CT is commonly used in orthopedic procedures. MRI is used along with CT to
identify muscle structures and diagnose osteonecrosis due to its superior soft
tissue contrast. However, MRI has poor contrast for bone structures. Clearly,
it would be helpful if a corresponding CT were available, as bone boundaries
are more clearly seen and CT has standardized (i.e., Hounsfield) units.
Therefore, we aim at MR-to-CT synthesis. The CycleGAN was successfully applied
to unpaired CT and MR images of the head, these images do not have as much
variation of intensity pairs as do images in the pelvic region due to the
presence of joints and muscles. In this paper, we extended the CycleGAN
approach by adding the gradient consistency loss to improve the accuracy at the
boundaries. We conducted two experiments. To evaluate image synthesis, we
investigated dependency of image synthesis accuracy on 1) the number of
training data and 2) the gradient consistency loss. To demonstrate the
applicability of our method, we also investigated a segmentation accuracy on
synthesized images.

本文研究如何通过 MR-to-CT 合成技术提高诊断组织与骨骼疾病的精准度，结合 CycleGAN 及梯度一致性损失函数的方法有效提升了对骨骼的边界识别。

使用 CycleGAN 从不成对数据中进行跨模态图像合成：梯度一致性损失和训练数据规模的影响

Cross-modality image synthesis from unpaired data using CycleGAN:  Effects of gradient consistency loss and training data size

MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis.
Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned
MR and CT training images of the same patient. However, misalignment between
paired images could lead to errors in synthesized CT images. To overcome this,
we propose to train a generative adversarial network (GAN) with unpaired MR and
CT images. A GAN consisting of two synthesis convolutional neural networks
(CNNs) and two discriminator CNNs was trained with cycle consistency to
transform 2D brain MR image slices into 2D brain CT image slices and vice
versa. Brain MR and CT images of 24 patients were analyzed. A quantitative
evaluation showed that the model was able to synthesize CT images that closely
approximate reference CT images, and was able to outperform a GAN model trained
with paired MR and CT images.

本文提出了一种使用生成对抗网络（GAN）实现未配对 MR 和 CT 图像的合成的方法，可以利用其进行放射治疗等方面的治疗规划。经过定量评估，该模型能够合成接近参考 CT 图像的 CT 图像，并且能够超过使用配对 MR 和 CT 图像训练的 GAN 模型。