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However, the heterogeneity of MRI data across magnetic field strengths and pulse sequences limits the generalizability of existing methods, posing a barrier to clinical translation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to reformulate cranial CT synthesis as a modular, structurally coupled problem using a deep learning approach, enhancing adaptability across heterogeneous MRI conditions, including different field strengths and sequence protocols.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We implemented a cascaded multitask pipeline that jointly models skull segmentation and Hounsfield Unit (HU) regression in anatomically targeted regions. A 3D patch\u2010based training paradigm shifts the modeling focus from global image translation to localized bone feature extraction. The backbone leverages a residual Mamba\u2010based state space model within a 3D U\u2010Net structure to improve spatial representation, while a Transformer U\u2010Net serves as a widely recognized reference baseline comparison. The model was trained and evaluated in terms of multi\u2010modal (T1\u2010weighted and T2\u2010FLAIR) and cross\u2010domain transferability from a public 1.5 Tesla brain dataset (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a037) to an independent 7 Tesla clinical brain dataset (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a044). Performance was assessed using Dice and Jaccard indices for segmentation accuracy and mean absolute error (MAE) for HU regression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Quantitative analysis showed that our multitask pipeline, incorporating both morphological and HU map prediction stages, significantly outperformed conventional direct MRI\u2010to\u2010CT mapping across all metrics on the public 1.5T dataset (p\u00a0&lt;\u00a00.05). Consistent performance on an external 7T clinical dataset (p\u00a0&lt;\u00a00.001) further demonstrated the method's robustness and adaptability across field strengths.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings support that task\u2010structured training for modality transformation markedly improves both accuracy and generalizability of cranial CT synthesis across heterogeneous MRI conditions. The observed consistency across field strengths validates the robustness of the proposed methodology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70429","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T08:16:26Z","timestamp":1777018586000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A proof\u2010of\u2010concept study of multitask learning for cranial synthetic CT generation across heterogeneous MRI field strengths"],"prefix":"10.1002","volume":"53","author":[{"given":"Zhuoyao","family":"Xin","sequence":"first","affiliation":[{"name":"F.M. Kirby Research Center for Brain Imaging Kennedy Krieger Institute  Baltimore Maryland USA"},{"name":"Neurosection Division of MR Research Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine  Baltimore Maryland USA"},{"name":"Department of Biomedical Engineering Johns Hopkins University  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiren","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Case Western Reserve University  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Columbia University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Neuroscience Columbia University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunming","family":"Gu","sequence":"additional","affiliation":[{"name":"F.M. Kirby Research Center for Brain Imaging Kennedy Krieger Institute  Baltimore Maryland USA"},{"name":"Neurosection Division of MR Research Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine  Baltimore Maryland USA"},{"name":"Department of Biomedical Engineering Johns Hopkins University  Baltimore Maryland USA"},{"name":"Department of Radiology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Greco","sequence":"additional","affiliation":[{"name":"Neuroradiology and Neurosurgery Mayo Clinic College of Medicine and Science  Jacksonville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik H.","family":"Middlebrooks","sequence":"additional","affiliation":[{"name":"Neuroradiology and Neurosurgery Mayo Clinic College of Medicine and Science  Jacksonville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Hua","sequence":"additional","affiliation":[{"name":"F.M. Kirby Research Center for Brain Imaging Kennedy Krieger Institute  Baltimore Maryland USA"},{"name":"Neurosection Division of MR Research Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Columbia University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.4103\/0973\u20101482.34694"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.01.065"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4865778"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/59\/13\/3599"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10334\u2010012\u20100353\u20104"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259\u2010020\u201005061\u2010w"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17867"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/58\/23\/8419"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2019.06.001"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020201537"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.969463"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.71.845.9691900"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010016\u20100747\u2010y"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/aac71a"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/acefa3"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.28067"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.10.5.055001"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.4081\/oncol.2017.321"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010022\u201008568\u2010y"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.10.002"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.08.043"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2021.07.027"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12155"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.988800"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.467683"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16847"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16556"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.541663"},{"key":"e_1_2_10_30_1","unstructured":"CaoA LiZ JomskyJ LaineAF GuoJ.MedSegMamba: 3D CNN\u2010mamba hybrid architecture for brain segmentation. arXiv. 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