{"id":"https://openalex.org/W4408354719","doi":"https://doi.org/10.1109/icassp49660.2025.10889078","title":"Learning to Optimally Sample in MRI for Denoising-Driven Regularization","display_name":"Learning to Optimally Sample in MRI for Denoising-Driven Regularization","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354719","doi":"https://doi.org/10.1109/icassp49660.2025.10889078"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","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/A5007086799","display_name":"Pavan Kumar Reddy K","orcid":"https://orcid.org/0000-0002-2800-8755"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pavan Kumar Reddy K","raw_affiliation_strings":["TCS Research,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research,Bengaluru,India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045035201","display_name":"Kunal N. Chaudhury","orcid":"https://orcid.org/0000-0002-8136-605X"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kunal N. Chaudhury","raw_affiliation_strings":["Indian Institute of Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01708426,"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/T10320","display_name":"Neural Networks and Applications","score":0.9395999908447266,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9395999908447266,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9243000149726868,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.907800018787384,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.7099810838699341},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6401060819625854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6008191108703613},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5659868717193604},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.5403426289558411},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4947304427623749},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41491299867630005},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3491763472557068},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07268071174621582}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7099810838699341},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6401060819625854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6008191108703613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5659868717193604},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.5403426289558411},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4947304427623749},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41491299867630005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3491763472557068},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07268071174621582},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334771","display_name":"Science and Engineering Research Board","ror":"https://ror.org/03ffdsr55"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1831416753","https://openalex.org/W1993483669","https://openalex.org/W1998419211","https://openalex.org/W2029816571","https://openalex.org/W2040319606","https://openalex.org/W2056370875","https://openalex.org/W2097073572","https://openalex.org/W2100556411","https://openalex.org/W2130754596","https://openalex.org/W2296055064","https://openalex.org/W2508457857","https://openalex.org/W2573726823","https://openalex.org/W2798774029","https://openalex.org/W2907696891","https://openalex.org/W2908554698","https://openalex.org/W2944761405","https://openalex.org/W2951760975","https://openalex.org/W2963399478","https://openalex.org/W2963439178","https://openalex.org/W2970545380","https://openalex.org/W2999778662","https://openalex.org/W3050528501","https://openalex.org/W3159379662","https://openalex.org/W3167568784","https://openalex.org/W4244393449","https://openalex.org/W4292363360","https://openalex.org/W4293775970","https://openalex.org/W4304480823","https://openalex.org/W4385804996","https://openalex.org/W6685335849","https://openalex.org/W6755625687"],"related_works":["https://openalex.org/W4287876945","https://openalex.org/W2087258800","https://openalex.org/W2810018092","https://openalex.org/W4407169614","https://openalex.org/W2387428419","https://openalex.org/W1581044291","https://openalex.org/W4401571043","https://openalex.org/W2098237619","https://openalex.org/W3209466624","https://openalex.org/W1974034585"],"abstract_inverted_index":{"The":[0],"reconstruction":[1,74],"quality":[2],"in":[3,90],"compressed":[4],"sensing":[5],"MRI":[6],"can":[7,97],"be":[8,98],"significantly":[9,122],"improved":[10],"by":[11,42,115],"optimizing":[12],"the":[13,49,61,64,67,73,80,85,95,107,111,124,128],"k-space":[14],"sampling.":[15,86],"While":[16],"previous":[17],"works":[18],"have":[19,33],"mainly":[20],"focused":[21],"on":[22],"total":[23],"variation":[24],"and":[25],"other":[26],"traditional":[27],"regularizers,":[28],"more":[29],"recent":[30],"denoising-driven":[31],"regularizers":[32],"not":[34],"been":[35],"fully":[36],"explored.":[37],"We":[38,126],"address":[39],"this":[40,91],"gap":[41],"developing":[43],"a":[44,102],"computational":[45],"framework":[46],"to":[47,72,83],"learn":[48],"optimal":[50],"sampling":[51,119],"for":[52,101,133],"Plug-and-Play":[53],"(PnP)":[54],"regularization.":[55],"A":[56,87],"technical":[57],"challenge":[58],"here":[59],"is":[60,77,93],"computation":[62],"of":[63,66,130],"gradient":[65,96],"training":[68],"loss":[69],"with":[70],"respect":[71],"variable,":[75],"which":[76],"used":[78],"within":[79],"learning":[81],"algorithm":[82],"optimize":[84],"notable":[88],"finding":[89],"direction":[92],"that":[94,121],"computed":[99],"analytically":[100],"kernel":[103],"denoiser":[104],"such":[105],"as":[106],"nonlocal":[108],"means.":[109],"Moreover,":[110],"superior":[112],"regularization":[113],"offered":[114],"PnP":[116],"helps":[117],"discover":[118],"patterns":[120],"improve":[123],"reconstruction.":[125],"demonstrate":[127],"effectiveness":[129],"our":[131],"proposal":[132],"different":[134],"anatomical":[135],"datasets.":[136]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
