Lung nodule detection in pediatric chest CT: Quantitative relationship between image quality and radiologist performance

Purpose : To determine the quantitative relationship between image quality and radiologist performance in detecting small lung nodules in pediatric CT. Methods : The study included clinical chest CT images of 30 pediatric patients (0–16 years) scanned at tube currents of 55–180 mA. Calibrated noise...

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Veröffentlicht in:Medical physics (Lancaster) 2011-05, Vol.38 (5), p.2609-2618
Hauptverfasser: Li, Xiang, Samei, Ehsan, Barnhart, Huiman X., Gaca, Ana Maria, Hollingsworth, Caroline L., Maxfield, Charles M., Carrico, Caroline W. T., Colsher, James G., Frush, Donald P.
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container_end_page 2618
container_issue 5
container_start_page 2609
container_title Medical physics (Lancaster)
container_volume 38
creator Li, Xiang
Samei, Ehsan
Barnhart, Huiman X.
Gaca, Ana Maria
Hollingsworth, Caroline L.
Maxfield, Charles M.
Carrico, Caroline W. T.
Colsher, James G.
Frush, Donald P.
description Purpose : To determine the quantitative relationship between image quality and radiologist performance in detecting small lung nodules in pediatric CT. Methods : The study included clinical chest CT images of 30 pediatric patients (0–16 years) scanned at tube currents of 55–180 mA. Calibrated noise addition software was used to simulate cases at three nominal mA settings: 70, 35, and 17.5 mA, resulting in quantum noise of 7–32 Hounsfield Unit (HU). Using a validated nodule simulation technique, lung nodules with diameters of 3–5 mm and peak contrasts of 200–500 HU were inserted into the cases, which were then randomized and rated independently by four experienced pediatric radiologists for nodule presence on a continuous scale from 0 (definitely absent) to 100 (definitely present). The receiver operating characteristic (ROC) data were analyzed to quantify the relationship between diagnostic accuracy (area under the ROC curve, AUC) and image quality (the product of nodule peak contrast and displayed diameter to noise ratio, CDNR display ). Results : AUC increased rapidly from 0.70 to 0.87 when CDNR display increased from 60 to 130 mm, followed by a slow increase to 0.94 when CDNR display further increased to 257 mm. For the average nodule diameter (4 mm) and contrast (350 HU), AUC decreased from 0.93 to 0.71 with noise increased from 7 to 28 HU. Conclusions : We quantified the relationship between image quality and the performance of radiologists in detecting lung nodules in pediatric CT. The relationship can guide CT protocol design to achieve the desired diagnostic performance at the lowest radiation dose.]
doi_str_mv 10.1118/1.3582975
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Using a validated nodule simulation technique, lung nodules with diameters of 3–5 mm and peak contrasts of 200–500 HU were inserted into the cases, which were then randomized and rated independently by four experienced pediatric radiologists for nodule presence on a continuous scale from 0 (definitely absent) to 100 (definitely present). The receiver operating characteristic (ROC) data were analyzed to quantify the relationship between diagnostic accuracy (area under the ROC curve, AUC) and image quality (the product of nodule peak contrast and displayed diameter to noise ratio, CDNR display ). Results : AUC increased rapidly from 0.70 to 0.87 when CDNR display increased from 60 to 130 mm, followed by a slow increase to 0.94 when CDNR display further increased to 257 mm. For the average nodule diameter (4 mm) and contrast (350 HU), AUC decreased from 0.93 to 0.71 with noise increased from 7 to 28 HU. 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T.</creatorcontrib><creatorcontrib>Colsher, James G.</creatorcontrib><creatorcontrib>Frush, Donald P.</creatorcontrib><title>Lung nodule detection in pediatric chest CT: Quantitative relationship between image quality and radiologist performance</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>Purpose : To determine the quantitative relationship between image quality and radiologist performance in detecting small lung nodules in pediatric CT. Methods : The study included clinical chest CT images of 30 pediatric patients (0–16 years) scanned at tube currents of 55–180 mA. Calibrated noise addition software was used to simulate cases at three nominal mA settings: 70, 35, and 17.5 mA, resulting in quantum noise of 7–32 Hounsfield Unit (HU). Using a validated nodule simulation technique, lung nodules with diameters of 3–5 mm and peak contrasts of 200–500 HU were inserted into the cases, which were then randomized and rated independently by four experienced pediatric radiologists for nodule presence on a continuous scale from 0 (definitely absent) to 100 (definitely present). The receiver operating characteristic (ROC) data were analyzed to quantify the relationship between diagnostic accuracy (area under the ROC curve, AUC) and image quality (the product of nodule peak contrast and displayed diameter to noise ratio, CDNR display ). Results : AUC increased rapidly from 0.70 to 0.87 when CDNR display increased from 60 to 130 mm, followed by a slow increase to 0.94 when CDNR display further increased to 257 mm. For the average nodule diameter (4 mm) and contrast (350 HU), AUC decreased from 0.93 to 0.71 with noise increased from 7 to 28 HU. Conclusions : We quantified the relationship between image quality and the performance of radiologists in detecting lung nodules in pediatric CT. The relationship can guide CT protocol design to achieve the desired diagnostic performance at the lowest radiation dose.]</description><subject>Adolescent</subject><subject>Algorithms</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Computed tomography</subject><subject>computerised tomography</subject><subject>diagnostic accuracy</subject><subject>dose</subject><subject>dose reduction</subject><subject>dosimetry</subject><subject>Dosimetry/exposure assessment</subject><subject>Female</subject><subject>Humans</subject><subject>image perception</subject><subject>image quality</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>lesion simulation</subject><subject>lung</subject><subject>Lungs</subject><subject>Male</subject><subject>Medical image contrast</subject><subject>Medical image noise</subject><subject>medical image processing</subject><subject>Medical image quality</subject><subject>Medical image reconstruction</subject><subject>Medical imaging</subject><subject>noise simulation</subject><subject>Observer Variation</subject><subject>optimization</subject><subject>paediatrics</subject><subject>Professional Competence</subject><subject>quantum noise</subject><subject>Radiographic Image Enhancement - methods</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiography, Thoracic - methods</subject><subject>Radiologists</subject><subject>receiver operating characteristic</subject><subject>Reproducibility of Results</subject><subject>ROC</subject><subject>sensitivity analysis</subject><subject>Sensitivity and Specificity</subject><subject>Solitary Pulmonary Nodule - diagnostic imaging</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kd9rFDEQx4NY7LX64D8geROEbfNjs0l8EMrRqnCiQn0O2WT2GtlLttls6_333fOuIkh9mof5zHeY-SD0mpIzSqk6p2dcKKaleIYWrJa8qhnRz9GCEF1XrCbiGJ2M409CSMMFeYGOGZWykVot0K_VFNc4Jj_1gD0UcCWkiEPEA_hgSw4OuxsYC15ev8ffJxtLKLaEO8AZeruDx5sw4BbKPcA8uLFrwLeT7UPZYhs9ztaH1Kd1mDMGyF3KGxsdvERHne1HeHWop-jH1eX18lO1-vrx8_JiVblaalFJwXXjaefAKy2kaJ2TbedaS33NGXMChGiajlHotGyZ4N4R5aTXjNNG8Zaforf73CGn22k-xGzC6KDvbYQ0jUZJpRhVDZ3JNwdyajfgzZDnY_LWPD5rBqo9cB962P7pU2J2Fgw1Bwvmy7ddmfkPe350v3-W4tMzOw1mr8E8apgD3j0VcJfyXwsH3_0P_mcbfwDeu6ya</recordid><startdate>201105</startdate><enddate>201105</enddate><creator>Li, Xiang</creator><creator>Samei, Ehsan</creator><creator>Barnhart, Huiman X.</creator><creator>Gaca, Ana Maria</creator><creator>Hollingsworth, Caroline L.</creator><creator>Maxfield, Charles M.</creator><creator>Carrico, Caroline W. 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T.</creatorcontrib><creatorcontrib>Colsher, James G.</creatorcontrib><creatorcontrib>Frush, Donald P.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xiang</au><au>Samei, Ehsan</au><au>Barnhart, Huiman X.</au><au>Gaca, Ana Maria</au><au>Hollingsworth, Caroline L.</au><au>Maxfield, Charles M.</au><au>Carrico, Caroline W. T.</au><au>Colsher, James G.</au><au>Frush, Donald P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lung nodule detection in pediatric chest CT: Quantitative relationship between image quality and radiologist performance</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2011-05</date><risdate>2011</risdate><volume>38</volume><issue>5</issue><spage>2609</spage><epage>2618</epage><pages>2609-2618</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><coden>MPHYA6</coden><abstract>Purpose : To determine the quantitative relationship between image quality and radiologist performance in detecting small lung nodules in pediatric CT. Methods : The study included clinical chest CT images of 30 pediatric patients (0–16 years) scanned at tube currents of 55–180 mA. Calibrated noise addition software was used to simulate cases at three nominal mA settings: 70, 35, and 17.5 mA, resulting in quantum noise of 7–32 Hounsfield Unit (HU). Using a validated nodule simulation technique, lung nodules with diameters of 3–5 mm and peak contrasts of 200–500 HU were inserted into the cases, which were then randomized and rated independently by four experienced pediatric radiologists for nodule presence on a continuous scale from 0 (definitely absent) to 100 (definitely present). The receiver operating characteristic (ROC) data were analyzed to quantify the relationship between diagnostic accuracy (area under the ROC curve, AUC) and image quality (the product of nodule peak contrast and displayed diameter to noise ratio, CDNR display ). Results : AUC increased rapidly from 0.70 to 0.87 when CDNR display increased from 60 to 130 mm, followed by a slow increase to 0.94 when CDNR display further increased to 257 mm. For the average nodule diameter (4 mm) and contrast (350 HU), AUC decreased from 0.93 to 0.71 with noise increased from 7 to 28 HU. Conclusions : We quantified the relationship between image quality and the performance of radiologists in detecting lung nodules in pediatric CT. The relationship can guide CT protocol design to achieve the desired diagnostic performance at the lowest radiation dose.]</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><pmid>21776798</pmid><doi>10.1118/1.3582975</doi><tpages>10</tpages></addata></record>
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source MEDLINE; Wiley Online Library Journals Frontfile Complete; Alma/SFX Local Collection
subjects Adolescent
Algorithms
Child
Child, Preschool
Computed tomography
computerised tomography
diagnostic accuracy
dose
dose reduction
dosimetry
Dosimetry/exposure assessment
Female
Humans
image perception
image quality
Infant
Infant, Newborn
lesion simulation
lung
Lungs
Male
Medical image contrast
Medical image noise
medical image processing
Medical image quality
Medical image reconstruction
Medical imaging
noise simulation
Observer Variation
optimization
paediatrics
Professional Competence
quantum noise
Radiographic Image Enhancement - methods
Radiographic Image Interpretation, Computer-Assisted - methods
Radiography, Thoracic - methods
Radiologists
receiver operating characteristic
Reproducibility of Results
ROC
sensitivity analysis
Sensitivity and Specificity
Solitary Pulmonary Nodule - diagnostic imaging
Tomography, X-Ray Computed - methods
title Lung nodule detection in pediatric chest CT: Quantitative relationship between image quality and radiologist performance
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