{"url":"/dataset/ixi-dataset","name":"IXI","full_name":"IXI Brain Development Dataset","description_markdown":"**IXI Dataset** is a collection of 600 MR brain images from normal, healthy subjects. The MR image acquisition protocol for each subject includes:\r\n\r\n * T1, T2 and PD-weighted images\r\n * MRA images\r\n * Diffusion-weighted images (15 directions)\r\n\r\nThe data has been collected at three different hospitals in London:\r\n\r\n * Hammersmith Hospital using a Philips 3T system (details of scanner parameters)\r\n * Guy’s Hospital using a Philips 1.5T system (details of scanner parameters)\r\n * Institute of Psychiatry using a GE 1.5T system (details of the scan parameters not available at the moment)\r\n\r\nThe data has been collected as part of the project:\r\n\r\n * IXI – Information eXtraction from Images (EPSRC GR/S21533/02)\r\n\r\nThe images in NIFTI format can be downloaded from [here](https://brain-development.org/ixi-dataset/):\r\n\r\nThis data is made available under the Creative Commons CC BY-SA 3.0 license. If you use the IXI data please acknowledge the source of the IXI data.","description_withheld":null,"homepage":"https://brain-development.org/ixi-dataset/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Image Super-Resolution","url":"/task/image-super-resolution","datasets_with_task":"/datasets/task/image-super-resolution"},{"name":"Image-to-Image Translation","url":"/task/image-to-image-translation","datasets_with_task":"/datasets/task/image-to-image-translation"},{"name":"Medical Image Registration","url":"/task/medical-image-registration","datasets_with_task":"/datasets/task/medical-image-registration"},{"name":"MRI Reconstruction","url":"/task/mri-reconstruction","datasets_with_task":"/datasets/task/mri-reconstruction"}],"languages":[],"variants":["IXI"],"data_loaders":[{"repo":"https://github.com/fepegar/torchio","url":"https://torchio.readthedocs.io/datasets.html#module-torchio.datasets.ixi","frameworks":["pytorch"]}],"num_papers_in_archive":23,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-super-resolution-on-ixi","task":"Image Super-Resolution","dataset_variant":"IXI","rows":9,"metrics":["PSNR 2x T2w","PSNR 4x T2w","SSIM 4x T2w","SSIM for 2x T2w"],"first_row_in_archive_order":{"model":"EDSR+MMHCA","paper":"/paper/multimodal-multi-head-convolutional-attention","metrics":{"PSNR 2x T2w":"40.43","PSNR 4x T2w":"32.70","SSIM 4x T2w":"0.9469","SSIM for 2x T2w":"0.9877"},"code_links":[{"title":"lilygeorgescu/mhca","url":"https://github.com/lilygeorgescu/mhca"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-registration-on-ixi","task":"Medical Image Registration","dataset_variant":"IXI","rows":8,"metrics":["DSC"],"first_row_in_archive_order":{"model":"LL_Net","paper":"/paper/a-light-weight-rectangular-decomposition","metrics":{"DSC":"0.767"},"code_links":[{"title":"BoyOfChu/LL_Net","url":"https://github.com/BoyOfChu/LL_Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-to-image-translation-on-ixi-dataset","task":"Image-to-Image Translation","dataset_variant":"IXI","rows":7,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"ResViT","paper":"/paper/resvit-residual-vision-transformers-for-multi","metrics":{"PSNR":"35.71 ± 1.77"},"code_links":[{"title":"icon-lab/ResViT","url":"https://github.com/icon-lab/ResViT"},{"title":"CV-Reimplementation/ResViT-Reimplementation","url":"https://github.com/CV-Reimplementation/ResViT-Reimplementation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/mri-reconstruction-on-ixi-dataset","task":"MRI Reconstruction","dataset_variant":"IXI","rows":1,"metrics":["DSSIM","MSE"],"first_row_in_archive_order":{"model":"Residual U-NET","paper":"/paper/deep-convolutional-autoencoders-for","metrics":{"DSSIM":"1.44e-03","MSE":"3.44e-05"},"code_links":[{"title":"AdrianArnaiz/Brain-MRI-Autoencoder","url":"https://github.com/AdrianArnaiz/Brain-MRI-Autoencoder"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-light-weight-rectangular-decomposition","title":"A light-weight rectangular decomposition large kernel convolution network for deformable medical image registration.","date":"2024-05-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/efficientmorph-parameter-efficient","title":"EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration","date":"2024-03-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/optron-better-medical-image-registration-via","title":"On-the-Fly Guidance Training for Medical Image Registration","date":"2023-08-29","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/unsupervised-medical-image-translation-with","title":"Unsupervised Medical Image Translation with Adversarial Diffusion Models","date":"2022-07-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multimodal-multi-head-convolutional-attention","title":"Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution","date":"2022-04-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/transmorph-transformer-for-unsupervised","title":"TransMorph: Transformer for unsupervised medical image registration","date":"2021-11-19","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/resvit-residual-vision-transformers-for-multi","title":"ResViT: Residual vision transformers for multi-modal medical image synthesis","date":"2021-06-30","rows_on_this_dataset":6,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mr-image-super-resolution-with-squeeze-and","title":"MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network","date":"2021-06-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/task-transformer-network-for-joint-mri","title":"Task Transformer Network for Joint MRI Reconstruction and Super-Resolution","date":"2021-06-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vit-v-net-vision-transformer-for-unsupervised","title":"ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration","date":"2021-04-13","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":4,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-convolutional-autoencoders-for","title":"Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brain","date":"2021-01-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/convolutional-neural-networks-with-3","title":"Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans","date":"2020-01-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/channel-splitting-network-for-single-mr-image","title":"Channel Splitting Network for Single MR Image Super-Resolution","date":"2018-10-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/voxelmorph-a-learning-framework-for","title":"VoxelMorph: A Learning Framework for Deformable Medical Image Registration","date":"2018-09-14","rows_on_this_dataset":1,"code_links":9,"syntology":null},{"paper":"/paper/fast-and-accurate-single-image-super","title":"Fast and Accurate Single Image Super-Resolution via Information Distillation Network","date":"2018-03-26","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/residual-dense-network-for-image-super","title":"Residual Dense Network for Image Super-Resolution","date":"2018-02-24","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":7,"samples_unverified":17,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/accurate-image-super-resolution-using-very","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","date":"2015-11-14","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/image-super-resolution-using-deep","title":"Image Super-Resolution Using Deep Convolutional Networks","date":"2014-12-31","rows_on_this_dataset":1,"code_links":60,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":27,"samples_ran":7,"samples_unverified":20,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":7,"samples_harvested":70,"samples_ran":23,"samples_unverified":47,"pointer_only_for_licence":11,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}