Kermani Milad Zeinali, Tavakoli Mohamad Bagher, Khorasani Amir, Abedi Iraj, Sadeghi Vahid, Amouheidari Alireza
Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Bioimaging, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Phys Eng Sci Med. 2025 Jul 22. doi: 10.1007/s13246-025-01606-1.
Radiotherapy is a crucial treatment for brain tumor malignancies. To address the limitations of CT-based treatment planning, recent research has explored MR-only radiotherapy, requiring precise MR-to-CT synthesis. This study compares two deep learning approaches, supervised (Pix2Pix) and unsupervised (CycleGAN), for generating pseudo-CT (pCT) images from T1- and T2-weighted MR sequences. 3270 paired T1- and T2-weighted MRI images were collected and registered with corresponding CT images. After preprocessing, a supervised pCT generative model was trained using the Pix2Pix framework, and an unsupervised generative network (CycleGAN) was also trained to enable a comparative assessment of pCT quality relative to the Pix2Pix model. To assess differences between pCT and reference CT images, three key metrics (SSIM, PSNR, and MAE) were used. Additionally, a dosimetric evaluation was performed on selected cases to assess clinical relevance. The average SSIM, PSNR, and MAE for Pix2Pix on T1 images were 0.964 ± 0.03, 32.812 ± 5.21, and 79.681 ± 9.52 HU, respectively. Statistical analysis revealed that Pix2Pix significantly outperformed CycleGAN in generating high-fidelity pCT images (p < 0.05). There was no notable difference in the effectiveness of T1-weighted versus T2-weighted MR images for generating pCT (p > 0.05). Dosimetric evaluation confirmed comparable dose distributions between pCT and reference CT, supporting clinical feasibility. Both supervised and unsupervised methods demonstrated the capability to generate accurate pCT images from conventional T1- and T2-weighted MR sequences. While supervised methods like Pix2Pix achieve higher accuracy, unsupervised approaches such as CycleGAN offer greater flexibility by eliminating the need for paired training data, making them suitable for applications where paired data is unavailable.
放射治疗是脑肿瘤恶性疾病的关键治疗方法。为解决基于CT的治疗计划的局限性，最近的研究探索了仅使用磁共振成像（MR）的放射治疗，这需要精确的MR到CT合成。本研究比较了两种深度学习方法，即监督式（Pix2Pix）和无监督式（CycleGAN），用于从T1加权和T2加权MR序列生成伪CT（pCT）图像。收集了3270对T1加权和T2加权MRI图像，并与相应的CT图像进行配准。预处理后，使用Pix2Pix框架训练了一个监督式pCT生成模型，还训练了一个无监督生成网络（CycleGAN），以便能够相对于Pix2Pix模型对pCT质量进行比较评估。为评估pCT与参考CT图像之间的差异，使用了三个关键指标（结构相似性指数（SSIM）、峰值信噪比（PSNR）和平均绝对误差（MAE））。此外，对选定病例进行了剂量学评估，以评估临床相关性。Pix2Pix在T1图像上的平均SSIM、PSNR和MAE分别为0.964±0.03、32.812±5.21和79.681±9.52HU。统计分析表明，在生成高保真pCT图像方面，Pix2Pix显著优于CycleGAN（p<0.05）。对于生成pCT，T1加权与T2加权MR图像的有效性没有显著差异（p>0.05）。剂量学评估证实了pCT与参考CT之间的剂量分布具有可比性，支持临床可行性。监督式和无监督式方法都证明了能够从传统的T1加权和T2加权MR序列生成准确的pCT图像。虽然像Pix2Pix这样的监督式方法具有更高的准确性，但像CycleGAN这样的无监督式方法通过消除对配对训练数据的需求提供了更大的灵活性，使其适用于无法获得配对数据的应用。
