{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T15:56:02Z","timestamp":1785426962953,"version":"3.56.0"},"reference-count":25,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015650","name":"Canon Medical Systems Corporation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100015650","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/01qpswk97","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: To directly compare the capabilities of hybrid-type iterative reconstruction (IR) with the newly developed deep learning reconstruction (DLR) for the inner ear on high-definition CT (HDCT) obtained using the super-high-resolution (SHR) mode for external, middle and inner ear evaluations and diagnosis in patients with and without otologic diseases. Methods: Included in this study were 140 patients who had undergone HDCT, consisting of 32 otologic disease patients and 108 non-otologic disease patients, and 280 inner and middle ears and temporal bones were evaluated on a per ear analysis. Signal-to-noise ratios (SNRs) of the temporal bone surrounding the aural vestibule of the ear and in the vestibule as well as the cerebellar hemisphere, overall image and detailed evaluation of the visibility of anatomical landmarks in the middle and inner ear and temporal bone obtained with the two methods were assessed and statistically compared using the paired t-test or Wilcoxon\u2019s signed-rank test. Then, receiver operating characteristic (ROC) analysis was performed to compare diagnostic performance between two reconstruction methods. Results: Each SNR of DLR was significantly higher than that of hybrid-type IR (p &lt; 0.05). Overall image quality and detailed visualization of each anatomical structure obtained with DLR were significantly better than those obtained with hybrid-type IR (p &lt; 0.05). The area under the curve of DLR had no significant difference with hybrid-type IR (p = 0.18). Conclusions: DLR has superior potential to hybrid-type IR for better image quality and visualization of anatomical landmarks in middle and inner ears and temporal bones on HDCT, although diagnostic performance was not affected in clinical practice.<\/jats:p>","DOI":"10.3390\/diagnostics16121756","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T02:32:46Z","timestamp":1780885966000},"page":"1756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Reconstruction Specialized for Inner Ear: Improving Image Quality and Anatomical Structure Visualization as Compared with Conventional Hybrid-Type Iterative Reconstruction on High-Definition CT"],"prefix":"10.3390","volume":"16","author":[{"given":"Masahiko","family":"Nomura","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology, School of Medicine, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Aichi, Japan"},{"name":"Joint Research Laboratory of Advanced Medical Imaging and Artificial Intelligence, School of Medicine, Fujita Health University, Toyoake 470-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirona","family":"Kimata","sequence":"additional","affiliation":[{"name":"Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1292-3166","authenticated-orcid":false,"given":"Yuya","family":"Ito","sequence":"additional","affiliation":[{"name":"Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6603-6362","authenticated-orcid":false,"given":"Kenji","family":"Fujii","sequence":"additional","affiliation":[{"name":"Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7413-2095","authenticated-orcid":false,"given":"Naruomi","family":"Akino","sequence":"additional","affiliation":[{"name":"Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takahiro","family":"Ueda","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, School of Medicine, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Yoshikawa","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, School of Medicine, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Aichi, Japan"},{"name":"Department of Radiology, Fujita Health University Bantane Hospital, Nagoya 454-8509, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9432-7180","authenticated-orcid":false,"given":"Daisuke","family":"Takenaka","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, School of Medicine, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Aichi, Japan"},{"name":"Department of Radiology, Fujita Health University Okazaki Medical Center, Okazaki 444-0827, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshiyuki","family":"Ozawa","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, School of Medicine, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4431-1084","authenticated-orcid":false,"given":"Yoshiharu","family":"Ohno","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, School of Medicine, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Aichi, Japan"},{"name":"Joint Research Laboratory of Advanced Medical Imaging and Artificial Intelligence, School of Medicine, Fujita Health University, Toyoake 470-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.3174\/ajnr.A5768","article-title":"Comparison of a Photon-Counting-Detector CT with an Energy-Integrating-Detector CT for Temporal Bone Imaging: A Cadaveric Study","volume":"39","author":"Zhou","year":"2018","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e15991","DOI":"10.1097\/MD.0000000000015991","article-title":"Evaluation of the depiction ability of the microanatomy of the temporal bone in quarter-detector CT: Model-based iterative reconstruction vs hybrid iterative reconstruction","volume":"98","author":"Kurokawa","year":"2019","journal-title":"Medicine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1007\/s11604-020-00987-5","article-title":"Improved image quality of temporal bone CT with an ultrahigh-resolution CT scanner: Clinical pilot studies","volume":"38","author":"Ohara","year":"2020","journal-title":"Jpn. J. Radiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1148\/radiol.211400","article-title":"Ultra-High-Resolution CT to Detect Intracochlear New Bone Formation after Cochlear Implantation","volume":"302","author":"Heutink","year":"2022","journal-title":"Radiology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1097\/RCT.0000000000001235","article-title":"Ultrahigh-Resolution Computed Tomography Improves Preoperative Computed Tomography Angiography for Deep Inferior Epigastric Artery Perforator Flap Reconstruction","volume":"46","author":"Ide","year":"2022","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1097\/RLI.0000000000000901","article-title":"Spectral Shaping Via Tin Prefiltration in Ultra-High-Resolution Photon-Counting and Energy-Integrating Detector CT of the Temporal Bone","volume":"57","author":"Grunz","year":"2022","journal-title":"Investig. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"W477","DOI":"10.2214\/AJR.11.8275","article-title":"Adaptive iterative dose reduction using 3D processing for reduced- and low-dose pulmonary CT: Comparison with standard-dose CT for image noise reduction and radiological findings","volume":"199","author":"Ohno","year":"2012","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5919","DOI":"10.1118\/1.4930797","article-title":"Influence of radiation dose and reconstruction algorithm in MDCT assessment of airway wall thickness: A phantom study","volume":"42","author":"Nagle","year":"2015","journal-title":"Med. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"772","DOI":"10.2214\/AJR.13.12312","article-title":"Submillisievert chest CT with filtered back projection and iterative reconstruction techniques","volume":"203","author":"Padole","year":"2014","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.crad.2015.04.004","article-title":"Feasibility of radiation dose reduction using AIDR-3D in dynamic pulmonary CT perfusion","volume":"70","author":"Mirsadraee","year":"2015","journal-title":"Clin. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.ejrad.2015.01.025","article-title":"Computer-aided detection of artificial pulmonary nodules using an ex vivo lung phantom: Influence of exposure parameters and iterative reconstruction","volume":"84","author":"Wroblewski","year":"2015","journal-title":"Eur. J. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"809","DOI":"10.3348\/kjr.2018.19.4.809","article-title":"Comparison of Filtered Back Projection, Hybrid Iterative Reconstruction, Model-Based Iterative Reconstruction, and Virtual Monoenergetic Reconstruction Images at Both Low- and Standard-Dose Settings in Measurement of Emphysema Volume and Airway Wall Thickness: A CT Phantom Study","volume":"19","author":"Kim","year":"2018","journal-title":"Korean J. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.3348\/kjr.2018.19.6.1187","article-title":"Impact of Model-Based Iterative Reconstruction on the Correlation between Computed Tomography Quantification of a Low Lung Attenuation Area and Airway Measurements and Pulmonary Function Test Results in Normal Subjects","volume":"19","author":"Kim","year":"2018","journal-title":"Korean J. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1007\/s11604-020-01045-w","article-title":"Deep learning-based and hybrid-type iterative reconstructions for CT: Comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions","volume":"39","author":"Matsukiyo","year":"2021","journal-title":"Jpn. J. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1007\/s11604-023-01470-7","article-title":"Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images","volume":"41","author":"Hamabuchi","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1007\/s11604-025-01781-x","article-title":"Application of a pulmonary nodule detection program using AI technology to ultra-low-dose CT: Differences in detection ability among various image reconstruction methods","volume":"43","author":"Tsuchiya","year":"2025","journal-title":"Jpn. J. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1007\/s00330-018-5810-7","article-title":"The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence","volume":"29","author":"Willemink","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e221257","DOI":"10.1148\/radiol.221257","article-title":"Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects","volume":"306","author":"Koetzier","year":"2023","journal-title":"Radiology"},{"key":"ref_19","unstructured":"Danish Society of Radiology (2025, January 08). European Guidelines on Quality Criteria for Computed Tomography. Available online: https:\/\/op.europa.eu\/en\/publication-detail\/-\/publication\/d229c9e1-a967-49de-b169-59ee68605f1a."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1007\/s00330-014-3406-4","article-title":"Initial results of a new generation dual source CT system using only an in-plane comb filter for ultra-high resolution temporal bone imaging","volume":"25","author":"Meyer","year":"2015","journal-title":"Eur. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.jacr.2020.03.033","article-title":"Early adoption of a certainty scale to improve diagnostic certainty communication","volume":"17","author":"Shinagare","year":"2020","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1016\/j.jacr.2025.07.027","article-title":"Potential Frameworks for Communicating Diagnostic Certainty in Radiology Reports: From the ACR Commission on Quality and Safety","volume":"22","author":"Shinagare","year":"2025","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1111\/j.1699-0463.1989.tb00464.x","article-title":"Reproducibility of histomorphologic diagnoses with special reference to the kappa statistic","volume":"97","author":"Svanholm","year":"1989","journal-title":"APMIS"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1038\/bmt.2012.244","article-title":"Investigation of the freely available easy-to-use software \u2019EZR\u2019 for medical statistics","volume":"48","author":"Kanda","year":"2013","journal-title":"Bone Marrow Transpl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3758\/BF03193146","article-title":"G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences","volume":"39","author":"Faul","year":"2007","journal-title":"Behav. Res. Methods"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/12\/1756\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T04:14:09Z","timestamp":1780978449000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/12\/1756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,6]]},"references-count":25,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["diagnostics16121756"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16121756","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,6]]}}}