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dc.contributor.authorFu, Zhiyang
dc.contributor.authorMandava, Sagar
dc.contributor.authorKeerthivasan, Mahesh B
dc.contributor.authorLi, Zhitao
dc.contributor.authorJohnson, Kevin
dc.contributor.authorMartin, Diego R
dc.contributor.authorAltbach, Maria I
dc.contributor.authorBilgin, Ali
dc.date.accessioned2020-11-19T20:16:47Z
dc.date.available2020-11-19T20:16:47Z
dc.date.issued2020-09-01
dc.identifier.citationFu, Z., Mandava, S., Keerthivasan, M. B., Li, Z., Johnson, K., Martin, D. R., ... & Bilgin, A. (2020). A multi-scale residual network for accelerated radial MR parameter mapping. Magnetic Resonance Imaging, 73, 152-162.en_US
dc.identifier.issn0730-725X
dc.identifier.pmid32882339
dc.identifier.doi10.1016/j.mri.2020.08.013
dc.identifier.urihttp://hdl.handle.net/10150/648560
dc.description.abstractA deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches. For each anatomy and relaxation parameter, an individual network is trained. in vivo T1 mapping results are obtained on brain and abdomen datasets and in vivo T2 mapping results are obtained on brain and knee datasets. Quantitative results for the T2 mapping of the knee show that MS-ResNet trained using either fully sampled or undersampled data outperforms conventional model-based compressed sensing methods. This is significant because obtaining fully sampled training data is not possible in many applications. in vivo brain and abdomen results for T1 mapping and in vivo brain results for T2 mapping demonstrate that MS-ResNet yields contrast-weighted images and parameter maps that are comparable to those achieved by model-based iterative methods while offering two orders of magnitude reduction in reconstruction times. The proposed approach enables recovery of high-quality contrast-weighted images and parameter maps from highly accelerated radial data acquisitions. The rapid image reconstructions enabled by the proposed approach makes it a good candidate for routine clinical use.en_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.rightsCopyright © 2020 Elsevier Inc. All rights reserved.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learningen_US
dc.subjectimage reconstructionen_US
dc.subjectMulti-contrast imagingen_US
dc.subjectT(1) mappingen_US
dc.subjectT(2) mappingen_US
dc.titleA multi-scale residual network for accelerated radial MR parameter mappingen_US
dc.typeArticleen_US
dc.identifier.eissn1873-5894
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen_US
dc.contributor.departmentUniv Arizona, Dept Med Imagingen_US
dc.contributor.departmentUniv Arizona, Dept Biomed Engnen_US
dc.identifier.journalMAGNETIC RESONANCE IMAGINGen_US
dc.description.note12 month embargo; available online 1 September 2020en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleMagnetic resonance imaging
dc.source.volume73
dc.source.beginpage152
dc.source.endpage162
dc.source.countryUnited States
dc.source.countryNetherlands


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