{"id":"https://openalex.org/W2323580262","doi":null,"title":"Camino: Open-Source Diffusion-MRI Reconstruction and Processing","display_name":"Camino: Open-Source Diffusion-MRI Reconstruction and Processing","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W2323580262","mag":"2323580262"},"language":"en","primary_location":{"id":"mag:2323580262","doi":null,"is_oa":false,"landing_page_url":"https://afni.nimh.nih.gov/sscc/staff/rwcox/ISMRM_2006/ISMRM%202006%20-%203340/files/02759.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015338409","display_name":"Philip A. Cook","orcid":"https://orcid.org/0000-0002-1985-8475","observed_orcids":["https://orcid.org/0000-0002-1985-8475"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"PA Cook","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102764809","display_name":"Yu Bai","orcid":"https://orcid.org/0000-0003-0464-1916","observed_orcids":["https://orcid.org/0000-0003-0464-1916"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu Bai","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-0464-1916","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031710540","display_name":"Shahrum Nedjati-Gilani","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shahrum Nedjati-Gilani","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026470099","display_name":"Kiran K. Seunarine","orcid":"https://orcid.org/0000-0002-2467-7716","observed_orcids":["https://orcid.org/0000-0002-2467-7716"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"KK Seunarine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027461916","display_name":"Matt G. Hall","orcid":"https://orcid.org/0000-0002-9530-5477","observed_orcids":["https://orcid.org/0000-0002-9530-5477"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"MG Hall","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009215153","display_name":"Geoff J.M. Parker","orcid":"https://orcid.org/0000-0003-2934-2234","observed_orcids":["https://orcid.org/0000-0003-2934-2234"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geoff J.M. Parker","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2934-2234","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5033449704","display_name":"Daniel C. Alexander","orcid":"https://orcid.org/0000-0003-2439-350X","observed_orcids":["https://orcid.org/0000-0003-2439-350X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel C. Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.3384,"has_fulltext":false,"cited_by_count":453,"citation_normalized_percentile":{"value":0.98022796,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12603","display_name":"NMR spectroscopy and applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/diffusion-mri","display_name":"diffusion MRI","score":0.7960000038146973},{"id":"https://openalex.org/keywords/diffusion-mri-reconstruction","display_name":"diffusion MRI reconstruction","score":0.6119999885559082},{"id":"https://openalex.org/keywords/open-source-software","display_name":"open source software","score":0.6980000138282776},{"id":"https://openalex.org/keywords/camino-de-santiago","display_name":"Camino de Santiago","score":0.527999997138977},{"id":"https://openalex.org/keywords/spherical-deconvolution","display_name":"spherical deconvolution","score":0.9240000247955322},{"id":"https://openalex.org/keywords/q-ball-imaging","display_name":"Q-ball imaging","score":0.75},{"id":"https://openalex.org/keywords/mixture-models","display_name":"mixture models","score":0.6930000185966492},{"id":"https://openalex.org/keywords/tractography","display_name":"tractography","score":0.7300000190734863},{"id":"https://openalex.org/keywords/data-pipelines","display_name":"data pipelines","score":0.5320000052452087},{"id":"https://openalex.org/keywords/java-programs","display_name":"Java programs","score":0.47200000286102295},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"parallel computing","score":0.6010000109672546},{"id":"https://openalex.org/keywords/nonlinear-least-squares-fitting","display_name":"nonlinear least squares fitting","score":0.5630000233650208}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7591948509216309},{"id":"https://openalex.org/C112968700","wikidata":"https://www.wikidata.org/wiki/Q11368","display_name":"Unix","level":3,"score":0.6899010539054871},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.6090444326400757},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.5460635423660278},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5229640603065491},{"id":"https://openalex.org/C61423126","wikidata":"https://www.wikidata.org/wiki/Q187432","display_name":"Scripting language","level":2,"score":0.5141894817352295},{"id":"https://openalex.org/C67212190","wikidata":"https://www.wikidata.org/wiki/Q104851","display_name":"Firmware","level":2,"score":0.48670628666877747},{"id":"https://openalex.org/C2779751349","wikidata":"https://www.wikidata.org/wiki/Q1474480","display_name":"Scanner","level":2,"score":0.479521781206131},{"id":"https://openalex.org/C548217200","wikidata":"https://www.wikidata.org/wiki/Q251","display_name":"Java","level":2,"score":0.43726539611816406},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.38448867201805115},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3674026131629944},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.3432236909866333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2061125636100769},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.15972718596458435}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2323580262","doi":null,"is_oa":false,"landing_page_url":"https://afni.nimh.nih.gov/sscc/staff/rwcox/ISMRM_2006/ISMRM%202006%20-%203340/files/02759.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W746158281","https://openalex.org/W2154600934","https://openalex.org/W2625270121","https://openalex.org/W2045619269","https://openalex.org/W3026522746","https://openalex.org/W2616278958","https://openalex.org/W2303842423","https://openalex.org/W2292756937","https://openalex.org/W2737386976","https://openalex.org/W7099670161"],"abstract_inverted_index":{"Introduction":[0],"Diffusion-MRI":[1],"is":[2,29,97,110,208,266,288,293,338,399,472,643,721,754],"a":[3,11,30,37,55,104,122,210,239,257,294,325,355,370,380,390,544,687,695],"rapidly":[4],"evolving":[5],"research":[6,51],"field":[7],"that":[8,297],"has":[9,121],"produced":[10],"wealth":[12],"of":[13,18,39,75,80,114,132,155,205,259,327,348,363,480,516,540,549,552,566,573,582,610,621,641,750],"algorithms":[14],"for":[15,47,61,103,179,222,232,276,302,379,529],"the":[16,26,48,73,81,84,88,112,153,167,177,188,280,289,299,318,339,346,349,361,364,367,413,456,469,481,487,496,502,512,517,537,547,569,580,603,608,619,627,683,715,751,757],"analysis":[17],"white":[19],"matter":[20],"fibre":[21,583],"architecture":[22],"and":[23,43,64,93,101,118,145,150,192,252,314,329,335,412,432,455,476,501,571,589,633,645,723],"disorders":[24],"in":[25,99,111,162,173,209,269,320,344,447,543,637,662,707],"brain.":[27],"Camino":[28,53,76,96,157,169,233,273,323,405,417,586,758],"free,":[31],"open-source":[32],"toolkit":[33],"designed":[34,102],"to":[35,45,87,142,147,196,360,461,711],"make":[36],"selection":[38],"this":[40],"technology":[41],"available":[42,755],"convenient":[44],"use":[46],"diffusion":[49,319,341,350,372,409,422,470,482,599,716],"MRI":[50],"community.":[52],"implements":[54],"data":[56,86,135,141,172,189,230,237,244,247,255,278,286,313,387,527,693],"processing":[57],"pipeline,":[58],"which":[59,200,292,345],"allows":[60,193],"easy":[62,223],"scripting":[63],"flexible":[65],"integration":[66,224],"with":[67,225,284,631,686,691,697],"other":[68,148],"software.":[69,227],"This":[70,185],"abstract":[71],"summarises":[72],"features":[74],"at":[77],"each":[78,119,156,180,194,285,303,321,541],"stage":[79,308],"pipeline":[82,136,190],"from":[83,238,242,256,264,354,486,525,598,618,694,756],"raw":[85,212,236,312,657,663,704],"statistics":[89,477,479,494],"used":[90,446,460],"by":[91,139,499,506],"clinicians":[92],"researchers.":[94],"Design":[95],"written":[98,661,676],"Java,":[100],"Unix-style":[105],"interface.":[106],"The":[107,134,215,229,246,306,333,444,508,519,639,656,747],"user":[108,684],"documentation":[109],"form":[113],"Unix":[115],"manual":[116],"pages,":[117],"program":[120,158,510,521,605],"shell":[123],"wrapper,":[124],"so":[125,272],"users":[126],"do":[127],"not":[128,267,473],"require":[129],"any":[130],"knowledge":[131],"Java.":[133],"provides":[137,253],"flexibility,":[138],"allowing":[140],"be":[143,160,197,235,450,459,484,595,616,660],"imported":[144],"exported":[146],"software,":[149],"transparency,":[151],"because":[152],"output":[154,207,548,609,620],"can":[159,234,449,458,483,532,594,606,614,659],"analyzed":[161],"detail.":[163],"Fig.":[164],"1":[165],"illustrates":[166],"pipeline.":[168],"processes":[170],"all":[171],"voxel":[174,181,195,270,386,438,712],"order,":[175,271,713],"where":[176,468],"measurements":[178,699],"are":[182,352,495,675],"stored":[183,702],"together.":[184],"ordering":[186],"facilitates":[187],"model":[191],"processed":[198],"independently,":[199],"simplifies":[201],"parallel":[202],"processing.":[203],"All":[204,551],"Camino\u2019s":[206,243,623],"documented":[211],"binary":[213,705],"format.":[214,282],"tractography":[216,625],"module":[217],"optionally":[218],"outputs":[219,511],"Analyze":[220,678],"images":[221],"visualisation":[226],"Data":[228,263],"source":[231,752],"scanner":[240,250,650,692],"or":[241,558,579,664],"synthesiser.":[245],"synthesiser":[248],"emulates":[249],"sequences":[251],"synthetic":[254],"range":[258,326],"customisable":[260],"test":[261],"functions.":[262],"scanners":[265],"typically":[268],"contains":[274],"tools":[275],"rearranging":[277],"into":[279],"correct":[281],"Associated":[283],"file":[287,296,720],"scheme":[290,719],"file,":[291],"text":[295],"describes":[298],"acquisition":[300],"parameters":[301],"measurement.":[304],"Reconstruction":[305],"reconstruction":[307,331],"takes":[309,375],"as":[310,577,635,677,703],"input":[311],"reconstructs":[315],"information":[316],"about":[317,376],"voxel.":[322,562],"supports":[324,406,419,587],"standard":[328,356,391],"advanced":[330],"algorithms.":[332],"simplest":[334],"fastest":[336],"inversion":[337],"linear":[340,357,371],"tensor":[342,351,373,410,471,542,600,717],"fit,":[343],"elements":[347],"calculated":[353,596,617],"least-squares":[358,408],"fit":[359,374,714],"log":[362],"measurements.":[365],"With":[366],"dtfit":[368,611,739],"program,":[369],"two":[377,492,557],"minutes":[378],"128":[381,383],"!":[382,384],"60":[385,698,735],"set,":[388],"on":[389,564],"Pentium":[392],"IV":[393],"2.4":[394],"GHz":[395],"workstation.":[396],"Nonlinear":[397],"fitting":[398,411,464],"slower":[400],"but":[401],"potentially":[402],"more":[403],"accurate.":[404],"nonlinear":[407],"RESTORE":[414],"method":[415],"[1].":[416],"also":[418,615],"non-tensor":[420],"based":[421],"reconstruction,":[423,463],"including":[424],"mixture":[425],"modelling":[426],"[2],":[427],"PAS-MRI":[428],"[3],":[429],"Q-ball":[430],"[4]":[431],"spherical":[433],"deconvolution":[434],"[5].":[435],"Spherical":[436],"harmonic":[437],"classification":[439,448,457],"[6]":[440],"detects":[441],"non-Gaussian":[442],"diffusion.":[443],"thresholds":[445],"set":[451],"interactively":[452],"(Fig.":[453,673,728],"2)":[454],"drive":[462],"multi-fibre":[465],"models":[466],"only":[467],"suitable.":[474],"Features":[475],"Voxel-based":[478],"extracted":[485],"reconstructed":[488],"data.":[489],"For":[490],"tensors,":[491],"common":[493],"trace":[497],"(computed":[498,505],"trd)":[500],"fractional":[503],"anisotropy":[504],"fa).":[507],"dteig":[509],"entire":[513],"eigen":[514],"system":[515],"tensor.":[518],"shredder":[520],"extracts":[522],"periodic":[523],"chunks":[524],"composite":[526],"sets,":[528],"example":[530],"it":[531],"create":[533],"an":[534,725],"image":[535],"containing":[536],"largest":[538],"eigenvalue":[539],"volume,":[545],"given":[546],"dteig.":[550],"these":[553],"programs":[554],"handle":[555],"one,":[556],"three":[559],"tensors":[560],"per":[561,649,654,700],"Statistics":[563],"populations":[565],"voxels":[567],"include":[568],"mean":[570],"variance":[572],"scalar":[574],"measures":[575],"such":[576],"anisotropy,":[578],"concentration":[581],"orientations.":[584],"Tractography":[585],"deterministic":[588],"probabilistic":[590,624],"streamline":[591],"tractography.":[592],"Streamlines":[593,613],"quickly":[597],"data,":[601],"since":[602],"track":[604],"read":[607],"directly.":[612],"sfpeaks.":[622],"uses":[626],"PICo":[628,642,669],"algorithm":[629],"[7]":[630],"improvements":[632],"refinements":[634],"described":[636],"[8].":[638],"calibration":[640],"automated":[644],"carried":[646],"out":[647],"once":[648],"sequence":[651],"(rather":[652],"than":[653],"subject).":[655],"streamlines":[658],"OOGL":[665],"(Geomview)":[666],"format,":[667],"while":[668],"connection":[670],"probability":[671],"maps":[672],"3)":[674],"images.":[679],"Example":[680],"We":[681],"demonstrate":[682],"interface":[685],"simple":[688],"example.":[689],"Starting":[690],"scan":[696],"voxel,":[701],"floats":[706],"SubjA.Bfloat,":[708],"we":[709],"convert":[710],"(the":[718],"A.scheme),":[722],"extract":[724],"FA":[726],"map":[727],"2).":[729],"cat":[730],"SubjA.Bfloat":[731],"|":[732,738,742],"scanner2voxel":[733],"\u2010components":[734],"-voxels":[736],"983040":[737],"-":[740],"A.scheme":[741],"fa":[743],">":[744],"SubjA.fa.Bdouble":[745],"Availability":[746],"latest":[748],"version":[749],"code":[753],"web":[759],"site:":[760],"http://www.cs.ucl.ac.uk/research/medic/camino.":[761]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":32},{"year":2019,"cited_by_count":23},{"year":2018,"cited_by_count":39},{"year":2017,"cited_by_count":34},{"year":2016,"cited_by_count":34},{"year":2015,"cited_by_count":46},{"year":2014,"cited_by_count":36},{"year":2013,"cited_by_count":38},{"year":2012,"cited_by_count":30}],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2016-06-24T00:00:00"}
