{"id":"https://openalex.org/W4402952861","doi":"https://doi.org/10.48550/arxiv.2408.16481","title":"A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising","display_name":"A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising","publication_year":2024,"publication_date":"2024-08-29","ids":{"openalex":"https://openalex.org/W4402952861","doi":"https://doi.org/10.48550/arxiv.2408.16481"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2408.16481","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.16481","pdf_url":"https://arxiv.org/pdf/2408.16481","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2408.16481","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100586972","display_name":"Shuaiyu Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Shuaiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012867488","display_name":"Tristan Whitmarsh","orcid":"https://orcid.org/0000-0001-8272-9505"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Whitmarsh, Tristan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002261838","display_name":"Dimitri A. Kessler","orcid":"https://orcid.org/0000-0002-1813-1039"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kessler, Dimitri A","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001393159","display_name":"Otso Arponen","orcid":"https://orcid.org/0000-0002-8541-0126"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arponen, Otso","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042960230","display_name":"Mary A. McLean","orcid":"https://orcid.org/0000-0002-3752-0179"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"McLean, Mary A","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035339853","display_name":"Gabrielle Baxter","orcid":"https://orcid.org/0000-0002-8242-1559"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baxter, Gabrielle","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061891450","display_name":"Frank Riemer","orcid":"https://orcid.org/0000-0002-3805-5221"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Riemer, Frank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086573178","display_name":"Aneurin J. Kennerley","orcid":"https://orcid.org/0000-0002-7599-7461"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kennerley, Aneurin J","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023299117","display_name":"William J. Brackenbury","orcid":"https://orcid.org/0000-0001-6882-3351"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brackenbury, William J","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061280047","display_name":"Fiona J. Gilbert","orcid":"https://orcid.org/0000-0002-0124-9962"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gilbert, Fiona J","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075112404","display_name":"Joshua Kaggie","orcid":"https://orcid.org/0000-0001-6706-3442"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaggie, Joshua D","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":11,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9541000127792358,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9351000189781189,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7348108291625977},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.6425010561943054},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6329138278961182},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6327033042907715},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5506158471107483},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.531473696231842},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5058795809745789},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44469690322875977},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4401811361312866},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4382481873035431},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12671807408332825},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11703738570213318}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7348108291625977},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.6425010561943054},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6329138278961182},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6327033042907715},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5506158471107483},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.531473696231842},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5058795809745789},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44469690322875977},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4401811361312866},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4382481873035431},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12671807408332825},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11703738570213318},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2408.16481","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.16481","pdf_url":"https://arxiv.org/pdf/2408.16481","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2408.16481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2408.16481","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2408.16481","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.16481","pdf_url":"https://arxiv.org/pdf/2408.16481","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2446802752","display_name":null,"funder_award_id":"A25922","funder_id":"https://openalex.org/F4320319985","funder_display_name":"Cancer Research UK"},{"id":"https://openalex.org/G4709757313","display_name":"Targeting sodium signalling to combat the disease-driving effects of tumour hypoxia","funder_award_id":"MR/X018067/1","funder_id":"https://openalex.org/F4320334626","funder_display_name":"Medical Research Council"},{"id":"https://openalex.org/G581313191","display_name":null,"funder_award_id":"EP/T022159/","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7002347110","display_name":"Cambridge Service for Data Driven Discovery (CSD3) - A National Data Intensive Science Cloud for Converged Simulation, AI & Analytics","funder_award_id":"EP/T022159/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320312927","display_name":"Dell EMC","ror":"https://ror.org/05rejmm18"},{"id":"https://openalex.org/F4320319985","display_name":"Cancer Research UK","ror":"https://ror.org/054225q67"},{"id":"https://openalex.org/F4320319990","display_name":"National Institute for Health and Care Research","ror":"https://ror.org/0187kwz08"},{"id":"https://openalex.org/F4320334626","display_name":"Medical Research Council","ror":"https://ror.org/03x94j517"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"},{"id":"https://openalex.org/F4320334632","display_name":"Science and Technology Facilities Council","ror":"https://ror.org/057g20z61"},{"id":"https://openalex.org/F4320336709","display_name":"NIHR Cambridge Biomedical Research Centre","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402952861.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2895947835","https://openalex.org/W2087258800","https://openalex.org/W2810018092","https://openalex.org/W2387428419","https://openalex.org/W4401571043","https://openalex.org/W2098237619","https://openalex.org/W1974034585","https://openalex.org/W2353444452","https://openalex.org/W2001438600","https://openalex.org/W2499707420"],"abstract_inverted_index":{"New":[0],"multinuclear":[1],"MRI":[2],"techniques,":[3],"such":[4,22,57],"as":[5,23,55,58],"sodium":[6,60,196],"MRI,":[7],"generally":[8],"suffer":[9],"from":[10,82,134],"low":[11,18],"image":[12,24,30,171],"quality":[13,179],"due":[14],"to":[15,70,121],"an":[16,224],"inherently":[17],"signal.":[19],"Postprocessing":[20],"methods,":[21],"denoising,":[25],"have":[26,112],"been":[27],"developed":[28],"for":[29,176],"enhancement.":[31],"However,":[32],"the":[33,130,135,150,166,169,173,178,181,220,232],"assessment":[34],"of":[35,48,180,229],"these":[36],"enhanced":[37],"images":[38,54,161,193,198],"is":[39,45],"challenging":[40],"especially":[41],"considering":[42],"when":[43],"there":[44],"a":[46,113,122,145,203,216],"lack":[47,97],"high":[49,52],"resolution":[50],"and":[51,172,194,211],"signal":[53],"reference,":[56],"in":[59,102,116],"MRI.":[61],"No-reference":[62],"Image":[63],"Quality":[64],"Assessment":[65],"(NR-IQA)":[66],"metrics":[67,77,87],"are":[68,93,119],"approaches":[69],"solve":[71],"this":[72],"problem.":[73],"Existing":[74],"learning-based":[75],"NR-IQA":[76,148,234],"rely":[78],"on":[79,159,186,207],"labels":[80],"derived":[81],"subjective":[83],"human":[84],"opinions":[85],"or":[86,96,162],"like":[88],"Signal-to-Noise":[89],"Ratio":[90],"(SNR),":[91],"which":[92,155],"either":[94],"time-consuming":[95],"accurate":[98],"ground":[99],"truths,":[100],"resulting":[101],"unreliable":[103],"assessment.":[104],"We":[105],"note":[106],"that":[107,117,127,200],"deep":[108],"learning":[109],"(DL)":[110],"models":[111],"unique":[114],"characteristic":[115,123],"they":[118],"specialized":[120],"training":[124,136],"set,":[125],"meaning":[126],"deviations":[128],"between":[129,168],"input":[131,170,182],"testing":[132],"data":[133,137],"will":[138],"reduce":[139],"prediction":[140,175],"accuracy.":[141],"Therefore,":[142],"we":[143],"propose":[144],"novel":[146],"DL-based":[147],"metric,":[149],"Model":[151],"Specialization":[152],"Metric":[153],"(MSM),":[154],"does":[156],"not":[157],"depend":[158],"ground-truth":[160],"labels.":[163],"MSM":[164,201,213],"measures":[165],"difference":[167],"model's":[174],"evaluating":[177],"image.":[183],"Experiments":[184],"conducted":[185],"both":[187],"simulated":[188,209],"distorted":[189],"proton":[190],"T1-weighted":[191],"MR":[192,197],"denoised":[195],"demonstrate":[199],"exhibits":[202],"superior":[204],"evaluation":[205],"performance":[206],"various":[208],"noises":[210],"distortions.":[212],"also":[214],"has":[215],"substantial":[217],"agreement":[218],"with":[219],"expert":[221],"evaluations,":[222],"achieving":[223],"averaged":[225],"Cohen's":[226],"Kappa":[227],"coefficient":[228],"0.6528,":[230],"outperforming":[231],"existing":[233],"metrics.":[235]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2024-09-29T00:00:00"}