{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T23:48:46Z","timestamp":1789688926974,"version":"4.0.1"},"reference-count":17,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T00:00:00Z","timestamp":1744934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Eliminating the need for a preoperative spine CT scan prior to spine surgery offers significant financial advantages, reduced radiation exposure, and a more streamlined workflow. This can be achieved by converting a standard-protocol MRI scan into a synthetic CT (sCT), which provides a precise representation of bone structures. The sCT is intended for use in the preoperative planning and navigation of spine surgeries, eliminating the need for an additional CT scan. Methods: The transformation is based on a neural network architecture that converts MRI data into high-resolution sCT images. Although the resolution of standard magnetic resonance imaging ranges from 3 to 5 mm, sCT achieves sub-millimeter accuracy (below 1 mm). In this study, we present the results of the generation of sCT and compare them with conventional CT scans of the same patients. Results: A comprehensive comparison was conducted using both 3D and 2D measurements on 500 different vertebrae. The 3D evaluation used 3D surface distance measurement to assess the full vertebrae and key anatomical elements. The 2D analysis focused on critical distances that define the vertebral body, pedicles, and spinous processes. All measurements were performed automatically and validated by orthopedic surgeons, ensuring clinical relevance. Conclusions: This study shows that converting a standard-protocol MRI scan into an sCT provides a precise representation of bone structures.<\/jats:p>","DOI":"10.3390\/jcm14082809","type":"journal-article","created":{"date-parts":[[2025,4,20]],"date-time":"2025-04-20T20:24:16Z","timestamp":1745180656000},"page":"2809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Generating a Synthetic Lumbar CT from a Standard MRI Protocol"],"prefix":"10.3390","volume":"14","author":[{"given":"Yehiel","family":"Shilo","sequence":"first","affiliation":[{"name":"MRI2CT, Jerusalem 9463121, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank M.","family":"Phillips","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy F.","family":"Witham","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kornelis","family":"Poelstra","sequence":"additional","affiliation":[{"name":"The Robotic Spine Institute of Las Vegas, Nevada Spine Clinic, Las Vegas, NV 89128, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lital","family":"Hodeda","sequence":"additional","affiliation":[{"name":"MRI2CT, Jerusalem 9463121, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6630-765X","authenticated-orcid":false,"given":"Moshe","family":"Stavsky","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Shaare Zedek Medical Center, Facularty of Medicine, Hebrew University, Jerusalem 91120, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.mric.2011.05.013","article-title":"Normal spinal anatomy on magnetic resonance imaging","volume":"19","author":"Jindal","year":"2011","journal-title":"Magn. Reson. Imaging Clin."},{"key":"ref_2","first-page":"S18","article-title":"Artificial Intelligence and Machine Learning in Spine Surgery","volume":"17","author":"Wolf","year":"2023","journal-title":"Contemp. Spine Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.nic.2019.08.001","article-title":"Spine anatomy imaging: An update","volume":"29","author":"Shanechi","year":"2019","journal-title":"Neuroimaging Clin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1177\/15563316211039509","article-title":"Computed tomography and magnetic resonance imaging overlay in the spine for surgical planning: A technical report","volume":"18","author":"Perez","year":"2022","journal-title":"HSS J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"S39","DOI":"10.1093\/ons\/opaa324","article-title":"Emerging technologies in spinal surgery: Ultra-low radiation imaging platforms","volume":"21","author":"Pacult","year":"2021","journal-title":"Oper. Neurosurg."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1097\/00007632-199607010-00011","article-title":"Reducing the lifetime risk of cancer from spinal radiographs among people with adolescent idiopathic scoliosis","volume":"21","author":"Levy","year":"1996","journal-title":"Spine"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wilson, J.P., Fontenot, L., Stewart, C., Kumbhare, D., Guthikonda, B., and Hoang, S. (2024). Image-guided navigation in spine surgery: From historical developments to future perspectives. J. Clin. Med., 13.","DOI":"10.3390\/jcm13072036"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s00264-009-0817-y","article-title":"The role of routine magnetic resonance imaging in the preoperative evaluation of adolescent idiopathic scoliosis","volume":"34","author":"Ozturk","year":"2010","journal-title":"Int. Orthop."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.ejmp.2021.07.027","article-title":"Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review","volume":"89","author":"Boulanger","year":"2021","journal-title":"Phys. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109999","DOI":"10.1016\/j.ejrad.2021.109999","article-title":"MRI-based synthetic CT of the lumbar spine: Geometric measurements for surgery planning in comparison with CT","volume":"144","author":"Chen","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1002\/mp.16847","article-title":"Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model","volume":"51","author":"Pan","year":"2024","journal-title":"Med Phys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1111\/joa.12451","article-title":"Morphological and postural sexual dimorphism of the lumbar spine facilitates greater lordosis in females","volume":"229","author":"Bailey","year":"2016","journal-title":"J. Anat."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1016\/j.spinee.2022.06.001","article-title":"Spondylophyte classification based on biomechanical effects on segmental stiffness","volume":"22","author":"Farshad","year":"2022","journal-title":"Spine J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1097\/00007632-200102150-00010","article-title":"Accuracy requirements for image-guided spinal pedicle screw placement","volume":"26","author":"Rampersaud","year":"2001","journal-title":"Spine"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1007\/s00586-015-3853-x","article-title":"Methods to determine pedicle screw placement accuracy in spine surgery: A systematic review","volume":"24","author":"Aoude","year":"2015","journal-title":"Eur. Spine J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Teixeira, P.A.G., Kessler, H., Morb\u00e9e, L., Douis, N., Boubaker, F., Gillet, R., and Blum, A. (Diagn. Interv. Imaging, 2024). Mineralized tissue visualization with MRI: Practical insights and recommendations for optimized clinical applications, Diagn. Interv. Imaging, in press.","DOI":"10.1016\/j.diii.2024.11.001"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"CASE23120","DOI":"10.3171\/CASE23120","article-title":"Robot-assisted screw fixation in a cadaver utilizing magnetic resonance imaging\u2013based synthetic computed tomography: Toward radiation-free spine surgery. Illustrative case","volume":"6","author":"Davidar","year":"2023","journal-title":"J. Neurosurg. 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