# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
## 5 Conclusions  
In this work, we described the development and initial validation of a CycleGAN approach to synthesize knee pseudo-CT images from standard T1-weighted MR images. Our proposed method includes minimal error when contrasted with their analogous CT image. Additionally, our results are in line with those previously described for other anatomical areas in the literature. This work serves as a proof of concept to demonstrate the great potential of CycleGANs for pseudo-CT synthesis as well as the feasibility of these methods using real clinical datasets. 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.  
However, further developments are needed before integrating the proposed method in real-world clinical settings. On the one hand, some efforts must be made to implement an external validation in a multi-center clinical essay that increments significantly the dataset size. On the other hand, this new dataset must include pathologies commonly seen in knee imaging (e.g., fractures, torn meniscus or ligament, osteoarthritis, arthrofibrosis, tendinopathies, osteochondroma, bursitis, and synovial or popltical cysts) and evaluate the performance of the method in these conditions.  
Conceptualization, J.B.-S., J.A.-B. and A.T.-C.; methodology, D.V.-C., J.M.M.-M., D.V.-H., J.A.-B. and A.T.-C.; software, D.V.-C., J.M.M.-M., D.V.[PERSON] and [PERSON]; validation, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]-C.; formal analysis, [PERSON]-[PERSON], [PERSON] and [PERSON]-C.; investigation, [PERSON]-[PERSON] and [PERSON]-C.; resources, [PERSON]-[PERSON], [PERSON]-[PERSON], and [PERSON]-C.; data curation, [PERSON]-C., [PERSON]-[PERSON], [PERSON]-O., [PERSON] and [PERSON]-C.; writing--original draft preparation, [PERSON]-[PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON]-[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; visualization, [PERSON], [PERSON] and [PERSON]; supervision, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; project administration, [PERSON] and [PERSON]-C.; funding acquisition, A.T.-C. All authors have read and agreed to the published version of the manuscript.  
This research is part of projects M3433 and M3438 for the establishment of a medical image analysis lab in radiomics and AI in the Dept. of Radiology (PI: [PERSON]), funded by Fundacion para la Investigacion Biomedica del Hospital Universitario Ramon y Cajal, (FIBioHRC). This research is also part of project PID2020-116769 RB-100, ADAPT-AI, (PI: [PERSON]) funded by Ministerio de Ciencia e Investigacion (MCIN)/Agencia Estatal de Investigacion (AEI).  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Hospital Universitario Ramon y Cajal (protocol 297/23).  
Patient consent was waived due to the retrospective nature of this study.  
The data presented in this study are not available available due to their intrinsic medical nature.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MR & Magnetic Resonance \\ CT & Computed Tomography \\ HU & Hounsfield Units \\ PET & Positron Emission Tomography \\ MAE & Mean Absolute Error \\ PSNR & Peak Signal-to-Noise Ratio \\ SSIM & Structural Similarity Index \\ ZNCC & Zero Normalized Cross Correlation \\ MI & Mutual Information \\ RC & Relative Change \\ \end{tabular}