Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images
<h3>Purpose</h3><p dir="ltr">To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions.</p><h3>Methods</h3><p dir="ltr">A custom‐...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513545048162304 |
|---|---|
| author | Ioannis A. Tsalafoutas (14776939) |
| author2 | Shady AlKhazzam (14776942) Virginia Tsapaki (17075218) Mohammed Hassan Kharita (14776945) |
| author2_role | author author author |
| author_facet | Ioannis A. Tsalafoutas (14776939) Shady AlKhazzam (14776942) Virginia Tsapaki (17075218) Mohammed Hassan Kharita (14776945) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ioannis A. Tsalafoutas (14776939) Shady AlKhazzam (14776942) Virginia Tsapaki (17075218) Mohammed Hassan Kharita (14776945) |
| dc.date.none.fl_str_mv | 2024-02-05T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1002/acm2.14285 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Automatic_image_quality_evaluation_in_digital_radiography_using_for_processing_and_for_presentation_images/29446187 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences digital radiography image quality phantoms post-processing |
| dc.title.none.fl_str_mv | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Purpose</h3><p dir="ltr">To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions.</p><h3>Methods</h3><p dir="ltr">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).</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Applied Clinical Medical Physics<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/acm2.14285" target="_blank">https://dx.doi.org/10.1002/acm2.14285</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_b7ed82589cb3fabbf3a9fdc4fcc46499 |
| identifier_str_mv | 10.1002/acm2.14285 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29446187 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation imagesIoannis A. Tsalafoutas (14776939)Shady AlKhazzam (14776942)Virginia Tsapaki (17075218)Mohammed Hassan Kharita (14776945)Biomedical and clinical sciencesClinical sciencesdigital radiographyimage qualityphantomspost-processing<h3>Purpose</h3><p dir="ltr">To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions.</p><h3>Methods</h3><p dir="ltr">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).</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Applied Clinical Medical Physics<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/acm2.14285" target="_blank">https://dx.doi.org/10.1002/acm2.14285</a></p>2024-02-05T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1002/acm2.14285https://figshare.com/articles/journal_contribution/Automatic_image_quality_evaluation_in_digital_radiography_using_for_processing_and_for_presentation_images/29446187CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294461872024-02-05T03:00:00Z |
| spellingShingle | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images Ioannis A. Tsalafoutas (14776939) Biomedical and clinical sciences Clinical sciences digital radiography image quality phantoms post-processing |
| status_str | publishedVersion |
| title | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images |
| title_full | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images |
| title_fullStr | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images |
| title_full_unstemmed | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images |
| title_short | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images |
| title_sort | Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images |
| topic | Biomedical and clinical sciences Clinical sciences digital radiography image quality phantoms post-processing |