?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Enhancing+Spatiotemporal+Disease+Progression+Models+via%C2%A0Latent+Diffusion+and%C2%A0Prior+Knowledge&rft.creator=Puglisi%2C+L&rft.creator=Alexander%2C+DC&rft.creator=Rav%C3%AC%2C+D&rft.description=In+this+work%2C+we+introduce+Brain+Latent+Progression+(BrLP)%2C+a+novel+spatiotemporal+disease+progression+model+based+on+latent+diffusion.+BrLP+is+designed+to+predict+the+evolution+of+diseases+at+the+individual+level+on+3D+brain+MRIs.+Existing+deep+generative+models+developed+for+this+task+are+primarily+data-driven+and+face+challenges+in+learning+disease+progressions.+BrLP+addresses+these+challenges+by+incorporating+prior+knowledge+from+disease+models+to+enhance+the+accuracy+of+predictions.+To+implement+this%2C+we+propose+to+integrate+an+auxiliary+model+that+infers+volumetric+changes+in+various+brain+regions.+Additionally%2C+we+introduce+Latent+Average+Stabilization+(LAS)%2C+a+novel+technique+to+improve+spatiotemporal+consistency+of+the+predicted+progression.+BrLP+is+trained+and+evaluated+on+a+large+dataset+comprising+11%2C730+T1-weighted+brain+MRIs+from+2%2C805+subjects%2C+collected+from+three+publicly+available%2C+longitudinal+Alzheimer%E2%80%99s+Disease+(AD)+studies.+In+our+experiments%2C+we+compare+the+MRI+scans+generated+by+BrLP+with+the+actual+follow-up+MRIs+available+from+the+subjects%2C+in+both+cross-sectional+and+longitudinal+settings.+BrLP+demonstrates+significant+improvements+over+existing+methods%2C+with+an+increase+of+22%25+in+volumetric+accuracy+across+AD-related+brain+regions+and+43%25+in+image+similarity+to+the+ground-truth+scans.+The+ability+of+BrLP+to+generate+conditioned+3D+scans+at+the+subject+level%2C+along+with+the+novelty+of+integrating+prior+knowledge+to+enhance+accuracy%2C+represents+a+significant+advancement+in+disease+progression+modeling%2C+opening+new+avenues+for+precision+medicine.+The+code+of+BrLP+is+available+at+the+following+link%3A+https%3A%2F%2Fgithub.com%2FLemuelPuglisi%2FBrLP.&rft.publisher=Springer+Nature&rft.date=2024&rft.type=Proceedings+paper&rft.language=eng&rft.source=+++++In%3A++Medical+Image+Computing+and+Computer+Assisted+Intervention+%E2%80%93+MICCAI+2024.++(pp.+pp.+173-183).++Springer+Nature+(2024)+++++&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F10198880%2F