James P Pirruccello, Paolo Di Achille, Seung Hoan Choi, Joel T Rämö, Shaan Khurshid, Mahan Nekoui, Sean J Jurgens, Victor Nauffal, Shinwan Kany, Kenney Ng, Samuel F Friedman, Puneet Batra, Kathryn L Lunetta, Aarno Palotie, Anthony A Philippakis, Jennifer E Ho, Steven A Lubitz, Patrick T Ellinor
Deep learning of left atrial structure and function provides link to atrial fibrillation risk
Nature communications
paper aims solve arterial input function aif determination dynamic contrastenhanced mri dcemri important linear illposed inverse problem using maximum entropy technique met regularization functionals addition estimating pharmacokinetic parameters dcemr image investigations urgent need obtain precise information aifthe concentration contrast agent left ventricular blood pool measured time reason main idea show find unique solution linear system equations generally form formula see text named illconditioned linear system equations discretization integral equations appear different tomographic image restoration reconstruction issues new algorithm described estimate appropriate probability distribution function aif according met regularization functionals contrast agent concentration applying bayesian estimation approach estimate two different pharmacokinetic parameters moreover using proposed approach analyzing simulated real datasets breast tumors according pharmacokinetic factors indicates using bayesian inferencethat infer uncertainties computed solutions specific knowledge noise errorscombined regularization functional maximum entropy problem improved convergence behavior led consistent morphological functional statistics results finally comparison proposed exponential distribution based met newtons method weibull distribution via met teachinglearningbased optimization mettlbo previous studies family gamma erlang distributions estimated new algorithm appropriate robust aifs