{"id":"https://openalex.org/W4401752137","doi":"https://doi.org/10.1109/spcom60851.2024.10631627","title":"A Frequency Domain Network for Hybrid-Domain Super-Resolution MRI","display_name":"A Frequency Domain Network for Hybrid-Domain Super-Resolution MRI","publication_year":2024,"publication_date":"2024-07-01","ids":{"openalex":"https://openalex.org/W4401752137","doi":"https://doi.org/10.1109/spcom60851.2024.10631627"},"language":"en","primary_location":{"id":"doi:10.1109/spcom60851.2024.10631627","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/spcom60851.2024.10631627","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Signal Processing and Communications (SPCOM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5066158538","display_name":"Vazim Ibrahim","orcid":"https://orcid.org/0000-0002-9557-8758"},"institutions":[{"id":"https://openalex.org/I158338959","display_name":"University of Kerala","ror":"https://ror.org/05tqa9940","country_code":"IN","type":"education","lineage":["https://openalex.org/I158338959"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vazim Ibrahim","raw_affiliation_strings":["School of Electronic Systems and Automation, Digital University Kerala,Trivandrum,Kerala,India,695317"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Systems and Automation, Digital University Kerala,Trivandrum,Kerala,India,695317","institution_ids":["https://openalex.org/I158338959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033569193","display_name":"Joseph Suresh Paul","orcid":"https://orcid.org/0000-0002-2896-6072"},"institutions":[{"id":"https://openalex.org/I158338959","display_name":"University of Kerala","ror":"https://ror.org/05tqa9940","country_code":"IN","type":"education","lineage":["https://openalex.org/I158338959"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Joseph Suresh Paul","raw_affiliation_strings":["School of Electronic Systems and Automation, Digital University Kerala,Trivandrum,Kerala,India,695317"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Systems and Automation, Digital University Kerala,Trivandrum,Kerala,India,695317","institution_ids":["https://openalex.org/I158338959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I158338959"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31638418,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9921000003814697,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9921000003814697,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.6882368326187134},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6053158640861511},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5231146216392517},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4172170162200928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20331549644470215},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.12346291542053223},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10188165307044983}],"concepts":[{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.6882368326187134},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6053158640861511},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5231146216392517},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4172170162200928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20331549644470215},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.12346291542053223},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10188165307044983},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/spcom60851.2024.10631627","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/spcom60851.2024.10631627","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Signal Processing and Communications (SPCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2187351272","https://openalex.org/W2194775991","https://openalex.org/W2599414549","https://openalex.org/W2780544323","https://openalex.org/W2795380527","https://openalex.org/W2897421580","https://openalex.org/W2952773607","https://openalex.org/W2953977469","https://openalex.org/W2962734274","https://openalex.org/W2963446712","https://openalex.org/W2966737464","https://openalex.org/W2972061446","https://openalex.org/W2976922789","https://openalex.org/W2983469640","https://openalex.org/W3000998666","https://openalex.org/W3045888007","https://openalex.org/W3093127150","https://openalex.org/W3101162162","https://openalex.org/W3101204238","https://openalex.org/W3101500493","https://openalex.org/W3102018640","https://openalex.org/W3106180885","https://openalex.org/W3134103558","https://openalex.org/W3140469881","https://openalex.org/W3157684158","https://openalex.org/W3166971825","https://openalex.org/W3175178093","https://openalex.org/W3190218712","https://openalex.org/W4205119575","https://openalex.org/W4225506672","https://openalex.org/W4297450596","https://openalex.org/W4304080867","https://openalex.org/W4308555638","https://openalex.org/W4312934855","https://openalex.org/W4321487431","https://openalex.org/W4323519308","https://openalex.org/W4367276800"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Hybrid-domain":[0],"networks":[1,34,177],"with":[2,129,139],"sequentially":[3],"or":[4,184],"parallelly":[5],"connected":[6],"Frequency-domain":[7],"(FDN)":[8],"and":[9,169,187],"Spatial-domain":[10],"(SDN)":[11],"networks,":[12],"have":[13],"been":[14],"extensively":[15],"used":[16,144],"for":[17,95],"Compressed-Sensing":[18],"MRI":[19,52],"(CS-MRI).":[20],"As":[21],"CS-MRI":[22,55],"scans":[23],"involve":[24],"randomly":[25],"acquired":[26],"high-frequency":[27,60,76,97,136],"data":[28,77,137],"through":[29],"variable-density":[30],"(VD)":[31],"sampling,":[32],"hybrid-domain":[33,92,167],"are":[35,112],"trained":[36],"to":[37,84,108,115,157,164],"distinguish":[38,109],"incoherent":[39],"structural":[40],"artefacts":[41,110],"from":[42,54,67,78,100,196],"the":[43,57,64,75,87,91,116,125,130,134,165,172],"true":[44],"image":[45,65,118],"features.":[46],"A":[47],"key":[48],"difference":[49],"of":[50,59,74,90,132,171,192],"super-resolution":[51,160],"(SR-MRI)":[53],"is":[56,121,155],"absence":[58],"measurements.":[61],"Therefore,":[62],"super-resolving":[63,193],"structures":[66],"a":[68,79,101,146,197],"truncated":[69,102],"acquisition":[70,104],"would":[71],"entail":[72],"estimation":[73],"low-resolution":[80],"(LR)":[81],"scan.":[82],"Due":[83],"this":[85],"reason,":[86],"frequency-domain":[88,126],"component":[89],"CNN":[93,127],"architecture":[94,128,186],"recovering":[96],"(HF)":[98],"information":[99],"Fourier":[103],"should":[105],"be":[106],"designed":[107],"that":[111],"highly":[113],"correlated":[114],"underlying":[117],"structures.":[119],"This":[120],"achieved":[122],"by":[123,178],"designing":[124],"goal":[131],"extrapolating":[133],"unknown":[135],"points":[138],"improved":[140,159],"filling":[141],"factor.":[142],"When":[143],"in":[145,162],"hybrid":[147],"domain":[148,175],"configuration,":[149],"Frequency":[150],"Domain":[151],"Super-Resolution":[152],"Network":[153],"(FDSRN)":[154],"able":[156],"achieve":[158],"performance":[161],"comparison":[163],"existing":[166],"networks;":[168],"some":[170],"recent":[173],"spatial":[174],"deep":[176],"employing":[179],"either":[180],"multi-contrast":[181],"training":[182],"samples,":[183],"multi-streaming":[185],"having":[188],"selectively":[189],"demonstrated":[190],"capability":[191],"LR":[194],"images":[195],"band-limited":[198],"acquisition.":[199]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
