# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
## 2 Materials and Methods
### Cycle-Consistent Generative Adversarial Network
#### 2.2.1 Overall Architecture
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Age (Sex)** & **MR Scan** & **Voxel Size (mm)** & **Matrix Size** & **CT Scan** & **Voxel Size (mm)** & **Matrix Size** \\ \hline
48 (F) & Philips Achieva dStream & 0.36 \(\times\) 0.36 \(\times\) 3.3 & 480 \(\times\) 480 \(\times\) 29 & Siemens Sensation 64 & 0.74 \(\times\) 0.74 \(\times\) 1 & 512 \(\times\) 512 \(\times\) 547 \\
46 (F) & Philips Intera & 0.35 \(\times\) 0.35 \(\times\) 4 & 512 \(\times\) 512 \(\times\) 20 & Philips Brilliance 64 & 0.27 \(\times\) 0.27 \(\times\) 1 & 768 \(\times\) 768 \(\times\) 468 \\
53 (F) & Philips Ingenia & 0.36 \(\times\) 0.36 \(\times\) 3 & 448 \(\times\) 448 \(\times\) 34 & Philips Brilliance 64 & 0.23 \(\times\) 0.23 \(\times\) 1.4 & 768 \(\times\) 768 \(\times\) 314 \\
37 (F) & Philips Achieva & 0.31 \(\times\) 0.31 \(\times\) 3 & 512 \(\times\) 512 \(\times\) 32 & Philips Brilliance 64 & 0.26 \(\times\) 0.26 \(\times\) 1.4 & 768 \(\times\) 768 \(\times\) 228 \\
40 (M) & Philips Intera & 0.39 \(\times\) 0.39 \(\times\) 4 & 512 \(\times\) 512 \(\times\) 30 & Toshiba Acquisition 0.36 \(\times\) 0.36 \(\times\) 0.5 & 512 \(\times\) 512 \(\times\) 621 \\
57 (F) & Philips Ingenia & 0.36 \(\times\) 0.36 \(\times\) 3 & 448 \(\times\) 448 \(\times\) 30 & Philips Brilliance 64 & 0.27 \(\times\) 0.27 \(\times\) 1.4 & 768 \(\times\) 768 \(\times\) 357 \\
20 (M) & Philips Achieva dStream & 0.35 \(\times\) 0.35 \(\times\) 3 & 512 \(\times\) 512 \(\times\) 32 & Toshiba Acquisition 0.38 \(\times\) 0.38 \(\times\) 0.5 & 512 \(\times\) 512 \(\times\) 511 \\
57 (M) & Philips Ingenia & 0.34 \(\times\) 0.34 \(\times\) 3 & 560 \(\times\) 560 \(\times\) 32 & Philips Brilliance 64 & 0.28 \(\times\) 0.28 \(\times\) 1.4 & 768 \(\times\) 768 \(\times\) 392 \\
42 (F) & Philips Achieva dStream & 0.26 \(\times\) 0.26 \(\times\) 4 & 640 \(\times\) 640 \(\times\) 20 & Philips Brilliance 64 & 0.26 \(\times\) 0.26 \(\times\) 1.4 & 768 \(\times\) 768 \(\times\) 249 \\
38 (M) & Philips Ingenia & 0.26 \(\times\) 0.26 \(\times\) 3 & 1008 \(\times\) 1008 \(\times\) 38 & Toshiba Acquisition 0.49 \(\times\) 0.49 \(\times\) 0.5 & 512 \(\times\) 512 \(\times\) 496 \\
59 (M) & Philips Achieva dStream & 0.35 \(\times\) 0.35 \(\times\) 3 & 512 \(\times\) 512 \(\times\) 32 & Siemens Sensation 64 & 0.35 \(\times\) 0.35 \(\times\) 1 & 512 \(\times\) 512 \(\times\) 372 \\
26 (M) & Siemens & 0.63 \(\times\) 0.63 \(\times\) 3.5 & 320 \(\times\) 320 \(\times\) 28 & Philips Incisive CT & 0.46 \(\times\) 0.46 \(\times\) 0.8 & 451 \(\times\) 451 \(\times\) 485 \\
33 (M) & Philips Achieva dStream & 0.36 \(\times\) 0.36 \(\times\) 3 & 448 \(\times\) 448 \(\times\) 30 & Philips Brilliance 64 & 0.39 \(\times\) 0.39 \(\times\) 1.4 & 768 \(\times\) 768 \(\times\) 349 \\
57 (F) & Philips Ingenia & 0.36 \(\times\) 0.36 \(\times\) 3 & 448 \(\times\) 448 \(\times\) 30 & Philips Incisive CT & 0.38 \(\times\) 0.38 \(\times\) 1 & 512 \(\times\) 512 \(\times\) 200 \\
51 (M) & Philips Ingenia & 0.36 \(\times\) 0.36 \(\times\) 3 & 448 \(\times\) 448 \(\times\) 36 & Philips Brilliance 64 & 0.27 \(\times\) 0.27 \(\times\) 1.4 & 768 \(\times\) 768 \(\times\) 283 \\
70 (F) & Philips Ingenia & 0.34 \(\times\) 0.34 \(\times\) 3 & 528 \(\times\) 528 \(\times\) 30 & Philips Spectral 0.50 \(\times\) 0.50 \(\times\) 2 & 512 \(\times\) 512 \(\times\) 126 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic and technical details from patients included in the dataset. mapping of features from one domain to the other. In conjunction with these generative networks, we introduce a pair of discriminative networks, denoted as D and E, strategically positioned to act as judges, evaluating the authenticity and quality of images within the MR and CT domains, respectively. This duality ensures a comprehensive assessment of the synthesized images in relation to their intended domains.