Achille Beysang , Nicolas Villani , Fatma Boubaker , Ulysse Puel , Michael Eliezer , Gabriela Hossu , Karim Haioun , Alain Blum , Pedro Augusto Gondim Teixeira , Cécile Parietti-Winkler , Romain Gillet
{"title":"颞骨的超高分辨率 CT：深度学习重建与混合重建和基于模型的迭代重建的比较。","authors":"Achille Beysang , Nicolas Villani , Fatma Boubaker , Ulysse Puel , Michael Eliezer , Gabriela Hossu , Karim Haioun , Alain Blum , Pedro Augusto Gondim Teixeira , Cécile Parietti-Winkler , Romain Gillet","doi":"10.1016/j.diii.2024.02.001","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study was to evaluate the ability of ultra-high-resolution computed tomography (UHR-CT) to assess stapes and chorda tympani nerve anatomy using a deep learning (DLR), a model-based, and a hybrid iterative reconstruction algorithm compared to simulated conventional CT.</p></div><div><h3>Materials and methods</h3><p>CT acquisitions were performed with a Mercury 4.0 phantom. Images were acquired with a 1024 × 1024 matrix and a 0.25 mm slice thickness and reconstructed using DLR, model-based, and hybrid iterative reconstruction algorithms. To simulate conventional CT, images were also reconstructed with a 512 × 512 matrix and a 0.5 mm slice thickness. Spatial resolution, noise power spectrum, and objective high-contrast detectability were compared. Three radiologists evaluated the clinical acceptability of these algorithms by assessing the thickness and image quality of the stapes footplate and superstructure elements, as well as the image quality of the chorda tympani nerve bony and tympanic segments using a 5-point confidence scale on 13 temporal bone CT examinations reconstructed with the four algorithms.</p></div><div><h3>Results</h3><p>UHR-CT provided higher spatial resolution than simulated conventional CT at the penalty of higher noise. DLR and model-based iterative reconstruction provided better noise reduction than hybrid iterative reconstruction, and DLR had the highest detectability index, regardless of the dose level. All stapedial structure thicknesses were thinner using UHR-CT by comparison with conventional simulated CT (<em>P</em> < 0.009). DLR showed the best visualization scores compared to the other reconstruction algorithms (<em>P</em> < 0.032).</p></div><div><h3>Conclusion</h3><p>UHR-CT with DLR results in less noise than UHR-CT with hybrid iterative reconstruction and significantly improves stapes and tympanic chorda tympani nerve depiction compared to simulated conventional CT and UHR-CT with iterative reconstruction.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211568424000366/pdfft?md5=e3ddcd3dea6038967becaee1d307a882&pid=1-s2.0-S2211568424000366-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Ultra-high-resolution CT of the temporal bone: Comparison between deep learning reconstruction and hybrid and model-based iterative reconstruction\",\"authors\":\"Achille Beysang , Nicolas Villani , Fatma Boubaker , Ulysse Puel , Michael Eliezer , Gabriela Hossu , Karim Haioun , Alain Blum , Pedro Augusto Gondim Teixeira , Cécile Parietti-Winkler , Romain Gillet\",\"doi\":\"10.1016/j.diii.2024.02.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>The purpose of this study was to evaluate the ability of ultra-high-resolution computed tomography (UHR-CT) to assess stapes and chorda tympani nerve anatomy using a deep learning (DLR), a model-based, and a hybrid iterative reconstruction algorithm compared to simulated conventional CT.</p></div><div><h3>Materials and methods</h3><p>CT acquisitions were performed with a Mercury 4.0 phantom. Images were acquired with a 1024 × 1024 matrix and a 0.25 mm slice thickness and reconstructed using DLR, model-based, and hybrid iterative reconstruction algorithms. To simulate conventional CT, images were also reconstructed with a 512 × 512 matrix and a 0.5 mm slice thickness. Spatial resolution, noise power spectrum, and objective high-contrast detectability were compared. Three radiologists evaluated the clinical acceptability of these algorithms by assessing the thickness and image quality of the stapes footplate and superstructure elements, as well as the image quality of the chorda tympani nerve bony and tympanic segments using a 5-point confidence scale on 13 temporal bone CT examinations reconstructed with the four algorithms.</p></div><div><h3>Results</h3><p>UHR-CT provided higher spatial resolution than simulated conventional CT at the penalty of higher noise. DLR and model-based iterative reconstruction provided better noise reduction than hybrid iterative reconstruction, and DLR had the highest detectability index, regardless of the dose level. All stapedial structure thicknesses were thinner using UHR-CT by comparison with conventional simulated CT (<em>P</em> < 0.009). DLR showed the best visualization scores compared to the other reconstruction algorithms (<em>P</em> < 0.032).</p></div><div><h3>Conclusion</h3><p>UHR-CT with DLR results in less noise than UHR-CT with hybrid iterative reconstruction and significantly improves stapes and tympanic chorda tympani nerve depiction compared to simulated conventional CT and UHR-CT with iterative reconstruction.</p></div>\",\"PeriodicalId\":48656,\"journal\":{\"name\":\"Diagnostic and Interventional Imaging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2211568424000366/pdfft?md5=e3ddcd3dea6038967becaee1d307a882&pid=1-s2.0-S2211568424000366-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diagnostic and Interventional Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2211568424000366\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diagnostic and Interventional Imaging","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211568424000366","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Ultra-high-resolution CT of the temporal bone: Comparison between deep learning reconstruction and hybrid and model-based iterative reconstruction
Purpose
The purpose of this study was to evaluate the ability of ultra-high-resolution computed tomography (UHR-CT) to assess stapes and chorda tympani nerve anatomy using a deep learning (DLR), a model-based, and a hybrid iterative reconstruction algorithm compared to simulated conventional CT.
Materials and methods
CT acquisitions were performed with a Mercury 4.0 phantom. Images were acquired with a 1024 × 1024 matrix and a 0.25 mm slice thickness and reconstructed using DLR, model-based, and hybrid iterative reconstruction algorithms. To simulate conventional CT, images were also reconstructed with a 512 × 512 matrix and a 0.5 mm slice thickness. Spatial resolution, noise power spectrum, and objective high-contrast detectability were compared. Three radiologists evaluated the clinical acceptability of these algorithms by assessing the thickness and image quality of the stapes footplate and superstructure elements, as well as the image quality of the chorda tympani nerve bony and tympanic segments using a 5-point confidence scale on 13 temporal bone CT examinations reconstructed with the four algorithms.
Results
UHR-CT provided higher spatial resolution than simulated conventional CT at the penalty of higher noise. DLR and model-based iterative reconstruction provided better noise reduction than hybrid iterative reconstruction, and DLR had the highest detectability index, regardless of the dose level. All stapedial structure thicknesses were thinner using UHR-CT by comparison with conventional simulated CT (P < 0.009). DLR showed the best visualization scores compared to the other reconstruction algorithms (P < 0.032).
Conclusion
UHR-CT with DLR results in less noise than UHR-CT with hybrid iterative reconstruction and significantly improves stapes and tympanic chorda tympani nerve depiction compared to simulated conventional CT and UHR-CT with iterative reconstruction.
期刊介绍：
Diagnostic and Interventional Imaging accepts publications originating from any part of the world based only on their scientific merit. The Journal focuses on illustrated articles with great iconographic topics and aims at aiding sharpening clinical decision-making skills as well as following high research topics. All articles are published in English.
Diagnostic and Interventional Imaging publishes editorials, technical notes, letters, original and review articles on abdominal, breast, cancer, cardiac, emergency, forensic medicine, head and neck, musculoskeletal, gastrointestinal, genitourinary, interventional, obstetric, pediatric, thoracic and vascular imaging, neuroradiology, nuclear medicine, as well as contrast material, computer developments, health policies and practice, and medical physics relevant to imaging.
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