# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
###### Abstract  
In the field of knee imaging, the incorporation of MR-based pseudo-CT synthesis holds the potential to mitigate the need for separate CT scans, simplifying workflows, enhancing patient comfort, and reducing radiation exposure. In this work, we present a novel DL framework, grounded in the development of the Cycle-Consistent Generative Adversarial Network (CycleGAN) method, tailored specifically for the synthesis of pseudo-CT images in knee imaging to surmount the limitations of current methods. Upon visually examining the outcomes, it is evident that the synthesized pseudo-CTs show an excellent quality and high robustness. Despite the limited dataset employed, the method is able to capture the particularities of the bone contours in the resulting image. The experimental 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 (IRC 1) report values of \(30.4638\pm 7.4770\), \(28.1168\pm 1.5245\), \(0.9230\pm 0.0217\), \(0.9807\pm 0.0071\), \(0.8548\pm 0.1019\), \(0.0055\pm 0.0265\), and \(0.0302\pm 0.0218\) (median \(\pm\) median absolute deviation), respectively. The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield units (m = 0.9785; adjusted \(R^{2}\) = 0.9988; \(\rho\) = 0.9849; \(p\) < 0.001). The Bland-Altman plot shows that the average of the differences is low (\((HU_{CT}-HU_{pseudo-CT}\) = 0.7199\(\pm\) 35.2490; 95% confidence interval \([-68.3681,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\  
correction is imperative to derive precise and quantifiable metabolic information [7; 8; 9]. Furthermore, when it comes to therapy, radiation therapy planning requires the precise delivery of radiation to target tissue while minimizing exposure to surrounding healthy structures, where the level of accuracy in targeting is critical to the effectiveness and safety of the treatment [10; 11]. Conventionally, CT scans are fundamental for both attenuation correction and radiation therapy planning, serving as the primary modality for generating patient-specific attenuation maps. In this context, recent advances in MR image acquisition and post-processing, including ultrashort echo time, zero echo time, T1-weighted gradient recalled echo, and susceptibility-weighted MR sequences, are showing promising results in MR-based bone visualization [12; 13; 14; 15; 16]. Additionally, the incorporation of MR-based pseudo-CT synthesis in knee imaging holds the potential to mitigate the need for separate CT scans, simplifying the workflow, enhancing patient comfort, and reducing radiation exposure, all while maintaining or even enhancing diagnostic and/or treatment accuracy. The existing landscape of MR-based pseudo-CT synthesis includes various methods and has evolved from traditional atlas-based techniques, which leverage preregistered CT-MR image pairs from a database to map MR images to their corresponding CT analogs [17; 18; 19]. These early methods demonstrated the potential of MR-based pseudo-CT synthesis but were limited by their reliance on anatomical priors, which could lead to inaccuracies in complex or atypical cases. In recent years, the field has witnessed a profound transformation, driven by the emergence of machine learning and, more specifically, deep learning (DL) techniques [20; 21; 22]. These advanced approaches have introduced a new era in MR-based pseudo-CT synthesis, characterized by their ability to autonomously learn and map the intricate relationships between MR and CT images. DL-based methods, often rooted in convolutional neural networks (CNNs) [23; 24; 25] and, more recently, generative adversarial networks (GANs) [26; 27; 28], have shown remarkable promise in generating high-quality pseudo-CT images from MR data.