, Kirsten Boedeker, Daniel Shin, Craig K Abbey, Ioannis Sechopoulos","doi":"10.1117/1.JMI.11.3.035501","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>The average (<math><mrow><msub><mrow><mi>f</mi></mrow><mrow><mi>av</mi></mrow></msub></mrow></math>) or peak (<math><mrow><msub><mrow><mi>f</mi></mrow><mrow><mtext>peak</mtext></mrow></msub></mrow></math>) noise power spectrum (NPS) frequency is often used as a one-parameter descriptor of the CT noise texture. Our study develops a more complete two-parameter model of the CT NPS and investigates the sensitivity of human observers to changes in it.</p><p><strong>Approach: </strong>A model of CT NPS was created based on its <math><mrow><msub><mi>f</mi><mtext>peak</mtext></msub></mrow></math> and a half-Gaussian fit (<math><mrow><mi>σ</mi></mrow></math>) to the downslope. Two-alternative forced-choice staircase studies were used to determine perceptual thresholds for noise texture, defined as parameter differences with a predetermined level of discrimination performance (80% correct). Five imaging scientist observers performed the forced-choice studies for eight directions in the <math><mrow><msub><mi>f</mi><mtext>peak</mtext></msub><mo>/</mo><mi>σ</mi></mrow></math>-space, for two reference NPSs (corresponding to body and lung kernels). The experiment was repeated with 32 radiologists, each evaluating a single direction in the <math><mrow><msub><mi>f</mi><mtext>peak</mtext></msub><mo>/</mo><mi>σ</mi></mrow></math>-space. NPS differences were quantified by the noise texture contrast (<math><mrow><msub><mi>C</mi><mtext>texture</mtext></msub></mrow></math>), the integral of the absolute NPS difference.</p><p><strong>Results: </strong>The two-parameter NPS model was found to be a good representation of various clinical CT reconstructions. Perception thresholds for <math><mrow><msub><mi>f</mi><mtext>peak</mtext></msub></mrow></math> alone are <math><mrow><mn>0.2</mn><mtext> </mtext><mi>lp</mi><mo>/</mo><mi>cm</mi></mrow></math> for body and <math><mrow><mn>0.4</mn><mtext> </mtext><mi>lp</mi><mo>/</mo><mi>cm</mi></mrow></math> for lung NPSs. For <math><mrow><mi>σ</mi></mrow></math>, these values are 0.15 and <math><mrow><mn>2</mn><mtext> </mtext><mi>lp</mi><mo>/</mo><mi>cm</mi></mrow></math>, respectively. Thresholds change if the other parameter also changes. Different NPSs with the same <math><mrow><msub><mrow><mi>f</mi></mrow><mrow><mtext>peak</mtext></mrow></msub></mrow></math> or <math><mrow><msub><mrow><mi>f</mi></mrow><mrow><mi>av</mi></mrow></msub></mrow></math> can be discriminated. Nonradiologist observers did not need more <math><mrow><msub><mi>C</mi><mtext>texture</mtext></msub></mrow></math> than radiologists.</p><p><strong>Conclusions: </strong><math><mrow><msub><mi>f</mi><mtext>peak</mtext></msub></mrow></math> or <math><mrow><msub><mrow><mi>f</mi></mrow><mrow><mi>av</mi></mrow></msub></mrow></math> is insufficient to describe noise texture completely. The discrimination of noise texture changes depending on its frequency content. Radiologists do not discriminate noise texture changes better than nonradiologists.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 3","pages":"035501"},"PeriodicalIF":1.9000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11086665/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1117/1.JMI.11.3.035501","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/9 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: The average () or peak () noise power spectrum (NPS) frequency is often used as a one-parameter descriptor of the CT noise texture. Our study develops a more complete two-parameter model of the CT NPS and investigates the sensitivity of human observers to changes in it.
Approach: A model of CT NPS was created based on its and a half-Gaussian fit () to the downslope. Two-alternative forced-choice staircase studies were used to determine perceptual thresholds for noise texture, defined as parameter differences with a predetermined level of discrimination performance (80% correct). Five imaging scientist observers performed the forced-choice studies for eight directions in the -space, for two reference NPSs (corresponding to body and lung kernels). The experiment was repeated with 32 radiologists, each evaluating a single direction in the -space. NPS differences were quantified by the noise texture contrast (), the integral of the absolute NPS difference.
Results: The two-parameter NPS model was found to be a good representation of various clinical CT reconstructions. Perception thresholds for alone are for body and for lung NPSs. For , these values are 0.15 and , respectively. Thresholds change if the other parameter also changes. Different NPSs with the same or can be discriminated. Nonradiologist observers did not need more than radiologists.
Conclusions: or is insufficient to describe noise texture completely. The discrimination of noise texture changes depending on its frequency content. Radiologists do not discriminate noise texture changes better than nonradiologists.
期刊介绍:
JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.