{"id":"https://openalex.org/W2920809311","doi":"https://doi.org/10.1117/12.2512894","title":"Sharpness preserved sinogram synthesis using convolutional neural network for sparse-view CT imaging","display_name":"Sharpness preserved sinogram synthesis using convolutional neural network for sparse-view CT imaging","publication_year":2019,"publication_date":"2019-03-15","ids":{"openalex":"https://openalex.org/W2920809311","doi":"https://doi.org/10.1117/12.2512894","mag":"2920809311"},"language":"en","primary_location":{"id":"doi:10.1117/12.2512894","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Processing","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/A5101972788","display_name":"Jiaxing Tan","orcid":"https://orcid.org/0000-0001-9908-3691"},"institutions":[{"id":"https://openalex.org/I1327163397","display_name":"State University of New York","ror":"https://ror.org/01q1z8k08","country_code":"US","type":"education","lineage":["https://openalex.org/I1327163397"]},{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiaxing Tan","raw_affiliation_strings":["The City Univ. of New York (United States)","The State Univ. of New York (United States)"],"affiliations":[{"raw_affiliation_string":"The City Univ. of New York (United States)","institution_ids":["https://openalex.org/I174216632"]},{"raw_affiliation_string":"The State Univ. of New York (United States)","institution_ids":["https://openalex.org/I1327163397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028884087","display_name":"Yongfeng Gao","orcid":"https://orcid.org/0000-0001-6169-3478"},"institutions":[{"id":"https://openalex.org/I1327163397","display_name":"State University of New York","ror":"https://ror.org/01q1z8k08","country_code":"US","type":"education","lineage":["https://openalex.org/I1327163397"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongfeng Gao","raw_affiliation_strings":["The State Univ. of New York (United States)"],"affiliations":[{"raw_affiliation_string":"The State Univ. of New York (United States)","institution_ids":["https://openalex.org/I1327163397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055734882","display_name":"Yumei Huo","orcid":"https://orcid.org/0000-0003-3550-8843"},"institutions":[{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yumei Huo","raw_affiliation_strings":["The City Univ. of New York (United States)"],"affiliations":[{"raw_affiliation_string":"The City Univ. of New York (United States)","institution_ids":["https://openalex.org/I174216632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100711454","display_name":"Lihong Li","orcid":"https://orcid.org/0000-0002-5440-8242"},"institutions":[{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lihong Li","raw_affiliation_strings":["The City Univ. of New York (United States)"],"affiliations":[{"raw_affiliation_string":"The City Univ. of New York (United States)","institution_ids":["https://openalex.org/I174216632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110322946","display_name":"Zhengrong Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I1327163397","display_name":"State University of New York","ror":"https://ror.org/01q1z8k08","country_code":"US","type":"education","lineage":["https://openalex.org/I1327163397"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhengrong Liang","raw_affiliation_strings":["The State Univ. of New York (United States)"],"affiliations":[{"raw_affiliation_string":"The State Univ. of New York (United States)","institution_ids":["https://openalex.org/I1327163397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101972788"],"corresponding_institution_ids":["https://openalex.org/I1327163397","https://openalex.org/I174216632"],"apc_list":null,"apc_paid":null,"fwci":0.7086,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.74037217,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"13","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9997000098228455,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9997000098228455,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9950000047683716,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9887999892234802,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7575478553771973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7272282838821411},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6870830059051514},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5949174165725708},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5810723304748535},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.48395732045173645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4764186143875122},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4485701620578766},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4458824694156647},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4456503987312317},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40047159790992737},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24522250890731812}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7575478553771973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7272282838821411},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6870830059051514},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5949174165725708},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5810723304748535},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.48395732045173645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4764186143875122},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4485701620578766},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4458824694156647},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4456503987312317},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40047159790992737},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24522250890731812},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2512894","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1921523184","https://openalex.org/W1999051088","https://openalex.org/W2019816115","https://openalex.org/W2020820596","https://openalex.org/W2053790843","https://openalex.org/W2054393756","https://openalex.org/W2133665775","https://openalex.org/W2167150277","https://openalex.org/W2171697262","https://openalex.org/W2580080206","https://openalex.org/W2593226005","https://openalex.org/W3105751747","https://openalex.org/W4242177601","https://openalex.org/W6640185926","https://openalex.org/W6684610746","https://openalex.org/W6687483927","https://openalex.org/W6690498163","https://openalex.org/W6732341091","https://openalex.org/W6786000376"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W2004988775"],"abstract_inverted_index":{"Sparse":[0],"view":[1,36],"computed":[2],"tomography":[3],"(CT)":[4],"is":[5,68,111,146],"an":[6,94],"effective":[7],"way":[8],"to":[9,25,53,71,92,133,180],"lower":[10],"the":[11,20,41,45,61,64,72,114,123,130,135,191,202],"radiation":[12],"exposure,":[13],"but":[14],"results":[15],"in":[16,19,122,173,201],"streaking":[17],"artifacts":[18],"constructed":[21],"CT":[22,98],"image":[23],"due":[24],"insufficient":[26],"projection":[27],"views.":[28],"Several":[29],"approaches":[30],"have":[31],"been":[32],"reported":[33],"for":[34,139],"full":[35],"sinogram":[37,99],"synthesis":[38,100],"by":[39,113],"interpolating":[40],"missing":[42],"data":[43],"into":[44],"sparse-view":[46],"sinogram.":[47],"However,":[48],"current":[49,181],"interpolation":[50],"methods":[51],"tend":[52],"generate":[54],"over-smoothed":[55],"sinogram,":[56],"which":[57,145],"could":[58],"not":[59],"preserve":[60],"sharpness":[62,67,109],"of":[63,79,129,137,148,193],"image.":[65],"Such":[66],"often":[69],"referred":[70],"region":[73],"boundaries":[74],"or":[75],"tissue":[76],"texture":[77],"and":[78,116,153,166,176,196],"high":[80,150],"importance":[81],"as":[82],"clinical":[83],"indicators.":[84],"To":[85],"address":[86],"this":[87,89],"issue,":[88],"paper":[90],"aims":[91],"propose":[93],"efficient":[95],"sharpness-preserve":[96],"spare-view":[97],"method":[101],"based":[102,119],"on":[103,190,198],"convolutional":[104],"neural":[105],"network":[106,142],"(CNN).":[107],"The":[108,162],"preserving":[110],"stressed":[112],"zero-order":[115],"first-order":[117],"difference":[118],"loss":[120,167,203],"function":[121,168],"model.":[124],"This":[125,184],"study":[126,185],"takes":[127],"advantage":[128],"residual":[131],"design":[132,165],"overcome":[134],"problem":[136],"degradation":[138],"our":[140],"deep":[141],"(20":[143],"layers),":[144],"capable":[147],"extracting":[149],"level":[151],"information":[152],"dealing":[154],"with":[155],"large":[156],"sample":[157],"dimensions":[158],"(672":[159],"x":[160],"672).":[161],"proposed":[163],"model":[164],"achieved":[169],"a":[170],"better":[171],"performance":[172],"both":[174],"quantitative":[175],"qualitative":[177],"evaluation":[178],"comparing":[179],"state-of-the-art":[182],"works.":[183],"also":[186],"performs":[187],"ablation":[188],"test":[189],"effect":[192],"different":[194],"designs":[195],"researches":[197],"hyper-parameter":[199],"settings":[200],"function.":[204]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}