# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
## 4 Discussion  
We developed and implemented a novel pseudo-CT synthesis framework, based on the CycleGAN method, specifically focused on synthesizing pseudo-CT images in knee imaging, aiming to address the limitations observed in existing methods. Thus, we demonstrated how the key feature of the CycleGAN, relying on its use of adversarial \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**MAE** & **PSNR** & **SSIM** & **ZNCC** & **MI** & **RC** & \(\mid\)**RC\(\mid\) \\ \hline
30.4638 & 28.1168 & 0.9230 & 0.9807 & 0.8548 & 0.0126 & 0.0440 \\ (7.4770) & (1.5245) & (0.0217) & (0.0071) & (0.1019) & (0.0344) & (0.0246) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the median (and median absolute deviation) Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Zero-Normalized Cross Correlation (ZNCC), Mutual Information (MI), Relative Change (RC), and absolute Relative Change (\(\mid\)RC\(\mid\)) metrics in the dataset. Figure 4: Voxel-by-voxel correlation plot between CT and pseudo-CT of all images in the dataset, expressed in normalized Hounsfield units (HU) (**left**) and Bland–Altman plot between CT and pseudo-CT for all images in the dataset (**right**). Gray scale bars show density of voxels in histogram grid. training and the principle of cycle consistency led to the successful generation of continuous knee pseudo-CTs. In recent years, several other DL-based pseudo-CT synthesis methods have been described for head, neck, and pelvis; however, to the best of our knowledge, this study represents the first reported effort in the field of MR-based knee pseudo-CT synthesis, shedding light to significantly advance the field of knee imaging. Despite the evident differences between these anatomical areas, our results are in line with those recently described by other authors in the literature. The qualitative (Figure 3) and quantitative (Figure 4 and Table 2) analyses indicated the promising potential of the method. As qualitatively depicted in Figure 3, the discrepancies observed at the bone boundaries may largely derive from defects in the MR-CT intra-subject registration, a task which is particularly complex due to multiple factors: differences in patient positioning between the CT and MR scans, the complex anatomy of the knee, and the high freedom of movement of the bone structures. As part of this evaluation, the management of artifacts in the pseudo-CT synthesis process is crucial; in this sense, we also demonstrated how the CyclGAN is able to handle the generation of densities associated with implants, as could be seen in the synthesis of the screws in second patient in Figure 3. This fact may have implications for the clinical usability of the synthesized images. As seen in the quantitative results shown in Figure 4, pseudo-CT images are very similar to their corresponding ground-truth CT images. The correlation plot showed an excellent correlation between the pseudo-CT HUs and the ground-truth CT HUs, and he Bland-Altman plot showed that the average of differences was low, demonstrating that the difference between methods was negligible below 200 HU and tended to decrease as the average increased, specially above 900 HU. As described by the numerical results reported in Table 2, all metrics convey to the same story, illustrating the strong interrelation between the synthesized pseudo-CT images and their corresponding ground-truth CT images. The significance of our artificially synthesized pseudo-CT holds crucial importance within the clinical context, specially when considering applications that require the precise delineation of knee bones and/or the computation of a pseudo-CT, such as in knee segmentation tasks, PET/MR attenuation correction and/or radiotherapy planning, where MR-CT datasets are usually limited, leading to larger, highly flexible, and generalizable images. Our study had several limitations. First, our dataset was very small, specially given the anatomical complexity of the knee.