{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T02:55:19Z","timestamp":1791082519665,"version":"4.1.0"},"posted":{"date-parts":[[2026,9,29]]},"group-title":"Bioengineering","reference-count":41,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["DP1DK139798-02"],"award-info":[{"award-number":["DP1DK139798-02"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>To determine whether structural MRI information can improve reconstruction of highly accelerated 3D snapshot chemical exchange saturation transfer (CEST) MRI.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>\n                    Fully sampled brain CEST data were retrospectively undersampled at acceleration factors (AFs) of 4, 6, 8, 10, and 12. We compared reconstruction without structural information with population-level pretraining using T1-weighted (T1w), T2-weighted (T2w), or combined T1w\u2013T2w images, as well as direct conditioning using a co-registered subject-specific T1w image. Performance was evaluated in two held-out healthy subjects using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE) across frequency offsets. Z-spectrum fidelity and derived magnetization transfer ratio asymmetry (MTR\n                    <jats:sub>asym<\/jats:sub>\n                    ) maps were additionally evaluated within three-dimensional gray- and white-matter masks.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    Subject-specific T1w conditioning consistently achieved the highest PSNR and SSIM and the lowest MAE across AFs, with greater improvements at higher acceleration. It also improved Z-spectrum agreement, particularly in white matter and combined gray- and white-matter regions across most AFs. Improvements in MTR\n                    <jats:sub>asym<\/jats:sub>\n                    were more modest and varied across tissue types and acceleration factors.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Incorporating subject-specific structural information can improve reconstruction of highly accelerated snapshot CEST MRI, particularly at higher acceleration factors. However, improvements in reconstructed source images did not consistently translate into better preservation of derived CEST contrast, highlighting the need for reconstruction strategies that more directly incorporate CEST spectral information.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.09.28.755205","type":"posted-content","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:15:25Z","timestamp":1790698525000},"source":"Crossref","is-referenced-by-count":0,"title":["Anatomically Guided Deep Learning Reconstruction of Accelerated Snapshot CEST MRI"],"prefix":"10.64898","author":[{"given":"Emmanuel Akwasi","family":"Mensah","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, University of Rochester, Rochester, NY 14627, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abrar","family":"Faiyaz","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Rochester, Rochester, NY 14642, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Schifitto","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Rochester, Rochester, NY 14642, USA"},{"name":"Department of Electrical & Computer Engineering, University of Rochester, Rochester, NY"},{"name":"Department of Imaging Sciences, University of Rochester, Rochester, NY 14642, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Nasir","family":"Uddin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Rochester, Rochester, NY 14627, USA"},{"name":"Department of Neurology, University of Rochester, Rochester, NY 14642, USA"},{"name":"Department of Electrical & Computer Engineering, University of Rochester, Rochester, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026100319450741000_2026.09.28.755205v1.1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22761"},{"issue":"22","key":"2026100319450741000_2026.09.28.755205v1.2","doi-asserted-by":"crossref","first-page":"11059","DOI":"10.3390\/ijms262211059","article-title":"Chemical Exchange Saturation Transfer Imaging in Neuroinflammation: Methods, Challenges, and Recommendations","volume":"26","year":"2025","journal-title":"International Journal of Molecular Sciences"},{"issue":"1","key":"2026100319450741000_2026.09.28.755205v1.3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/jmri.25838","article-title":"Clinical applications of chemical exchange saturation transfer (CEST) MRI","volume":"47","year":"2018","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"2026100319450741000_2026.09.28.755205v1.4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/jmri.25838","article-title":"Clinical applications of chemical exchange saturation transfer (CEST) MRI","volume":"47","year":"2018","journal-title":"Journal of Magnetic Resonance Imaging"},{"issue":"11","key":"2026100319450741000_2026.09.28.755205v1.5","doi-asserted-by":"crossref","first-page":"e4789","DOI":"10.1002\/nbm.4789","article-title":"Theory of chemical exchange saturation transfer MRI in the context of different magnetic fields","volume":"35","year":"2022","journal-title":"NMR Biomed"},{"issue":"1","key":"2026100319450741000_2026.09.28.755205v1.6","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1002\/mrm.29842","article-title":"Dual contrast CEST MRI for pH-weighted imaging in stroke","volume":"91","year":"2024","journal-title":"Magn Reson Med"},{"issue":"6","key":"2026100319450741000_2026.09.28.755205v1.7","doi-asserted-by":"crossref","first-page":"e4944","DOI":"10.1002\/nbm.4944","article-title":"Radiofrequency labeling strategies in chemical exchange saturation transfer MRI","volume":"36","year":"2023","journal-title":"NMR Biomed"},{"key":"2026100319450741000_2026.09.28.755205v1.8","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/58\/22\/R221"},{"key":"2026100319450741000_2026.09.28.755205v1.9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmr.2011.05.001"},{"issue":"1","key":"2026100319450741000_2026.09.28.755205v1.10","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s40658-016-0155-2","article-title":"An overview of CEST MRI for non-MR physicists","volume":"3","year":"2016","journal-title":"EJNMMI Phys"},{"issue":"6","key":"2026100319450741000_2026.09.28.755205v1.11","doi-asserted-by":"crossref","first-page":"e4699","DOI":"10.1002\/nbm.4699","article-title":"Acquisition sequences and reconstruction methods for fast chemical exchange saturation transfer imaging","volume":"36","year":"2023","journal-title":"NMR Biomed"},{"issue":"4","key":"2026100319450741000_2026.09.28.755205v1.12","doi-asserted-by":"crossref","first-page":"e3879","DOI":"10.1002\/nbm.3879","article-title":"Snapshot-CEST: Optimizing spiral-centric-reordered gradient echo acquisition for fast and robust 3D CEST MRI at 9.4 T","volume":"31","year":"2018","journal-title":"NMR Biomed"},{"issue":"10","key":"2026100319450741000_2026.09.28.755205v1.13","doi-asserted-by":"crossref","first-page":"e4955","DOI":"10.1002\/nbm.4955","article-title":"Snapshot CEST++: Advancing rapid whole-brain APTw-CEST MRI at 3 T","volume":"36","year":"2023","journal-title":"NMR Biomed"},{"issue":"2","key":"2026100319450741000_2026.09.28.755205v1.14","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1002\/mrm.26141","article-title":"Accelerating chemical exchange saturation transfer (CEST) MRI by combining compressed sensing and sensitivity encoding techniques","volume":"77","year":"2017","journal-title":"Magnetic resonance in medicine"},{"issue":"6","key":"2026100319450741000_2026.09.28.755205v1.15","first-page":"2616","article-title":"Accelerating multipool CEST MRI of Parkinson\u2019s disease using deep learning\u2013based Z\u2010spectral compressed sensing","volume":"92","year":"2024","journal-title":"Magnetic Resonance in Medicine"},{"issue":"4","key":"2026100319450741000_2026.09.28.755205v1.16","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1002\/mrm.70172","article-title":"Accelerated Chemical Exchange Saturation Transfer Imaging With Deep Unrolling Networks and Synthetic Brain Tumor Datasets","volume":"95","year":"2026","journal-title":"Magn Reson Med"},{"key":"2026100319450741000_2026.09.28.755205v1.17","doi-asserted-by":"crossref","unstructured":"Pan, S.Q. , et al., Artificial intelligence in chemical exchange saturation transfer magnetic resonance imaging. Artificial Intelligence Review, 2025. 58(7).","DOI":"10.1007\/s10462-025-11227-5"},{"issue":"2","key":"2026100319450741000_2026.09.28.755205v1.18","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1002\/mrm.30089","article-title":"Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction","volume":"92","year":"2024","journal-title":"Magn Reson Med"},{"key":"2026100319450741000_2026.09.28.755205v1.19","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26977"},{"key":"2026100319450741000_2026.09.28.755205v1.20","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21391"},{"issue":"3","key":"2026100319450741000_2026.09.28.755205v1.21","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1002\/mp.14006","article-title":"Reconstruction of multicontrast MR images through deep learning","volume":"47","year":"2020","journal-title":"Med Phys"},{"issue":"3","key":"2026100319450741000_2026.09.28.755205v1.22","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1137\/15M1047325","article-title":"Multicontrast MRI Reconstruction with Structure-Guided Total Variation","volume":"9","year":"2016","journal-title":"SIAM Journal on Imaging Sciences"},{"key":"2026100319450741000_2026.09.28.755205v1.23","doi-asserted-by":"crossref","unstructured":"Wang, P. , et al. Improving amide proton transfer-weighted mri reconstruction using t2-weighted images. in International Conference on Medical Image Computing and Computer-Assisted Intervention. 2020. Springer.","DOI":"10.1007\/978-3-030-59713-9_1"},{"issue":"8","key":"2026100319450741000_2026.09.28.755205v1.24","doi-asserted-by":"crossref","first-page":"e5130","DOI":"10.1002\/nbm.5130","article-title":"Deep-learning-based super-resolution for accelerating chemical exchange saturation transfer MRI","volume":"37","year":"2024","journal-title":"NMR Biomed"},{"key":"2026100319450741000_2026.09.28.755205v1.25","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910150117"},{"key":"2026100319450741000_2026.09.28.755205v1.26","unstructured":"Uecker, M. , et al. Berkeley advanced reconstruction toolbox. in Proc. Intl. Soc. Mag. Reson. Med. 2015. ISMRM."},{"key":"2026100319450741000_2026.09.28.755205v1.27","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24751"},{"key":"2026100319450741000_2026.09.28.755205v1.28","doi-asserted-by":"publisher","DOI":"10.1016\/S1053-8119(02)91132-8"},{"key":"2026100319450741000_2026.09.28.755205v1.29","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2662206"},{"key":"2026100319450741000_2026.09.28.755205v1.30","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181cb3e25"},{"key":"2026100319450741000_2026.09.28.755205v1.31","unstructured":"Initiative, A.s.D.N. , Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). Laboratory of Neuro Imaging, USC, 2024."},{"key":"2026100319450741000_2026.09.28.755205v1.32","doi-asserted-by":"publisher","DOI":"10.1002\/cmmi.240"},{"key":"2026100319450741000_2026.09.28.755205v1.33","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.29241"},{"key":"2026100319450741000_2026.09.28.755205v1.34","doi-asserted-by":"publisher","DOI":"10.1109\/42.906424"},{"key":"2026100319450741000_2026.09.28.755205v1.35","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.21041"},{"issue":"10","key":"2026100319450741000_2026.09.28.755205v1.36","doi-asserted-by":"crossref","first-page":"e110803","DOI":"10.1371\/journal.pone.0110803","article-title":"Optimal brain MRI protocol for new neurological complaint","volume":"9","year":"2014","journal-title":"PLoS One"},{"issue":"8","key":"2026100319450741000_2026.09.28.755205v1.37","doi-asserted-by":"crossref","first-page":"4636","DOI":"10.1109\/JBHI.2024.3404225","article-title":"Attention-Based MultiOffset Deep Learning Reconstruction of Chemical Exchange Saturation Transfer (AMO-CEST) MRI","volume":"28","year":"2024","journal-title":"IEEE J Biomed Health Inform"},{"issue":"12","key":"2026100319450741000_2026.09.28.755205v1.38","doi-asserted-by":"crossref","first-page":"e70180","DOI":"10.1002\/nbm.70180","article-title":"k-Space and Image Domain Hybrid Reconstruction Using Low-Rank Plus Sparse Model (KILS) for Accelerated CEST MRI","volume":"38","year":"2025","journal-title":"NMR Biomed"},{"issue":"9","key":"2026100319450741000_2026.09.28.755205v1.39","doi-asserted-by":"crossref","first-page":"e4360","DOI":"10.1002\/nbm.4360","article-title":"Accelerating CEST MRI in the mouse brain at 9.4 T by exploiting sparsity in the Z-spectrum domain","volume":"33","year":"2020","journal-title":"NMR Biomed"},{"issue":"3","key":"2026100319450741000_2026.09.28.755205v1.40","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1002\/mrm.28219","article-title":"Joint multi-contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging","volume":"84","year":"2020","journal-title":"Magn Reson Med"},{"key":"2026100319450741000_2026.09.28.755205v1.41","doi-asserted-by":"crossref","unstructured":"Zhou, B. and S.K. Zhou , DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction With Deep T1 Prior, in 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020. p. 4272\u20134281.","DOI":"10.1109\/CVPR42600.2020.00433"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.09.28.755205","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T02:45:19Z","timestamp":1791081919000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.64898\/2026.09.28.755205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,29]]},"references-count":41,"URL":"https:\/\/doi.org\/10.64898\/2026.09.28.755205","relation":{},"subject":[],"published":{"date-parts":[[2026,9,29]]},"subtype":"preprint"}}