{"id":"https://openalex.org/W2767238880","doi":"https://doi.org/10.1109/icdsp.2017.8096124","title":"Permuted cubes wavelet thresholding for mask-sensed MRI","display_name":"Permuted cubes wavelet thresholding for mask-sensed MRI","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2767238880","doi":"https://doi.org/10.1109/icdsp.2017.8096124","mag":"2767238880"},"language":"en","primary_location":{"id":"doi:10.1109/icdsp.2017.8096124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2017.8096124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Digital Signal Processing (DSP)","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/A5083433486","display_name":"Sebastian Schmale","orcid":null},"institutions":[{"id":"https://openalex.org/I180437899","display_name":"University of Bremen","ror":"https://ror.org/04ers2y35","country_code":"DE","type":"education","lineage":["https://openalex.org/I180437899"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Sebastian Schmale","raw_affiliation_strings":["Institute of Electrodynamics and Microelectronics (ITEM.me), University of Bremen, Bremen, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Electrodynamics and Microelectronics (ITEM.me), University of Bremen, Bremen, Germany","institution_ids":["https://openalex.org/I180437899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015103826","display_name":"Pascal Seidel","orcid":null},"institutions":[{"id":"https://openalex.org/I180437899","display_name":"University of Bremen","ror":"https://ror.org/04ers2y35","country_code":"DE","type":"education","lineage":["https://openalex.org/I180437899"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Pascal Seidel","raw_affiliation_strings":["Institute of Electrodynamics and Microelectronics (ITEM.me), University of Bremen, Bremen, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Electrodynamics and Microelectronics (ITEM.me), University of Bremen, Bremen, Germany","institution_ids":["https://openalex.org/I180437899"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101959993","display_name":"Steffen Paul","orcid":"https://orcid.org/0000-0003-3392-0471"},"institutions":[{"id":"https://openalex.org/I180437899","display_name":"University of Bremen","ror":"https://ror.org/04ers2y35","country_code":"DE","type":"education","lineage":["https://openalex.org/I180437899"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Steffen Paul","raw_affiliation_strings":["Institute of Electrodynamics and Microelectronics (ITEM.me), University of Bremen, Bremen, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Electrodynamics and Microelectronics (ITEM.me), University of Bremen, Bremen, Germany","institution_ids":["https://openalex.org/I180437899"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083433486"],"corresponding_institution_ids":["https://openalex.org/I180437899"],"apc_list":null,"apc_paid":null,"fwci":0.091,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4805813,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9994999766349792,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/thresholding","display_name":"Thresholding","score":0.7938200235366821},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7203623056411743},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6945379972457886},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6778616905212402},{"id":"https://openalex.org/keywords/jpeg-2000","display_name":"JPEG 2000","score":0.6669924259185791},{"id":"https://openalex.org/keywords/inpainting","display_name":"Inpainting","score":0.5726417899131775},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5444319248199463},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4684826135635376},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4478052854537964},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4383643865585327},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4262514114379883},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4122248888015747},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16800940036773682},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.14319178462028503},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.1370793581008911}],"concepts":[{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.7938200235366821},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7203623056411743},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6945379972457886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6778616905212402},{"id":"https://openalex.org/C69216139","wikidata":"https://www.wikidata.org/wiki/Q931783","display_name":"JPEG 2000","level":5,"score":0.6669924259185791},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.5726417899131775},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5444319248199463},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4684826135635376},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4478052854537964},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4383643865585327},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4262514114379883},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4122248888015747},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16800940036773682},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.14319178462028503},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.1370793581008911},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdsp.2017.8096124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2017.8096124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Digital Signal Processing (DSP)","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":12,"referenced_works":["https://openalex.org/W1972499291","https://openalex.org/W1974438823","https://openalex.org/W1998749136","https://openalex.org/W2000269560","https://openalex.org/W2029816571","https://openalex.org/W2044810215","https://openalex.org/W2050470336","https://openalex.org/W2080492017","https://openalex.org/W2164278908","https://openalex.org/W2304034118","https://openalex.org/W3123837026","https://openalex.org/W4292363360"],"related_works":["https://openalex.org/W2380775572","https://openalex.org/W2213520135","https://openalex.org/W4242046654","https://openalex.org/W1504109132","https://openalex.org/W3174923100","https://openalex.org/W3134074939","https://openalex.org/W2117562399","https://openalex.org/W2244018504","https://openalex.org/W4298074124","https://openalex.org/W2003548600"],"abstract_inverted_index":{"In":[0,91],"this":[1],"work":[2],"we":[3,119],"present":[4],"a":[5,51],"novel":[6],"inpainting":[7],"algorithm":[8,96],"to":[9,49,106],"gain":[10],"reduced":[11],"acquisition":[12],"time":[13,56],"and":[14,30,132],"high":[15,65],"quality":[16,71,113],"data":[17,35,84,109],"reconstruction":[18,70],"for":[19],"MRI":[20,24,117,138],"applications.":[21],"We":[22],"analyzed":[23],"recordings":[25],"of":[26,31,40,53,64,72,114,126,135],"two":[27],"synthetically":[28],"generated":[29],"one":[32],"real":[33],"measured":[34],"set.":[36],"On":[37],"the":[38,41,54,58,62,69,94,112,115,123,133],"basis":[39],"proposed":[42,95],"mask-based":[43],"sampling":[44],"trajectories,":[45],"patients":[46],"only":[47],"have":[48],"spend":[50],"fraction":[52],"recording":[55],"in":[57,61],"MRI.":[59],"Especially,":[60],"range":[63],"k-space":[66],"coefficient":[67],"reduction,":[68],"our":[73],"Permuted":[74],"Cubes":[75],"Wavelet":[76],"Thresholding":[77],"(PCWT)":[78],"approach":[79],"can":[80],"compete":[81],"with":[82,104],"standard":[83],"compression-focused":[85],"methods":[86],"like":[87],"JPEG2000":[88],"or":[89],"MPEG-4.":[90],"all":[92],"simulations,":[93],"also":[97],"outperforms":[98],"state-of-the-art":[99],"techniques":[100],"such":[101],"as":[102],"BM3D-MRI":[103],"respect":[105],"accuracy":[107],"after":[108],"reconstruction.":[110],"Regarding":[111],"approximated":[116],"data,":[118],"mainly":[120],"focus":[121],"on":[122],"clear":[124],"recovery":[125],"sharp":[127],"edges":[128],"without":[129],"undesirable":[130],"artifacts":[131],"identification":[134],"tumors":[136],"within":[137],"frames.":[139]},"counts_by_year":[{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
