Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images
Purpose To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions. Methods A custom‐made phantom constructed according to the instructions given in the IAEA Human Health Series No.39 publicat...
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Veröffentlicht in: | Journal of Applied Clinical Medical Physics 2024-04, Vol.25 (4), p.e14285-n/a |
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description | Purpose
To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions.
Methods
A custom‐made phantom constructed according to the instructions given in the IAEA Human Health Series No.39 publication was used, along with the respective software that automatically calculates various IQ metrics. Images with various exposure parameters were acquired with a digital radiography unit, which for each acquisition produces two images: one for‐processing (raw) and one for‐presentation (clinical). Various examination protocols were used, which incorporate diverse post‐processing algorithms. The IQ metrics’ values (IQ‐scores) obtained were analyzed to investigate the effects of increasing incident air kerma (IAK) on the image receptor, tube potential (kVp), additional filtration, and examination protocol on image quality, and the differences between image type (raw or clinical).
Results
The IQ‐scores were consistent for repeated identical exposures for both raw and clinical images. The effect that changes in exposure parameters and examination protocol had on IQ‐scores were different depending on the IQ metric and image type. The expected positive effect that increasing IAK and decreasing tube potential should have on IQ was clearly exhibited in two IQ metrics only, the signal difference‐to‐noise‐ratio (SDNR) and the detectability index (d’), for both image types. No effect of additional filtration on any of the IQ metrics was detected on images of either type. An interesting finding of the study was that for all different image acquisition selections the d’ scores were larger in raw images, whereas the other IQ metrics were larger in clinical images for most of the cases.
Conclusions
Since IQ‐scores of raw and their respective clinical images may be largely different, the same type of image should be consistently used for monitoring IQ constancy and when results from different X‐ray systems are compared. |
doi_str_mv | 10.1002/acm2.14285 |
format | Article |
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To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions.
Methods
A custom‐made phantom constructed according to the instructions given in the IAEA Human Health Series No.39 publication was used, along with the respective software that automatically calculates various IQ metrics. Images with various exposure parameters were acquired with a digital radiography unit, which for each acquisition produces two images: one for‐processing (raw) and one for‐presentation (clinical). Various examination protocols were used, which incorporate diverse post‐processing algorithms. The IQ metrics’ values (IQ‐scores) obtained were analyzed to investigate the effects of increasing incident air kerma (IAK) on the image receptor, tube potential (kVp), additional filtration, and examination protocol on image quality, and the differences between image type (raw or clinical).
Results
The IQ‐scores were consistent for repeated identical exposures for both raw and clinical images. The effect that changes in exposure parameters and examination protocol had on IQ‐scores were different depending on the IQ metric and image type. The expected positive effect that increasing IAK and decreasing tube potential should have on IQ was clearly exhibited in two IQ metrics only, the signal difference‐to‐noise‐ratio (SDNR) and the detectability index (d’), for both image types. No effect of additional filtration on any of the IQ metrics was detected on images of either type. An interesting finding of the study was that for all different image acquisition selections the d’ scores were larger in raw images, whereas the other IQ metrics were larger in clinical images for most of the cases.
Conclusions
Since IQ‐scores of raw and their respective clinical images may be largely different, the same type of image should be consistently used for monitoring IQ constancy and when results from different X‐ray systems are compared.</description><identifier>ISSN: 1526-9914</identifier><identifier>EISSN: 1526-9914</identifier><identifier>DOI: 10.1002/acm2.14285</identifier><identifier>PMID: 38317593</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Algorithms ; digital radiography ; Humans ; image quality ; Imaging Physics ; phantoms ; Phantoms, Imaging ; post‐processing ; Radiation Dosage ; Radiographic Image Enhancement ; Radiography ; Radiography, Medical ; Software ; X-Rays</subject><ispartof>Journal of Applied Clinical Medical Physics, 2024-04, Vol.25 (4), p.e14285-n/a</ispartof><rights>2024 The Authors. is published by Wiley Periodicals, Inc. on behalf of The American Association of Physicists in Medicine.</rights><rights>2024 The Authors. Journal of Applied Clinical Medical Physics is published by Wiley Periodicals, Inc. on behalf of The American Association of Physicists in Medicine.</rights><rights>COPYRIGHT 2024 John Wiley & Sons, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c4475-f460f9cf1bb8ff2e5ab909050b4fa4840fb78677f971c634377ef518f2f6ddb53</cites><orcidid>0000-0001-8007-1682</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11005988/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11005988/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,1417,11562,27924,27925,45574,45575,46052,46476,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38317593$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tsalafoutas, Ioannis A.</creatorcontrib><creatorcontrib>AlKhazzam, Shady</creatorcontrib><creatorcontrib>Tsapaki, Virginia</creatorcontrib><creatorcontrib>Kharita, Mohammed Hassan</creatorcontrib><title>Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images</title><title>Journal of Applied Clinical Medical Physics</title><addtitle>J Appl Clin Med Phys</addtitle><description>Purpose
To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions.
Methods
A custom‐made phantom constructed according to the instructions given in the IAEA Human Health Series No.39 publication was used, along with the respective software that automatically calculates various IQ metrics. Images with various exposure parameters were acquired with a digital radiography unit, which for each acquisition produces two images: one for‐processing (raw) and one for‐presentation (clinical). Various examination protocols were used, which incorporate diverse post‐processing algorithms. The IQ metrics’ values (IQ‐scores) obtained were analyzed to investigate the effects of increasing incident air kerma (IAK) on the image receptor, tube potential (kVp), additional filtration, and examination protocol on image quality, and the differences between image type (raw or clinical).
Results
The IQ‐scores were consistent for repeated identical exposures for both raw and clinical images. The effect that changes in exposure parameters and examination protocol had on IQ‐scores were different depending on the IQ metric and image type. The expected positive effect that increasing IAK and decreasing tube potential should have on IQ was clearly exhibited in two IQ metrics only, the signal difference‐to‐noise‐ratio (SDNR) and the detectability index (d’), for both image types. No effect of additional filtration on any of the IQ metrics was detected on images of either type. An interesting finding of the study was that for all different image acquisition selections the d’ scores were larger in raw images, whereas the other IQ metrics were larger in clinical images for most of the cases.
Conclusions
Since IQ‐scores of raw and their respective clinical images may be largely different, the same type of image should be consistently used for monitoring IQ constancy and when results from different X‐ray systems are compared.</description><subject>Algorithms</subject><subject>digital radiography</subject><subject>Humans</subject><subject>image quality</subject><subject>Imaging Physics</subject><subject>phantoms</subject><subject>Phantoms, Imaging</subject><subject>post‐processing</subject><subject>Radiation Dosage</subject><subject>Radiographic Image Enhancement</subject><subject>Radiography</subject><subject>Radiography, Medical</subject><subject>Software</subject><subject>X-Rays</subject><issn>1526-9914</issn><issn>1526-9914</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><recordid>eNp9kc9u2yAAxlHVqf_WSx9g8nGqlAwwGDhNUbS2kzrtsp0RxuAy2ZCCnSq3PUKfsU9SEmdRd6k4gD4-fnzwAXCF4BxBiL8o3eM5IpjTI3CGKK5mQiBy_GZ9Cs5T-gMhQrzkJ-C05CViVJRnwC_GIfRqcLpwvWpN8Tiqzg2bwqxVN2Y9-ML5onGtG1RXRNW40Ea1etgUY3K-LWyIL3-fVzFok3aC8s1BNMn4YQ_Z0tNH8MGqLpnL_XwBft98-7W8m93_vP2-XNzPNCGMziypoBXaorrm1mJDVS2ggBTWxCrCCbQ14xVjVjCkq5KUjBlLEbfYVk1T0_ICfJ24q7HuTaNzjKg6uYo5RtzIoJz8f8e7B9mGtUT5R6ngPBM-7wkxPI4mDbJ3SZuuU96EMUksMBZEsJ11Pllb1RnpvA0ZqfNoTO908Ma6rC-YYJBXOXU-cD0d0DGkFI09BENQbiuV20rlrtJs_vT2KQfrvw6zAU2Gp3zN5h2UXCx_4An6CveasKs</recordid><startdate>202404</startdate><enddate>202404</enddate><creator>Tsalafoutas, Ioannis A.</creator><creator>AlKhazzam, Shady</creator><creator>Tsapaki, Virginia</creator><creator>Kharita, Mohammed Hassan</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IAO</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8007-1682</orcidid></search><sort><creationdate>202404</creationdate><title>Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images</title><author>Tsalafoutas, Ioannis A. ; AlKhazzam, Shady ; Tsapaki, Virginia ; Kharita, Mohammed Hassan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4475-f460f9cf1bb8ff2e5ab909050b4fa4840fb78677f971c634377ef518f2f6ddb53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>digital radiography</topic><topic>Humans</topic><topic>image quality</topic><topic>Imaging Physics</topic><topic>phantoms</topic><topic>Phantoms, Imaging</topic><topic>post‐processing</topic><topic>Radiation Dosage</topic><topic>Radiographic Image Enhancement</topic><topic>Radiography</topic><topic>Radiography, Medical</topic><topic>Software</topic><topic>X-Rays</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tsalafoutas, Ioannis A.</creatorcontrib><creatorcontrib>AlKhazzam, Shady</creatorcontrib><creatorcontrib>Tsapaki, Virginia</creatorcontrib><creatorcontrib>Kharita, Mohammed Hassan</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Online Library Free Content</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale Academic OneFile</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of Applied Clinical Medical Physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tsalafoutas, Ioannis A.</au><au>AlKhazzam, Shady</au><au>Tsapaki, Virginia</au><au>Kharita, Mohammed Hassan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images</atitle><jtitle>Journal of Applied Clinical Medical Physics</jtitle><addtitle>J Appl Clin Med Phys</addtitle><date>2024-04</date><risdate>2024</risdate><volume>25</volume><issue>4</issue><spage>e14285</spage><epage>n/a</epage><pages>e14285-n/a</pages><issn>1526-9914</issn><eissn>1526-9914</eissn><abstract>Purpose
To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions.
Methods
A custom‐made phantom constructed according to the instructions given in the IAEA Human Health Series No.39 publication was used, along with the respective software that automatically calculates various IQ metrics. Images with various exposure parameters were acquired with a digital radiography unit, which for each acquisition produces two images: one for‐processing (raw) and one for‐presentation (clinical). Various examination protocols were used, which incorporate diverse post‐processing algorithms. The IQ metrics’ values (IQ‐scores) obtained were analyzed to investigate the effects of increasing incident air kerma (IAK) on the image receptor, tube potential (kVp), additional filtration, and examination protocol on image quality, and the differences between image type (raw or clinical).
Results
The IQ‐scores were consistent for repeated identical exposures for both raw and clinical images. The effect that changes in exposure parameters and examination protocol had on IQ‐scores were different depending on the IQ metric and image type. The expected positive effect that increasing IAK and decreasing tube potential should have on IQ was clearly exhibited in two IQ metrics only, the signal difference‐to‐noise‐ratio (SDNR) and the detectability index (d’), for both image types. No effect of additional filtration on any of the IQ metrics was detected on images of either type. An interesting finding of the study was that for all different image acquisition selections the d’ scores were larger in raw images, whereas the other IQ metrics were larger in clinical images for most of the cases.
Conclusions
Since IQ‐scores of raw and their respective clinical images may be largely different, the same type of image should be consistently used for monitoring IQ constancy and when results from different X‐ray systems are compared.</abstract><cop>United States</cop><pub>John Wiley & Sons, Inc</pub><pmid>38317593</pmid><doi>10.1002/acm2.14285</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-8007-1682</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms digital radiography Humans image quality Imaging Physics phantoms Phantoms, Imaging post‐processing Radiation Dosage Radiographic Image Enhancement Radiography Radiography, Medical Software X-Rays |
title | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images |
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