{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:58:02Z","timestamp":1785488282867,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,17]]},"DOI":"10.1145\/3774521.3774613","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T07:34:24Z","timestamp":1785483264000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["TransUNet-Recon: A Transformer-Augmented UNet Architecture for Accelerated MRI Reconstruction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8089-3675","authenticated-orcid":false,"given":"Susant Kumar","family":"Panigrahi","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4954-2177","authenticated-orcid":false,"given":"Subhoshri","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur (IIEST), Shibpur, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7708-7894","authenticated-orcid":false,"given":"Pradipta","family":"Sasmal","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9046-149X","authenticated-orcid":false,"given":"Debdoot","family":"Sheet","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Hemant\u00a0K Aggarwal Merry\u00a0P Mani and Mathews Jacob. 2018. MoDL: Model-based deep learning architecture for inverse problems. IEEE transactions on medical imaging 38 2 (2018) 394\u2013405.","DOI":"10.1109\/TMI.2018.2865356"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Amir Aghabiglou and Ender\u00a0M Eksioglu. 2021. Projection-Based cascaded U-Net model for MR image reconstruction. Computer Methods and Programs in Biomedicine 207 (2021) 106151.","DOI":"10.1016\/j.cmpb.2021.106151"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Mehmet Ak\u00e7akaya Steen Moeller Sebastian Weing\u00e4rtner and K\u00e2mil U\u011furbil. 2019. Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: database-free deep learning for fast imaging. Magnetic resonance in medicine 81 1 (2019) 439\u2013453.","DOI":"10.1002\/mrm.27420"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_9"},{"key":"e_1_3_3_2_6_2","unstructured":"Jieneng Chen Yongyi Lu Qihang Yu and et al.2021. TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2102.04306 (2021)."},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"e_1_3_3_2_8_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, and et al.2021. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_2_9_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Taejoon Eo Yohan Jun Taeseong Kim Jinseong Jang Ho-Joon Lee and Dosik Hwang. 2018. KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magnetic resonance in medicine 80 5 (2018) 2188\u20132201.","DOI":"10.1002\/mrm.27201"},{"key":"e_1_3_3_2_11_2","unstructured":"Golnaz Ghiasi Tsung-Yi Lin and Quoc\u00a0V Le. 2018. DropBlock: A regularization method for convolutional networks. 31 (2018)."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Mark\u00a0A Griswold Peter\u00a0M Jakob Ralf\u00a0M Heidemann and et al.2002. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magnetic resonance in medicine 47 6 (2002) 1202\u20131210.","DOI":"10.1002\/mrm.10171"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","unstructured":"Pengfei Guo Yiqun Mei Jinyuan Zhou Shanshan Jiang and Vishal\u00a0M. Patel. 2024. ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer. IEEE Transactions on Medical Imaging 43 1 (2024) 582\u2013593. 10.1109\/TMI.2023.3314747","DOI":"10.1109\/TMI.2023.3314747"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87231-1_2"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Jesse Hamilton Dominique Franson and Nicole Seiberlich. 2017. Recent advances in parallel imaging for MRI. Progress in nuclear magnetic resonance spectroscopy 101 (2017) 71\u201395.","DOI":"10.1016\/j.pnmrs.2017.04.002"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Yoseo Han Leonard Sunwoo and Jong\u00a0Chul Ye. 2019. k-space deep learning for accelerated MRI. IEEE transactions on medical imaging 39 2 (2019) 377\u2013386.","DOI":"10.1109\/TMI.2019.2927101"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Dianlin Hu Yikun Zhang Jianfeng Zhu Qiegen Liu and Yang Chen. 2022. TRANS-Net: Transformer-enhanced residual-error alternative suppression network for MRI reconstruction. IEEE Transactions on Instrumentation and Measurement 71 (2022) 1\u201313.","DOI":"10.1109\/TIM.2022.3205684"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC48229.2022.9871475"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00523"},{"key":"e_1_3_3_2_23_2","unstructured":"Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1412.6980 (2014)."},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Florian Knoll Jure Zbontar Anuroop Sriram Matthew\u00a0J Muckley Mary Bruno Aaron Defazio Marc Parente Krzysztof\u00a0J Geras Joe Katsnelson Hersh Chandarana et\u00a0al. 2020. fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning. Radiology: Artificial Intelligence 2 1 (2020) e190007.","DOI":"10.1148\/ryai.2020190007"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"e_1_3_3_2_26_2","first-page":"774","volume-title":"International Conference on Medical Imaging with Deep Learning","author":"Lin Kang","year":"2022","unstructured":"Kang Lin and Reinhard Heckel. 2022. Vision transformers enable fast and robust accelerated MRI. In International Conference on Medical Imaging with Deep Learning. PMLR, 774\u2013795."},{"key":"e_1_3_3_2_27_2","unstructured":"Kang Lin and Reinhard Heckel. 2023. Robustness of deep learning for accelerated MRI: benefits of diverse training data. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2312.10271 (2023)."},{"key":"e_1_3_3_2_28_2","unstructured":"Yuanyuan Liu Zhuo-Xu Cui Shucong Qin Congcong Liu Hairong Zheng Haifeng Wang Yihang Zhou Dong Liang and Yanjie Zhu. 2025. Score-based diffusion models with self-supervised learning for accelerated 3D multi-contrast cardiac MR imaging. IEEE Transactions on Medical Imaging (2025)."},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Tieyuan Lu Xinlin Zhang Yihui Huang Di Guo Feng Huang Qin Xu Yuhan Hu Lin Ou-Yang Jianzhong Lin Zhiping Yan et\u00a0al. 2020. pFISTA-SENSE-ResNet for parallel MRI reconstruction. Journal of Magnetic Resonance 318 (2020) 106790.","DOI":"10.1016\/j.jmr.2020.106790"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Michael Lustig David Donoho and John\u00a0M Pauly. 2007. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine 58 6 (2007) 1182\u20131195.","DOI":"10.1002\/mrm.21391"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"crossref","unstructured":"Angshul Majumdar. 2015. Improving synthesis and analysis prior blind compressed sensing with low-rank constraints for dynamic MRI reconstruction. Magnetic resonance imaging 33 1 (2015) 174\u2013179.","DOI":"10.1016\/j.mri.2014.08.031"},{"key":"e_1_3_3_2_32_2","unstructured":"Duy-Kien Nguyen Mahmoud Assran Unnat Jain Martin\u00a0R Oswald Cees\u00a0GM Snoek and Xinlei Chen. 2024. An image is worth more than 16x16 patches: Exploring transformers on individual pixels. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2406.09415 (2024)."},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"crossref","unstructured":"Vishal\u00a0M Patel Ray Maleh Anna\u00a0C Gilbert and Rama Chellappa. 2012. Gradient-based image recovery methods from incomplete fourier measurements. IEEE Transactions on Image Processing 21 1 (2012) 94\u2013105.","DOI":"10.1109\/TIP.2011.2159803"},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"crossref","unstructured":"KP Pruessmann M Weiger MB Scheidegger and P Boesiger. 1999. SENSE: sensitivity encoding for fast MRI. Magnetic resonance in medicine 42 5 (1999) 952\u2013962.","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Jo Schlemper Jose Caballero Joseph\u00a0V Hajnal Anthony\u00a0N Price and Daniel Rueckert. 2018. A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE transactions on medical imaging 37 2 (2018) 491\u2013503.","DOI":"10.1109\/TMI.2017.2760978"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Claude\u00a0E Shannon. 1949. Communication in the presence of noise. Proceedings of the IRE 37 1 (1949) 10\u201321.","DOI":"10.1109\/JRPROC.1949.232969"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_7"},{"key":"e_1_3_3_2_39_2","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan\u00a0N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS) , Vol.\u00a030. Curran Associates, Inc."},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2016.7493320"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Zhou Wang Alan\u00a0C Bovik Hamid\u00a0R Sheikh and Eero\u00a0P Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13 4 (2004) 600\u2013612.","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_3_3_2_43_2","first-page":"164","volume-title":"European Conference on Computer Vision","author":"Xin Bingyu","year":"2024","unstructured":"Bingyu Xin, Meng Ye, Leon Axel, and Dimitris\u00a0N Metaxas. 2024. Rethinking Deep Unrolled Model for Accelerated MRI Reconstruction. In European Conference on Computer Vision. Springer, 164\u2013181."},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"crossref","unstructured":"Guang Yang Simiao Yu Hao Dong Greg Slabaugh Pier\u00a0Luigi Dragotti Xujiong Ye Fangde Liu Simon Arridge Jennifer Keegan Yike Guo et\u00a0al. 2017. DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE transactions on medical imaging 37 6 (2017) 1310\u20131321.","DOI":"10.1109\/TMI.2017.2785879"},{"key":"e_1_3_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00081"},{"key":"e_1_3_3_2_46_2","unstructured":"Jure Zbontar Florian Knoll Anuroop Sriram et\u00a0al. 2018. fastMRI: An open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1811.08839 (2018)."}],"event":{"name":"ICVGIP 2025: Indian Conference on Computer Vision, Graphics, and Image Processing","location":"Mandi Himachal Pradesh India","acronym":"ICVGIP 2025"},"container-title":["Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774521.3774613","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:06:26Z","timestamp":1785485186000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774521.3774613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":45,"alternative-id":["10.1145\/3774521.3774613","10.1145\/3774521"],"URL":"https:\/\/doi.org\/10.1145\/3774521.3774613","relation":{},"subject":[],"published":{"date-parts":[[2025,12,17]]},"assertion":[{"value":"2026-07-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}