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. 2020 Jul 14;296(3):627–637. doi: 10.1148/radiol.2020192793

Figure 1a:

(a) Image shows conceptual framework of study. Two deep learning models combined multiple inputs from baseline PET and MRI to predict relative cerebral blood flow (CBF) change (rΔCBF) after acetazolamide, with rΔCBF measured with PET (PET-rΔCBF) as reference. PET-plus-MRI model used both baseline PET and MRI inputs. MRI-only model used only MRI inputs. (b) Image shows architecture of deep learning model (PET-plus-MRI model shown for simplicity; MRI-only model would exclude baseline PET CBF map from inputs). Network components are color coded and labeled at bottom, and input and output image dimensions are labeled. Channel numbers in each step are shown above blocks. ASL = arterial spin labeling, ATT = arterial transit time, BN = batch normalization layer, CONV = convolutional layer, FLAIR = fluid-attenuated inversion-recovery, linear = linear layer, mASL = mean ASL difference signal, MD = multidelay, PD = proton density–weighted image, ReLU = rectifier linear unit, SD = single-delay, Syn-rΔCBF = synthetic rΔCBF predicted by deep learning models.

(a) Image shows conceptual framework of study. Two deep learning models combined multiple inputs from baseline PET and MRI to predict relative cerebral blood flow (CBF) change (rΔCBF) after acetazolamide, with rΔCBF measured with PET (PET-rΔCBF) as reference. PET-plus-MRI model used both baseline PET and MRI inputs. MRI-only model used only MRI inputs. (b) Image shows architecture of deep learning model (PET-plus-MRI model shown for simplicity; MRI-only model would exclude baseline PET CBF map from inputs). Network components are color coded and labeled at bottom, and input and output image dimensions are labeled. Channel numbers in each step are shown above blocks. ASL = arterial spin labeling, ATT = arterial transit time, BN = batch normalization layer, CONV = convolutional layer, FLAIR = fluid-attenuated inversion-recovery, linear = linear layer, mASL = mean ASL difference signal, MD = multidelay, PD = proton density–weighted image, ReLU = rectifier linear unit, SD = single-delay, Syn-rΔCBF = synthetic rΔCBF predicted by deep learning models.