[{"_1":2,"_185":-5,"_186":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_160":161},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_84":85,"_108":109,"_153":-5,"_154":11,"_155":17,"_156":11,"_157":-5,"_158":159},"canonicalUrl","https://hub.docker.com/r/tigrlab/feenics/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","tigrlab","reponame","feenics","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":70,"_75":11,"_76":77,"_81":29,"_82":83},"categories",[],"content_types",[40],"image","date_registered","2018-07-30T20:10:09.041897Z","description","A pipeline for FrEquENcy-based Ica Cleaning of Spiral mri scans","is_private","last_updated","2018-07-30T20:12:08.415204Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",313,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","# FeenICS (fē'nĭks)\nA pipeline for FrEquENcy-based Ica Cleaning of Spirals. Aims to remove spiral-specific aritfact from fMRI scans. The source code can be found here: https://github.com/TIGRLab/FeenICS\n\nThis tool was built to work with FSL's [MELODIC](http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC).\nIt was created to automate the process of evaluating and removing ICA components containing spiral artifact, which is accomplished by evaulating the per slice frequency loading, and scoring components based on patterns\nof low or high frequency information. The fsl_regfilt command-line program can then be used to regress out components selected for removal.\n\n## There are four executables:\n+ [**s1_folder_setup.py**] : To create the folder structure necessary to run Melodic and the remainder of the scripts.\n+ [**s2_identify_components.py**] : To run check_slices.py for an entire folder of subjects and outputs a classification text file (default: fix4melview_Standard_thr20.txt).\n+ [**s3_remove_flagged_components.py**] : To remove flagged components once content with the results of the classifcation. This script reads the final line of the classification text file.\n+ [**check_slices.py**]: To classify components as keep or remove. Is run by s2_identify_components.py, but can be run by itself on an individual scan.\n\n\n### s1_folder_setup.py\n\n```\nUsage:\n s1_folder_setup.py -p -i PATH -s PATH \n\nArguments:\n\n Path to top experiment directory. Outputs will be created\n here.\nOptions:\n\n --sprl, -i PATH Specify alternative folder containing split spirals.\n\n --subs, -s PATH Specify alternative experiment folder containing\n subjectID subfolders.\n\n --parallel, -p Print instuctions for running in parallel (GNU parallel)\n instead of running FSL steps on machine in series.\n\nDETAILS\nMakes subject and sprl subfolders within . Moves separated spiral\nfiles to appropriate subfolders. If split spirals are not already contained\nwithin , use the \"-i\" option to specify an alternative path. Specify\n\"-p\" if you would not like to run FSL preprocessing steps at this time. It will\nprint instructions to run MCFLIRT, BET, and MELODIC using GNU parallel.\n```\n\n### s2_identify_components.py\n\n```\nUsage:\n s2_identify_components.py -m FLOAT -l FLOAT \n\nArguments:\n Path to top experiment directory.\n\nOptions:\n --midFactor, -m Cutoff multiplier for mid range frequency information.\n Raise this value to more aggressively remove noise\n components. Default is 3.\n\n --lowFactor, -l Cutoff multiplier for low range frequency information.\n Raise this value to keep more signal components.\n Default is 1.\n\nDETAILS\nFeeds paths, multipliers, and output file names to check_slices.py for each\nsubject within the specified directory. The default name for the output\nclassification file is fix4melview_Standard_thr20.txt.\n```\n\n### s3_remove_flagged_components.py\n\n```\nUsage:\n s3_remove_flagged_components.py -c PATH -o PATH \n\nArguments:\n Path to top experiment directory.\n\nOptions:\n --clean_img, -c Path to desired location of cleaned images.\n\n --output, -o If csv outputs were generated in non-default location,\n identify path to this location.\n\nDETAILS\nUses fsl_regfilt to regress out the components specified in the last line of the\nclassification file (fix4melview_Standard_thr20.txt). The outputs are cleaned,\nbut still separated spiral niftis. They will be named subject.sprl.denoised.nii.gz.\n```\n\n### icarus-report (optional)\n\nAn html report creation tool written by [E.Dickie](https://github.com/edickie).\nThis step is optional, but will make checking your data much easier.\n\nFollow the sample in **Usage Examples**, below or click [here](https://github.com/edickie/ICArus)\nfor futher instructions.\n\n\n### check_slices.py\n\n```\nUsage:\n check_slices.py \n\nArguments:\n Path to any melodic_IC.nii.gz file.\n\n Path to desired output classification file location.\n\n Multiplier to be used to determine mid/high frequency\n cutoffs. If called from s2, default is 3.\n\n Multiplier to be used to determine low frequency cutoffs.\n If called from s2, default is 1.\n\nDETAILS\nThis script is called by s2_identify_components.py. Can be used independently to\ntroubleshoot classification or path identification issues, or just to run one\nspiral scan at a time. The algorithm scores each slice based on how much high\nand low frequency information is present after performing an FFT (fast fourier\ntransform). The scores are tallied per component, and the result determines\nwhether or not a component is to be flagged for removal. The output of this script\nis a classification text file (fix4melview_Standard_thr20.txt), which details the\nscore and classification criteria per slice, as well as the removal decision\nper component. The final line of this document is a list of which components are\nto be removed. If you disagree with the decision, change the component numbers in\nthis list, as this line will be read into s3_remove_flagged_components.py.\n\n```\n### Usage Examples:\n\nTo run FeenICS locally for an experiment called EXPR, with additional use of Erin Dickie's ICArus package (https://github.com/edickie/ICArus) to better visualize the decisions made by the check_slices.py algorithm\n\n#### Step 1:\n~~~sh\ns1_folder_setup.py /path/to/EXPR\n~~~\n\n#### Step 2:\n~~~sh\ns2_identify_components.py /path/to/EXPR\n~~~\n\n#### Step 3(optional) - Run ICArus (see https://github.com/edickie/ICArus for more information):\n~~~sh\nsprls=`cd /path/to/EXPR; ls -1d */sprl*/`\nicarus-report ${sprls}\n\nOpen qc_icafix.html in your favourite web browser to view the classification results.\n~~~\n\n#### Step 4 - After you are okay with classifications, run s3_1_remove_flagged_components.py:\n~~~sh\ns3_remove_flagged_components.py /path/to/EXPR\n~~~","has_starred","hub_user","immutable_tags_settings",{"_71":11,"_72":73},"enabled","rules",[74],".*","is_automated","permissions",{"_78":17,"_79":11,"_80":11},"read","write","admin","user","storage_size",4147653320,"orgData",{"_86":87,"_88":89,"_90":91,"_92":29,"_93":94,"_95":96,"_97":98,"_99":100,"_101":102,"_103":104,"_105":104,"_106":104,"_107":17},"id","983c24a551b344df9b06e39c00994a85","uuid","983c24a5-51b3-44df-9b06-e39c00994a85","type","organization","orgname","full_name","TIGRLab","location","Toronto","company","CAMH","profile_url","http://imaging-genetics.camh.ca/","date_joined","2018-07-24T21:03:06.144611Z","gravatar_url","","gravatar_email","badge","is_active","recentTags",[110,147],{"_111":40,"_112":113,"_114":115,"_86":116,"_117":118,"_46":135,"_136":113,"_137":138,"_139":52,"_53":140,"_141":142,"_143":126,"_144":135,"_145":62,"_146":17},"content_type","creator",3627067,"full_size",4147642155,32033747,"images",[119],{"_120":121,"_122":123,"_124":104,"_125":126,"_127":128,"_129":130,"_131":104,"_132":-5,"_133":115,"_59":62,"_134":-5},"architecture","amd64","digest","sha256:e1130d83fbf392c8e2c8bed1a2402a8913236f6bd299efaf45ebd52e9086b947","features","last_pulled","2026-09-17T20:43:36.326028045Z","last_pushed","2018-07-30T20:10:09Z","os","linux","os_features","os_version","size","variant","2018-07-30T20:12:08.543321Z","last_updater","last_updater_username","edickie","media_type","latest","repository",5805200,"tag_last_pulled","tag_last_pushed","tag_status","v2",{"_111":40,"_112":113,"_114":115,"_86":148,"_117":149,"_46":151,"_136":113,"_137":138,"_139":52,"_53":152,"_141":142,"_143":126,"_144":151,"_145":62,"_146":17},32033601,[150],{"_120":121,"_122":123,"_124":104,"_125":126,"_127":128,"_129":130,"_131":104,"_132":-5,"_133":115,"_59":62,"_134":-5},"2018-07-30T20:10:08.946994Z","0.0.1","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_34":162,"_169":170,"_158":183,"_184":11},{"_36":163,"_38":164,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":165,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":166,"_75":11,"_76":168,"_81":29,"_82":83},[],[40],[52],{"_71":11,"_72":167},[74],{"_78":17,"_79":11,"_80":11},"tags",{"_171":172,"_173":-5,"_174":-5,"_175":176},"count",2,"next","previous","results",[177,180],{"_111":40,"_112":113,"_114":115,"_86":116,"_117":178,"_46":135,"_136":113,"_137":138,"_139":52,"_53":140,"_141":142,"_143":126,"_144":135,"_145":62,"_146":17},[179],{"_120":121,"_122":123,"_124":104,"_125":126,"_127":128,"_129":130,"_131":104,"_132":-5,"_133":115,"_59":62,"_134":-5},{"_111":40,"_112":113,"_114":115,"_86":148,"_117":181,"_46":151,"_136":113,"_137":138,"_139":52,"_53":152,"_141":142,"_143":126,"_144":151,"_145":62,"_146":17},[182],{"_120":121,"_122":123,"_124":104,"_125":126,"_127":128,"_129":130,"_131":104,"_132":-5,"_133":115,"_59":62,"_134":-5},[],"canDeleteTags","actionData","errors"]

tigrlab/feenics

Tags for tigrlab/feenics