{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T10:55:40Z","timestamp":1790765740185,"version":"4.1.0"},"reference-count":31,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Vet Radiology Ultrasound"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Obtaining high\u2010resolution brain MRI in small animals is challenging due to the inherent trade\u2010off between image quality and acquisition time. Deep learning\u2010based reconstruction (DLR) has emerged as a solution to improve image quality without prolonging scan time; however, its clinical utility and potential risks, such as pseudolesions, remain insufficiently evaluated in veterinary patients. This study aimed to evaluate the impact of DLR on lesion assessment and image quality in canine brain MRI and to investigate potential limitations associated with its application. This retrospective study compared DLR with conventional reconstruction in 25 canine brain MRI examinations diagnosed with intracranial diseases. Transverse T2\u2010weighted and fluid\u2010attenuated inversion recovery sequences were reconstructed using both conventional and DLR methods. Quantitative analysis included signal\u2010to\u2010noise ratio (SNR), contrast\u2010to\u2010noise ratio (CNR), and lesion edge sharpness. Two blinded radiologists assessed image quality, lesion conspicuity, artifacts, pseudolesions, and lesion masking. DLR images exhibited significantly higher SNR, CNR, and lesion edge sharpness than conventional images in both sequences (\n                    <jats:italic>p<\/jats:italic>\n                    \u00a0&lt;\u00a00.05). DLR images also yielded significantly higher qualitative scores for image quality and lesion conspicuity (\n                    <jats:italic>p<\/jats:italic>\n                    \u00a0&lt;\u00a00.05). Truncation artifact was significantly reduced, whereas motion and pulsation artifacts showed no improvement. No instances of pseudolesions or lesion masking were noted. DLR significantly improves image quality and lesion conspicuity in canine brain MRI and reduces truncation artifacts; however, it does not mitigate motion or pulsation interference. Collectively, these findings suggest that DLR has potential value for improving image quality and lesion visibility in canine brain MRI, whereas further studies are needed to determine its impact on diagnostic performance.\n                  <\/jats:p>","DOI":"10.1111\/vru.70260","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T09:59:03Z","timestamp":1790762343000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of Intracranial Lesions Using Deep Learning\u2010Based Reconstruction in Canine Brain MRI: Comparison With Conventional Reconstruction on T2\u2010Weighted and FLAIR Sequences"],"prefix":"10.1111","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2841-9236","authenticated-orcid":false,"given":"Wooseok","family":"Jin","sequence":"first","affiliation":[{"name":"College of Veterinary Medicine Kyungpook National University  Daegu Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyemin","family":"Na","sequence":"additional","affiliation":[{"name":"College of Veterinary Medicine Kyungpook National University  Daegu Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3097-0345","authenticated-orcid":false,"given":"Sang\u2010Kwon","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Veterinary Medicine Kyungpook National University  Daegu Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7167-0755","authenticated-orcid":false,"given":"Hojung","family":"Choi","sequence":"additional","affiliation":[{"name":"College of Veterinary Medicine Chungnam National University  Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3207-0989","authenticated-orcid":false,"given":"Youngwon","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Veterinary Medicine Chungnam National University  Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4649-809X","authenticated-orcid":false,"given":"Kija","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Veterinary Medicine Kyungpook National University  Daegu Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"R. M.Lebel \u201cPerformance Characterization of a Novel Deep Learning\u2010Based MR Image Reconstruction Pipeline \u201d preprint arXiv: 2008.06559 August 14 2020."},{"key":"e_1_2_10_3_1","volume-title":"The Clinical Benefits of AIR Recon DL for MR Image Reconstruction","author":"Peters R. D.","year":"2020"},{"issue":"5","key":"e_1_2_10_4_1","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1111\/vru.13279","article-title":"Deep Learning\u2010Based Reconstruction for Canine Brain Magnetic Resonance Imaging Could Improve Image Quality While Reducing Scan Time","volume":"64","author":"Choi H.","year":"2023","journal-title":"Veterinary Radiology & Ultrasound"},{"issue":"6","key":"e_1_2_10_5_1","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1111\/vru.13298","article-title":"Deep Learning\u2010Based Reconstruction Can Improve Canine Thoracolumbar Magnetic Resonance Image Quality and Reduce Slice Thickness","volume":"64","author":"Kang H.","year":"2023","journal-title":"Veterinary Radiology & Ultrasound"},{"issue":"6","key":"e_1_2_10_6_1","doi-asserted-by":"crossref","DOI":"10.1148\/rg.220133","article-title":"Clinical Impact of Deep Learning Reconstruction in MRI","volume":"43","author":"Kiryu S.","year":"2023","journal-title":"Radiographics"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","first-page":"443","DOI":"10.5114\/pjr\/192822","article-title":"Deep Learning and AI in Reducing Magnetic Resonance Imaging Scanning Time: Advantages and Pitfalls in Clinical Practice","volume":"89","author":"Foti G.","year":"2024","journal-title":"Polish Journal of Radiology"},{"issue":"3","key":"e_1_2_10_8_1","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1148\/radiol.2018180547","article-title":"Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide","volume":"290","author":"Soffer S.","year":"2019","journal-title":"Radiology"},{"issue":"1","key":"e_1_2_10_9_1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions","volume":"8","author":"Alzubaidi L.","year":"2021","journal-title":"Journal of Big Data"},{"issue":"4","key":"e_1_2_10_10_1","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1111\/1754-9485.13649","article-title":"Deep Learning Denoising Reconstruction Enables Faster T2\u2010Weighted FLAIR Sequence Acquisition With Satisfactory Image Quality","volume":"68","author":"Brain M. E.","year":"2024","journal-title":"Journal of Medical Imaging and Radiation Oncology"},{"issue":"1","key":"e_1_2_10_11_1","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.acra.2023.05.010","article-title":"Deep Learning Accelerated Image Reconstruction of Fluid\u2010Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality","volume":"31","author":"Estler A.","year":"2024","journal-title":"Academic Radiology"},{"issue":"4","key":"e_1_2_10_12_1","doi-asserted-by":"crossref","first-page":"374","DOI":"10.3348\/kjr.2023.0842","article-title":"Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning\u2010Based Image Reconstruction in Patients With Suspected Focal Epilepsy","volume":"25","author":"Suh P. S.","year":"2024","journal-title":"Korean Journal of Radiology"},{"issue":"10","key":"e_1_2_10_13_1","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1177\/02841851241273114","article-title":"Assessment of Multi\u2010Modal Magnetic Resonance Imaging for Glioma Based on a Deep Learning Reconstruction Approach With the Denoising Method","volume":"65","author":"Sun J.","year":"2024","journal-title":"Acta Radiologica"},{"issue":"1","key":"e_1_2_10_14_1","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.asjsur.2024.09.156","article-title":"Deep Learning\u2010Based Reconstruction Enhanced Image Quality and Lesion Detection of White Matter Hyperintensity Through in FLAIR MRI","volume":"48","author":"Sun J. P.","year":"2025","journal-title":"Asian Journal of Surgery"},{"issue":"1","key":"e_1_2_10_15_1","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s00234-022-03053-1","article-title":"Deep Learning Reconstruction in Pediatric Brain MRI: Comparison of Image Quality With Conventional T2\u2010Weighted MRI","volume":"65","author":"Kim S.\u2010H.","year":"2023","journal-title":"Neuroradiology"},{"issue":"1","key":"e_1_2_10_16_1","doi-asserted-by":"crossref","first-page":"5846","DOI":"10.1038\/s41598-025-88822-7","article-title":"Accelerating Veterinary Low Field MRI Acquisitions Using the Deep Learning Based Denoising Solution HawkAI","volume":"15","author":"Nour Eddin J.","year":"2025","journal-title":"Scientific Reports"},{"issue":"3","key":"e_1_2_10_17_1","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1111\/vru.12608","article-title":"Consensus Recommendations on Standardized Magnetic Resonance Imaging Protocols for Multicenter Canine Brain Tumor Clinical Trials","volume":"59","author":"Packer R. 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A.","year":"2024","journal-title":"Topics in Magnetic Resonance Imaging"},{"issue":"11","key":"e_1_2_10_20_1","doi-asserted-by":"crossref","first-page":"7364","DOI":"10.1007\/s00330-024-10771-y","article-title":"Assessing Deep Learning Reconstruction for Faster Prostate MRI: Visual vs. Diagnostic Performance Metrics","volume":"34","author":"Lohuizen Q.","year":"2024","journal-title":"European Radiology"},{"key":"e_1_2_10_21_1","doi-asserted-by":"crossref","DOI":"10.1111\/vru.70015","article-title":"A Veterinary DICOM\u2010Based Deep Learning Denoising Algorithm Can Improve Subjective and Objective Brain MRI Image Quality","volume":"66","author":"Mai W.","year":"2025","journal-title":"Veterinary Radiology & Ultrasound"},{"issue":"1","key":"e_1_2_10_22_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/TMI.2020.3025065","article-title":"Uncertainty Quantification in Deep MRI Reconstruction","volume":"40","author":"Edupuganti V.","year":"2021","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"12","key":"e_1_2_10_23_1","doi-asserted-by":"crossref","first-page":"5118","DOI":"10.1016\/j.acra.2024.06.010","article-title":"Super\u2010Resolution Deep Learning Reconstruction for 3D Brain MR Imaging: Improvement of Cranial Nerve Depiction and Interobserver Agreement in Evaluations of Neurovascular Conflict","volume":"31","author":"Yasaka K.","year":"2024","journal-title":"Academic Radiology"},{"issue":"1","key":"e_1_2_10_24_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3174\/ajnr.A8482","article-title":"Deep Learning\u2013Based Super\u2010Resolution Reconstruction on Undersampled Brain Diffusion\u2010Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction","volume":"46","author":"Zhang S.","year":"2025","journal-title":"American Journal of Neuroradiology"},{"issue":"3","key":"e_1_2_10_25_1","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1002\/cmr.a.21339","article-title":"Edge Sharpness Assessment by Parametric Modeling: Application to Magnetic Resonance Imaging","volume":"44","author":"Ahmad R.","year":"2015","journal-title":"Concepts in Magnetic Resonance Part A"},{"issue":"1","key":"e_1_2_10_26_1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.acra.2022.03.018","article-title":"Comprehensive Clinical Evaluation of a Deep Learning\u2010Accelerated, Single\u2010Breath\u2010Hold Abdominal HASTE at 1.5 T and 3 T","volume":"30","author":"Herrmann J.","year":"2023","journal-title":"Academic Radiology"},{"issue":"2","key":"e_1_2_10_27_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1097\/RLI.0000000000001110","article-title":"Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath\u2010Hold Examination With Dixon Fat Suppression","volume":"60","author":"Chaika M.","year":"2025","journal-title":"Investigative Radiology"},{"issue":"2","key":"e_1_2_10_28_1","doi-asserted-by":"crossref","first-page":"180","DOI":"10.3348\/kjr.2024.0701","article-title":"Evaluation of Image Quality and Scan Time Efficiency in Accelerated 3D T1\u2010Weighted Pediatric Brain MRI Using Deep Learning\u2010Based Reconstruction","volume":"26","author":"Yoo H.","year":"2025","journal-title":"Korean Journal of Radiology"},{"issue":"1","key":"e_1_2_10_29_1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0278668","article-title":"Motion Artifact Reduction for Magnetic Resonance Imaging With Deep Learning and K\u2010Space Analysis","volume":"18","author":"Cui L.","year":"2023","journal-title":"PLoS ONE"},{"issue":"4","key":"e_1_2_10_30_1","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1002\/mp.16119","article-title":"Deep Learning\u2010Based Motion Quantification From K\u2010Space for Fast Model\u2010Based Magnetic Resonance Imaging Motion Correction","volume":"50","author":"Hossbach J.","year":"2023","journal-title":"Medical Physics"},{"issue":"11","key":"e_1_2_10_31_1","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.3174\/ajnr.A7664","article-title":"Accelerated Synthetic MRI With Deep Learning\u2013Based Reconstruction for Pediatric Neuroimaging","volume":"43","author":"Kim E.","year":"2022","journal-title":"American Journal of Neuroradiology"},{"issue":"2","key":"e_1_2_10_32_1","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.231938","article-title":"Ultrafast Brain MRI With Deep Learning Reconstruction for Suspected Acute Ischemic Stroke","volume":"310","author":"Altmann S.","year":"2024","journal-title":"Radiology"}],"container-title":["Veterinary Radiology &amp; Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/vru.70260","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/vru.70260","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/vru.70260","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T09:59:08Z","timestamp":1790762348000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/vru.70260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,30]]},"references-count":31,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["10.1111\/vru.70260"],"URL":"https:\/\/doi.org\/10.1111\/vru.70260","archive":["Portico"],"relation":{},"ISSN":["1058-8183","1740-8261"],"issn-type":[{"value":"1058-8183","type":"print"},{"value":"1740-8261","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,30]]},"assertion":[{"value":"2026-05-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-18","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70260"}}