Juliana T Camargo, Mariana Ramirez, Byron J Gajewski, Debra K Sullivan, Susan E Carlson, Heather D Gibbs
Nutrition Literacy Among Latina/x People During Pregnancy Is Associated With Socioeconomic Position
Journal of the Academy of Nutrition and Dietetics
variety deep learningbased iterative approaches available predict tracer kinetic tk parameters fully sampled undersampled dynamic contrastenhanced dce mri data however methods offer distinct benefits drawbacks propose hybrid algorithm named greybox using model well dlbased solving multiparametric nonlinear inverse problem directly estimating tk parameters undersampled dce mri data invariant undersampling rate proposed algorithm inspired plugandplay algorithms used solving linear inverse imaging problems technique tested effectiveness solving nonlinear illposed inverse problem generating 3d tk parameter maps fourdimensional 4d spatial temporal retrospectively undersampled kspace data algorithm learns deep learningbased prior using unet estimate ktransmathbf ktrans vpmathbf vp parameters based patlak pharmacokinetic model trained prior utilized estimate tk parameter maps using iterative gradientbased optimization scheme unlike existing dl models network invariant undersampling rate input data proposed method compared total variationbased direct reconstruction technique brain breast prostate dcemri datasets various undersampling rates using radial golden angle rga scheme breast dataset indirect estimation using fast composite splitting algorithm utilized comparison undersampling rates 8 12 20 used experiments results compared using psnr ssim metrics breast dataset 10 patients data four patients utilized training 1032 samples two validation 752 samples entire volume four patients testing similarly prostate dataset 18 patients 10 patients utilized training 720 samples five validation 216 samples whole volume three patients testing brain dataset nineteen patients ten patients used training 3152 samples five validation 1168 samples whole volume four patients testing statistical tests also conducted assess significance improvement performance experiments showed proposed greybox performs significantly better direct reconstruction methods proposed algorithm improved estimated ktransmathbf ktrans vpmathbf vp terms peak signaltonoise ratio 3 db compared standard reconstruction methods proposed hybrid reconstruction algorithm greybox provide stateoftheart performance solving nonlinear inverse problem dcemri also first kind utilize convolutional neural networkbased encodings part plugandplay priors improve performance reconstruction algorithm