{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unsupervised-mri-reconstruction-via-zero-shot","title":"Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers","arxiv_id":"2105.08059","date":"2021-05-15","proceeding":null,"authors":["Yilmaz Korkmaz","Salman UH Dar","Mahmut Yurt","Muzaffer Özbey","Tolga Çukur"],"abstract":"Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency. To reduce supervision requirements, the recent deep image prior framework instead conjoins untrained MRI priors with the imaging operator during inference. Yet, canonical convolutional architectures are suboptimal in capturing long-range relationships, and priors based on randomly initialized networks may yield suboptimal performance. To address these limitations, here we introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adversarial TransformERs (SLATER). SLATER embodies a deep adversarial network with cross-attention transformers to map noise and latent variables onto coil-combined MR images. During pre-training, this unconditional network learns a high-quality MRI prior in an unsupervised generative modeling task. During inference, a zero-shot reconstruction is then performed by incorporating the imaging operator and optimizing the prior to maximize consistency to undersampled data. Comprehensive experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against state-of-the-art unsupervised methods.","url_abs":"https://arxiv.org/abs/2105.08059v3","url_pdf":"https://arxiv.org/pdf/2105.08059v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unsupervised-mri-reconstruction-via-zero-shot","repo_url":"https://github.com/icon-lab/SLATER","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"mri-reconstruction","task_name":"MRI Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2105.08059","atlas_url":"https://app.syntology.ai/?focus=2105.08059","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.08059"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/icon-lab/SLATER","reach":null}],"summary":{"ran_violates":2,"ran_fixture":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"d9509e7bcb8dcdbc","entry":"fft2c","repo":"icon-lab/SLATER","repo_kind":"official","path":"run_recon_multi_coil.py","file_url":"https://github.com/icon-lab/SLATER/blob/HEAD/run_recon_multi_coil.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"d9509e7bcb8dcdbc"}},{"code_sha256_prefix":"2860ec50274d4165","entry":"fft2c_multi_np","repo":"icon-lab/SLATER","repo_kind":"official","path":"run_recon_multi_coil.py","file_url":"https://github.com/icon-lab/SLATER/blob/HEAD/run_recon_multi_coil.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"2860ec50274d4165"}},{"code_sha256_prefix":"4751406df2d457bc","entry":"ifft2c","repo":"icon-lab/SLATER","repo_kind":"official","path":"run_recon_multi_coil.py","file_url":"https://github.com/icon-lab/SLATER/blob/HEAD/run_recon_multi_coil.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"4751406df2d457bc"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}