{"id":"https://openalex.org/W7134921519","doi":"https://doi.org/10.48550/arxiv.2603.09621","title":"Physics-Driven 3D Gaussian Rendering for Zero-Shot MRI Super-Resolution","display_name":"Physics-Driven 3D Gaussian Rendering for Zero-Shot MRI Super-Resolution","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7134921519","doi":"https://doi.org/10.48550/arxiv.2603.09621"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.09621","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09621","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.09621","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080891881","display_name":"Shuting Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Shuting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128742512","display_name":"Lei Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128758560","display_name":"Wei Min Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128731999","display_name":"Zhao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126668030","display_name":"Zizhou Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zizhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.8633000254631042,"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":0.8633000254631042,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.045099999755620956,"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":0.010700000450015068,"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/rendering","display_name":"Rendering (computer graphics)","score":0.7057999968528748},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6460000276565552},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5027999877929688},{"id":"https://openalex.org/keywords/volume-rendering","display_name":"Volume rendering","score":0.4399999976158142},{"id":"https://openalex.org/keywords/real-time-mri","display_name":"Real-time MRI","score":0.4237000048160553},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.4221999943256378},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.40130001306533813},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.39640000462532043}],"concepts":[{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.7057999968528748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6764000058174133},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6460000276565552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5961999893188477},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5512999892234802},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5027999877929688},{"id":"https://openalex.org/C30769735","wikidata":"https://www.wikidata.org/wiki/Q2165951","display_name":"Volume rendering","level":3,"score":0.4399999976158142},{"id":"https://openalex.org/C157787499","wikidata":"https://www.wikidata.org/wiki/Q13479657","display_name":"Real-time MRI","level":3,"score":0.4237000048160553},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.40130001306533813},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.39640000462532043},{"id":"https://openalex.org/C36816356","wikidata":"https://www.wikidata.org/wiki/Q16911860","display_name":"3D rendering","level":3,"score":0.36800000071525574},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34290000796318054},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.33559998869895935},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.32899999618530273},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C104317376","wikidata":"https://www.wikidata.org/wiki/Q1894545","display_name":"Gaussian blur","level":5,"score":0.2671000063419342},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.2612000107765198},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.09621","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09621","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.09621","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09621","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-resolution":[0],"Magnetic":[1],"Resonance":[2],"Imaging":[3],"(MRI)":[4],"is":[5],"vital":[6],"for":[7,136],"clinical":[8,137],"diagnosis":[9],"but":[10],"limited":[11],"by":[12,38],"long":[13],"acquisition":[14],"times":[15],"and":[16,74,117,130],"motion":[17],"artifacts.":[18],"Super-resolution":[19],"(SR)":[20],"reconstructs":[21],"low-resolution":[22],"scans":[23],"into":[24],"high-resolution":[25],"images,":[26],"yet":[27],"existing":[28],"methods":[29,34],"are":[30],"mutually":[31],"constrained:":[32],"paired-data":[33],"achieve":[35],"efficiency":[36],"only":[37],"relying":[39],"on":[40,121],"costly":[41],"aligned":[42],"datasets,":[43],"while":[44,86],"implicit":[45],"neural":[46],"representation":[47,69],"approaches":[48],"avoid":[49],"such":[50],"data":[51,72],"needs":[52],"at":[53],"the":[54,133],"expense":[55],"of":[56],"heavy":[57],"computation.":[58],"We":[59],"propose":[60],"a":[61,105],"zero-shot":[62],"MRI":[63,97,124,138],"SR":[64],"framework":[65],"using":[66],"explicit":[67],"Gaussian":[68,77,102],"to":[70],"balance":[71],"requirements":[73],"efficiency.":[75],"MRI-tailored":[76],"parameters":[78,85],"embed":[79],"tissue":[80],"physical":[81],"properties,":[82],"reducing":[83],"learnable":[84],"preserving":[87],"MR":[88],"signal":[89,98],"fidelity.":[90],"A":[91],"physics-grounded":[92],"volume":[93],"rendering":[94],"strategy":[95],"models":[96],"formation":[99],"via":[100],"normalized":[101],"aggregation.":[103],"Additionally,":[104],"brick-based":[106],"order-independent":[107],"rasterization":[108],"scheme":[109],"enables":[110],"highly":[111],"parallel":[112],"3D":[113],"computation,":[114],"lowering":[115],"training":[116],"inference":[118],"costs.":[119],"Experiments":[120],"two":[122],"public":[123],"datasets":[125],"show":[126],"superior":[127],"reconstruction":[128],"quality":[129],"efficiency,":[131],"demonstrating":[132],"method's":[134],"potential":[135],"SR.":[139]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-12T00:00:00"}