{"id":"https://openalex.org/W7134929077","doi":"https://doi.org/10.1109/icdmw69685.2025.00016","title":"Monte Carlo Synthetic Data Generation for Radiograph Denoising","display_name":"Monte Carlo Synthetic Data Generation for Radiograph Denoising","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7134929077","doi":"https://doi.org/10.1109/icdmw69685.2025.00016"},"language":null,"primary_location":{"id":"doi:10.1109/icdmw69685.2025.00016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","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/A5026802012","display_name":"Eric Roginek","orcid":null},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Eric W. Roginek","raw_affiliation_strings":["Fordham University,Los Alamos National Laboratory"],"affiliations":[{"raw_affiliation_string":"Fordham University,Los Alamos National Laboratory","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010811821","display_name":"Joel Kulesza","orcid":"https://orcid.org/0000-0002-2669-6339"},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joel A. Kulesza","raw_affiliation_strings":["Los Alamos National Laboratory"],"affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049423390","display_name":"Yijun Zhao","orcid":"https://orcid.org/0000-0003-2424-5988"},"institutions":[{"id":"https://openalex.org/I164389053","display_name":"Fordham University","ror":"https://ror.org/03qnxaf80","country_code":"US","type":"education","lineage":["https://openalex.org/I164389053"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yijun Zhao","raw_affiliation_strings":["Fordham University"],"affiliations":[{"raw_affiliation_string":"Fordham University","institution_ids":["https://openalex.org/I164389053"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026802012"],"corresponding_institution_ids":["https://openalex.org/I1343871089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76726453,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"86","last_page":"95"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.39340001344680786,"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":0.39340001344680786,"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":0.04270000010728836,"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"}},{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.03799999877810478,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.6093999743461609},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5164999961853027},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4431000053882599},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.41359999775886536},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3375999927520752}],"concepts":[{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.6093999743461609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5960000157356262},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5418000221252441},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44749999046325684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4431000053882599},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3779999911785126},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3375999927520752},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2709999978542328},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2639000117778778}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdmw69685.2025.00016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","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":27,"referenced_works":["https://openalex.org/W1510355813","https://openalex.org/W1567413565","https://openalex.org/W1580389772","https://openalex.org/W1899329334","https://openalex.org/W2007339694","https://openalex.org/W2025768430","https://openalex.org/W2056370875","https://openalex.org/W2067681708","https://openalex.org/W2097073572","https://openalex.org/W2099244020","https://openalex.org/W2123936229","https://openalex.org/W2125527601","https://openalex.org/W2161037052","https://openalex.org/W2229725544","https://openalex.org/W2242218935","https://openalex.org/W2508457857","https://openalex.org/W2611650229","https://openalex.org/W2948077267","https://openalex.org/W2963640123","https://openalex.org/W2983315964","https://openalex.org/W3003257820","https://openalex.org/W3155072588","https://openalex.org/W3204590007","https://openalex.org/W4298118611","https://openalex.org/W4382315317","https://openalex.org/W4383104596","https://openalex.org/W4402392401"],"related_works":[],"abstract_inverted_index":{"High-quality":[0],"medical":[1],"radiographs":[2,44,170],"are":[3,11,121],"essential":[4],"for":[5,104,190],"accurate":[6,168],"diagnosis,":[7],"yet":[8],"realistic":[9,79],"datasets":[10],"scarce,":[12],"subject":[13],"to":[14,39,98,129,175,186],"privacy":[15],"constraints":[16],"and":[17,100,109,132,154,163],"often":[18],"lack":[19],"real-world":[20,200],"noise":[21],"distributions.":[22],"In":[23],"this":[24],"study,":[25],"we":[26],"use":[27],"the":[28,161],"Monte":[29,172],"Carlo":[30,173],"N-Particle":[31],"(MCNP)":[32],"code's":[33],"Flux":[34],"Image":[35],"Radiograph":[36],"(FIR)":[37],"tally":[38],"generate":[40],"simulated":[41],"low-resolution":[42],"X-ray":[43],"of":[45,59,65,165],"a":[46,53,183],"simple":[47],"phantom,":[48],"an":[49],"iron":[50],"cube":[51],"containing":[52],"tungsten":[54],"sphere,":[55],"at":[56,124],"two":[57],"levels":[58],"statistical":[60,69],"sampling.":[61],"Low-sample":[62],"simulations":[63,83],"consisting":[64],"10<sup":[66,85],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[67,86],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">6</sup>":[68],"samples":[70,88],"(i.e.,":[71],"source":[72],"photons)":[73],"yield":[74],"noisy":[75,126],"images":[76],"with":[77,84,171],"physically":[78,167],"noise,":[80],"while":[81],"high-sample":[82],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">8</sup>":[87],"provide":[89],"denoised":[90],"ground-truth":[91],"images.":[92],"This":[93,180],"paired":[94],"dataset":[95],"is":[96],"used":[97],"train":[99],"compare":[101],"several":[102],"models":[103,120,189],"radiographic":[105],"denoising,":[106],"including":[107,141],"interpolation-based":[108],"deep":[110,118],"learning":[111,119],"approaches.":[112],"Our":[113],"experiments":[114],"show":[115],"all":[116,138],"three":[117],"highly":[122],"effective":[123],"mapping":[125],"low-sample":[127],"inputs":[128],"high-quality":[130],"outputs":[131],"outperform":[133],"traditional":[134],"denoising":[135,192],"methods":[136,174],"across":[137],"evaluation":[139],"metrics,":[140],"mean":[142],"squared":[143],"error":[144],"(MSE),":[145],"peak":[146],"signal-to-noise":[147],"ratio":[148],"(PSNR),":[149],"structural":[150],"similarity":[151],"index":[152],"(SSIM),":[153],"multi-scale":[155],"SSIM":[156],"(MS-SSIM).":[157],"These":[158],"results":[159],"demonstrate":[160],"feasibility":[162],"advantages":[164],"using":[166],"synthetic":[169],"develop":[176],"AI-driven":[177],"enhancement":[178],"techniques.":[179],"approach":[181],"offers":[182],"privacy-preserving":[184],"path":[185],"benchmark":[187],"generative":[188],"image":[191],"in":[193,199],"healthcare,":[194],"potentially":[195],"improving":[196],"model":[197],"generalizability":[198],"low-dose":[201],"imaging":[202],"scenarios.":[203]},"counts_by_year":[],"updated_date":"2026-03-13T14:20:09.374765","created_date":"2026-03-12T00:00:00"}
