{"id":22326074,"url":"https://github.com/mrphys/sodium_mri_dncnn","last_synced_at":"2025-03-26T06:11:50.555Z","repository":{"id":232838948,"uuid":"783765500","full_name":"mrphys/sodium_MRI_DnCNN","owner":"mrphys","description":"Example code for training a denoising convolutional neural network (DnCNN) for MRI","archived":false,"fork":false,"pushed_at":"2024-04-11T15:39:31.000Z","size":34648,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-01-31T07:32:07.585Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mrphys.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-08T14:25:38.000Z","updated_at":"2024-07-04T11:27:58.000Z","dependencies_parsed_at":null,"dependency_job_id":"12223a23-113f-4a3e-a21f-4402948db3bd","html_url":"https://github.com/mrphys/sodium_MRI_DnCNN","commit_stats":null,"previous_names":["mrphys/sodium_mri_dncnn"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrphys%2Fsodium_MRI_DnCNN","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrphys%2Fsodium_MRI_DnCNN/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrphys%2Fsodium_MRI_DnCNN/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrphys%2Fsodium_MRI_DnCNN/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mrphys","download_url":"https://codeload.github.com/mrphys/sodium_MRI_DnCNN/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245598320,"owners_count":20641884,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-04T02:15:32.841Z","updated_at":"2025-03-26T06:11:50.549Z","avatar_url":"https://github.com/mrphys.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Rapid 2D 23Na MRI of the calf using a denoising convolutional neural network\n============================================================================\n\nRebecca R. Baker, Vivek Muthurangu, Marilena Rega, Stephen B. Walsh, Jennifer A. Steeden\n\nSynopsis:\n---------\nA modified denoising convolutional neural network \\[1\\] is trained using 1H DICOM data from the fastMRI dataset \\[2\\] for application to 23Na MRI of the calf. Provided code includes model training and pretrained models as implemented for the paper. The ethics does not allow sharing of medical image data, thus 23Na data are not included. The fastMRI knee dataset required for training can be downloaded from https://fastmri.med.nyu.edu/.\n\nExample\n-------\n![alt text](https://github.com/mrphys/sodium_MRI_DnCNN/blob/570a78e0fbfc7492dc2b7007ece50926604e2959/Pretrained_test_image_30NSA.png)\n\nInstallation and use\n====================\nFor installation please:\n1. Download github repository\n2. From within the project folder, create Docker image and launch interactive docker container:\n```\ndocker compose up --build -d\n```\n3. Dowload the fastMRI knee DICOM dataset from https://fastmri.med.nyu.edu/ and save the folder of DICOM files \"knee_mri_clinical_seq_batch2\" in the data folder\n4. Test training by using the following command:\n```\nnohup docker compose exec tensorflow python train_network.py -m \u003e training.log \u0026\n```\n5. Shutdown docker container\n```\ndocker compose down\n```\n0. Alternatively, can be used with VScode (.devcontainer folder) for development within the docker container\n\nNote that only Linux is supported.\n\nTrained models are saved in ./model/trained_models.\n\nPretrained models can be found in ./model/pretrained_models.\n\nReferences\n================\n\\[1\\] Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Trans Image Process 2017;26:3142–55. https://doi.org/10.1109/TIP.2017.2662206\n\n\\[2\\] Knoll F, Zbontar J, Sriram A, Muckley MJ, Bruno M, Defazio A, et al. fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. Radiol Artif Intell 2020;2:e190007\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrphys%2Fsodium_mri_dncnn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmrphys%2Fsodium_mri_dncnn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrphys%2Fsodium_mri_dncnn/lists"}