Algorithm 1

$$\begin{array}{cc}\hfill & 1\mathbf{Input}:\text{A set of training low-dose PET images}{\mathbf{I}}^L=\{I_1^L,I_2^L,\dots ,I_N^L\},\text{a set of training}\hfill \\ \hfill & \text{full-dose PET images}{\mathbf{I}}^F=\{I_1^F,I_2^F,\dots ,I_N^F\}.\text{N is the total number of training subjects}.\hfill \\ \hfill & \text{Parameters}:\text{concatenated number}H.\hfill \\ \hfill & 2\mathbf{Initialize}:h=1.{\mathbf{I}}^L\text{is used as the initial estimation}{\tilde{\mathbf{I}}}^{F(0)}\text{of the set of full-dose}\hfill \\ \hfill & \text{images}{\mathbf{I}}^F.\hfill \\ \hfill & 3\mathbf{while}\mathit{h}<H\hfill \\ \hfill & 4\text{Perform the 3D c-GANs between}{\tilde{\mathbf{I}}}^{F(h-1)}\text{and}{\mathbf{I}}^F\text{to obtain the generator network}{G}_h\hfill \\ \hfill & \text{and discriminator network}{D}_h\text{according to Eq.}(4).\hfill \\ \hfill & 5\text{For each training subject}i(i=1,2,\dots ,N),\text{use the above trained generator network}{G}_h\hfill \\ \hfill & \text{to generate the estimated full-dose PET image}{\tilde{I}}_i^{F(h)}.\text{Finally, get the estimations for}\hfill \\ \hfill & \text{all training subjects}{\tilde{\mathbf{I}}}^{F(h)}.\hfill \\ \hfill & 6h=h+1.\hfill \\ \hfill & 7\mathrm{End}\mathrm{while}\hfill \\ \hfill & 8\mathbf{Output}:\text{The trained generator networks}\{G_1,G_2,\dots ,G_H\}\text{and discriminator networks}\hfill \\ \hfill & \{D_1,D_2,\dots ,D_H\}.\hfill \end{array}$$