{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:25:27Z","timestamp":1783117527912,"version":"3.54.6"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["G-SACF"],"award-info":[{"award-number":["G-SACF"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["SGDX20230821092359002"],"award-info":[{"award-number":["SGDX20230821092359002"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.eswa.2026.132866","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:25:15Z","timestamp":1778757915000},"page":"132866","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MF2MR2: Multi-frequency fusion for accelerated multi-contrast MRI reconstruction"],"prefix":"10.1016","volume":"328","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5845-9899","authenticated-orcid":false,"given":"Jing","family":"Zou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7838-7101","authenticated-orcid":false,"given":"Lanqing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9992-3387","authenticated-orcid":false,"given":"Xiaohan","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8878-0325","authenticated-orcid":false,"given":"Angelica I.","family":"Aviles-Rivero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1495-3278","authenticated-orcid":false,"given":"Shujun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7059-0929","authenticated-orcid":false,"given":"Jing","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.eswa.2026.132866_bib0001","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1002\/jmri.1183","article-title":"Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy","volume":"14","author":"Bernstein","year":"2001","journal-title":"JMRI"},{"issue":"6","key":"10.1016\/j.eswa.2026.132866_bib0002","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1002\/mrm.22956","article-title":"Multi-contrast reconstruction with Bayesian compressed sensing","volume":"66","author":"Bilgic","year":"2011","journal-title":"MRM"},{"issue":"2","key":"10.1016\/j.eswa.2026.132866_bib0003","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1002\/mrm.27076","article-title":"Improving parallel imaging by jointly reconstructing multi-contrast data","volume":"80","author":"Bilgic","year":"2018","journal-title":"MRM"},{"key":"10.1016\/j.eswa.2026.132866_bib0004","unstructured":"Brain-Development (2019). Brain-development IXI dataset. Accessed: 2025-04. https:\/\/brain-development.org\/ixi-dataset\/."},{"issue":"6","key":"10.1016\/j.eswa.2026.132866_bib0005","doi-asserted-by":"crossref","first-page":"3705","DOI":"10.1002\/mrm.27694","article-title":"High-dimensionality undersampled patch-based reconstruction (HD-PROST) for accelerated multi-contrast MRI","volume":"81","author":"Bustin","year":"2019","journal-title":"MRM"},{"key":"10.1016\/j.eswa.2026.132866_bib0006","series-title":"MICCAI","article-title":"Accelerated multi-contrast MRI reconstruction via frequency and spatial mutual learning","author":"Chen","year":"2024"},{"issue":"11","key":"10.1016\/j.eswa.2026.132866_bib0007","doi-asserted-by":"crossref","first-page":"6751","DOI":"10.1109\/JBHI.2024.3432139","article-title":"FEFA: Frequency enhanced multi-modal MRI reconstruction with deep feature alignment","volume":"28","author":"Chen","year":"2024","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"3","key":"10.1016\/j.eswa.2026.132866_bib0008","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1137\/15M1047325","article-title":"Multicontrast MRI reconstruction with structure-guided total variation","volume":"9","author":"Ehrhardt","year":"2016","journal-title":"SIAM Journal on Imaging Sciences"},{"key":"10.1016\/j.eswa.2026.132866_bib0009","article-title":"McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction","volume":"168","author":"Ekanayake","year":"2024","journal-title":"CIBM"},{"key":"10.1016\/j.eswa.2026.132866_bib0010","series-title":"MICCAI","first-page":"140","article-title":"Multi-contrast MRI super-resolution via a multi-stage integration network","author":"Feng","year":"2021"},{"issue":"10","key":"10.1016\/j.eswa.2026.132866_bib0011","first-page":"2804","article-title":"Multimodal transformer for accelerated MR imaging","volume":"42","author":"Feng","year":"2022","journal-title":"TMI"},{"issue":"2","key":"10.1016\/j.eswa.2026.132866_bib0012","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1002\/mrm.25142","article-title":"Promise: Parallel-imaging and compressed-sensing reconstruction of multicontrast imaging using sharable information","volume":"73","author":"Gong","year":"2015","journal-title":"MRM"},{"key":"10.1016\/j.eswa.2026.132866_bib0013","unstructured":"Gu, A., & Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv: 2312.00752."},{"key":"10.1016\/j.eswa.2026.132866_bib0014","series-title":"ECCV","first-page":"222","article-title":"MambaiR: A simple baseline for image restoration with state-space model","author":"Guo","year":"2025"},{"key":"10.1016\/j.eswa.2026.132866_bib0015","doi-asserted-by":"crossref","unstructured":"He, X., Cao, K., Yan, K., Li, R., Xie, C., Zhang, J., & Zhou, M. (2024). Pan-Mamba: Effective pan-sharpening with state space model. arXiv: 2402.12192.","DOI":"10.1016\/j.inffus.2024.102779"},{"key":"10.1016\/j.eswa.2026.132866_bib0016","series-title":"Paired t test","first-page":"1","author":"Hsu","year":"2007"},{"key":"10.1016\/j.eswa.2026.132866_bib0017","series-title":"MICCAI","first-page":"313","article-title":"Accurate multi-contrast MRI super-resolution via a dual cross-attention transformer network","author":"Huang","year":"2023"},{"issue":"1","key":"10.1016\/j.eswa.2026.132866_bib0018","article-title":"FastMRI: A publicly available raw K-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning","volume":"2","author":"Knoll","year":"2020","journal-title":"RAI"},{"key":"10.1016\/j.eswa.2026.132866_bib0019","series-title":"CVPR","first-page":"5926","article-title":"Training generative image super-resolution models by wavelet-domain losses enables better control of artifacts","author":"Korkmaz","year":"2024"},{"key":"10.1016\/j.eswa.2026.132866_bib0020","first-page":"95","article-title":"Sparse MRI reconstruction using multi-contrast image guided graph representation","volume":"43","author":"Lai","year":"2017","journal-title":"MRM"},{"key":"10.1016\/j.eswa.2026.132866_bib0021","doi-asserted-by":"crossref","first-page":"4686","DOI":"10.1109\/TIP.2024.3445729","article-title":"Joint under-sampling pattern and dual-domain reconstruction for accelerating multi-contrast MRI","volume":"33","author":"Lei","year":"2024","journal-title":"IEEE Transactions on Image Processing"},{"issue":"3","key":"10.1016\/j.eswa.2026.132866_bib0022","first-page":"1436","article-title":"Multi-contrast complementary learning for accelerated MR imaging","volume":"28","author":"Li","year":"2023","journal-title":"JBHI"},{"key":"10.1016\/j.eswa.2026.132866_bib0023","series-title":"CVPR","first-page":"20636","article-title":"Transformer-empowered multi-scale contextual matching and aggregation for multi-contrast MRI super-resolution","author":"Li","year":"2022"},{"key":"10.1016\/j.eswa.2026.132866_bib0024","series-title":"ICCV","first-page":"1833","article-title":"SwinIR: Image restoration using swin transformer","author":"Liang","year":"2021"},{"key":"10.1016\/j.eswa.2026.132866_bib0025","article-title":"Dual-space high-frequency learning for transformer-based MRI super-resolution","volume":"250","author":"Lin","year":"2024","journal-title":"CMPB"},{"key":"10.1016\/j.eswa.2026.132866_bib0026","first-page":"2966","article-title":"Image reconstruction for accelerated MR scan with faster fourier convolutional neural networks","volume":"33","author":"Liu","year":"2024","journal-title":"TIP"},{"key":"10.1016\/j.eswa.2026.132866_bib0027","first-page":"1","article-title":"Understanding the effective receptive field in deep convolutional neural networks","volume":"29","author":"Luo","year":"2016","journal-title":"NIPS"},{"issue":"6","key":"10.1016\/j.eswa.2026.132866_bib0028","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1002\/mrm.21391","article-title":"Sparse MRI: The application of compressed sensing for rapid MR imaging","volume":"58","author":"Lustig","year":"2007","journal-title":"MRM"},{"issue":"2","key":"10.1016\/j.eswa.2026.132866_bib0029","first-page":"72","article-title":"Compressed sensing MRI","volume":"25","author":"Lustig","year":"2008","journal-title":"SPM"},{"issue":"9","key":"10.1016\/j.eswa.2026.132866_bib0030","first-page":"2738","article-title":"Multi-contrast super-resolution MRI through a progressive network","volume":"39","author":"Lyu","year":"2020","journal-title":"TMI"},{"key":"10.1016\/j.eswa.2026.132866_bib0031","first-page":"3707","article-title":"Learning attention in the frequency domain for flexible real photograph denoising","volume":"33","author":"Ma","year":"2024","journal-title":"TIP"},{"key":"10.1016\/j.eswa.2026.132866_bib0032","unstructured":"Mao, X., Liu, Y., Shen, W., Li, Q., & Wang, Y. (2021). Deep residual fourier transformation for single image deblurring. 2(3), 5. arXiv: 2111.11745."},{"issue":"10","key":"10.1016\/j.eswa.2026.132866_bib0033","first-page":"1993","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2014","journal-title":"TMI"},{"key":"10.1016\/j.eswa.2026.132866_bib0034","series-title":"CVPR","first-page":"6583","article-title":"WaveFace: Authentic face restoration with efficient frequency recovery","author":"Miao","year":"2024"},{"key":"10.1016\/j.eswa.2026.132866_bib0035","unstructured":"Mirza, M. U., Dalmaz, O., Bedel, H. A., Elmas, G., Korkmaz, Y., Gungor, A., Dar, S. U. H., & \u00c7ukur, T. (2023). Learning fourier-constrained diffusion bridges for MRI reconstruction. arXiv: 2308.01096."},{"key":"10.1016\/j.eswa.2026.132866_bib0036","unstructured":"Molina, J., Petrache, M., Costabal, F. S., & Courdurier, M. (2024). Understanding the dynamics of the frequency bias in neural networks. arXiv: 2405.14957."},{"key":"10.1016\/j.eswa.2026.132866_bib0037","series-title":"CVPR","first-page":"10199","article-title":"Wavelet diffusion models are fast and scalable image generators","author":"Phung","year":"2023"},{"key":"10.1016\/j.eswa.2026.132866_bib0038","series-title":"ICML","first-page":"5301","article-title":"On the spectral bias of neural networks","author":"Rahaman","year":"2019"},{"issue":"3","key":"10.1016\/j.eswa.2026.132866_bib0039","first-page":"621","article-title":"Coupled dictionary learning for multi-contrast MRI reconstruction","volume":"39","author":"Song","year":"2019","journal-title":"TMI"},{"issue":"1","key":"10.1016\/j.eswa.2026.132866_bib0040","article-title":"Improving magnetic resonance imaging with smart and thin metasurfaces","volume":"11","author":"Stoja","year":"2021","journal-title":"SR"},{"key":"10.1016\/j.eswa.2026.132866_bib0041","article-title":"Fourier convolution block with global receptive field for MRI reconstruction","volume":"99","author":"Sun","year":"2025","journal-title":"MIA"},{"issue":"12","key":"10.1016\/j.eswa.2026.132866_bib0042","first-page":"6141","article-title":"A deep information sharing network for multi-contrast compressed sensing MRI reconstruction","volume":"28","author":"Sun","year":"2019","journal-title":"TIP"},{"key":"10.1016\/j.eswa.2026.132866_bib0043","series-title":"CVPR","first-page":"22356","article-title":"Spatial-frequency mutual learning for face super-resolution","author":"Wang","year":"2023"},{"key":"10.1016\/j.eswa.2026.132866_bib0044","first-page":"1018","article-title":"MD-GraphFormer: A model-driven graph transformer for fast multi-contrast mr imaging","volume":"9","author":"Wang","year":"2023","journal-title":"TCI"},{"key":"10.1016\/j.eswa.2026.132866_bib0045","article-title":"DSMENet: Detail and structure mutually enhancing network for under-sampled MRI reconstruction","volume":"154","author":"Wang","year":"2023","journal-title":"CIBM"},{"issue":"7","key":"10.1016\/j.eswa.2026.132866_bib0046","first-page":"2105","article-title":"Deep-learning-based multi-modal fusion for fast MR reconstruction","volume":"66","author":"Xiang","year":"2018","journal-title":"TBE"},{"key":"10.1016\/j.eswa.2026.132866_bib0047","series-title":"MICCAI","first-page":"178","article-title":"Learning MRI K-space subsampling pattern using progressive weight pruning","author":"Xuan","year":"2020"},{"issue":"9","key":"10.1016\/j.eswa.2026.132866_bib0048","first-page":"2499","article-title":"Multimodal MRI reconstruction assisted with spatial alignment network","volume":"41","author":"Xuan","year":"2022","journal-title":"TMI"},{"issue":"6","key":"10.1016\/j.eswa.2026.132866_bib0049","first-page":"1310","article-title":"DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction","volume":"37","author":"Yang","year":"2017","journal-title":"TMI"},{"issue":"11","key":"10.1016\/j.eswa.2026.132866_bib0050","first-page":"5506","article-title":"Frequency learning via multi-scale fourier transformer for MRI reconstruction","volume":"27","author":"Yi","year":"2023","journal-title":"JBHI"},{"key":"10.1016\/j.eswa.2026.132866_bib0051","series-title":"DSFormer: A dual-domain self-supervised transformer for accelerated multi-contrast MRI reconstruction","first-page":"4966","author":"Zhou","year":"2023"},{"key":"10.1016\/j.eswa.2026.132866_bib0052","series-title":"CVPR","first-page":"4273","article-title":"DudoRNet: Learning a dual-domain recurrent network for fast MRI reconstruction with deep t1 prior","author":"Zhou","year":"2020"},{"issue":"7","key":"10.1016\/j.eswa.2026.132866_bib0053","doi-asserted-by":"crossref","first-page":"5281","DOI":"10.1109\/TPAMI.2024.3368112","article-title":"A general spatial-frequency learning framework for multimodal image fusion","volume":"47","author":"Zhou","year":"2024","journal-title":"TPAMI"},{"key":"10.1016\/j.eswa.2026.132866_bib0054","article-title":"MMR-Mamba: Multi-modal MRI reconstruction with Mamba and spatial-frequency information fusion","volume":"102","author":"Zou","year":"2025","journal-title":"MIA"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426017793?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426017793?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:14:32Z","timestamp":1783116872000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426017793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":54,"alternative-id":["S0957417426017793"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132866","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MF2MR2: Multi-frequency fusion for accelerated multi-contrast MRI reconstruction","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132866","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"132866"}}