对比GAN与扩散模型在MRI转CT中的表现，发现扩散模型更优。
Comparative Analysis of GAN and Diffusion for MRI-to-CT translation
- 将3D转换拆解为2D切片序列，降低计算成本。
- 多切片输入比单切片输入生成的CT图像更连续。
- 扩散模型在保持切片间连续性上显著优于GAN。
计算机断层扫描（CT）对治疗和诊断至关重要；当无法获取真实CT时，从磁共振成像（MRI）生成合成CT（sCT）成为重要需求。本文比较了两种常用架构在MRI-to-CT转换中的表现：条件生成对抗网络（cGAN）与条件去噪扩散概率模型（cDDPM）。采用经典实现：Pix2Pix代表cGAN，Palette代表cDDPM。将传统的3D转换问题分解为一系列横断面2D转换，以评估降低计算成本的可行性。同时研究单张与多张MRI切片作为条件输入的影响。通过全面评估协议进行性能测试，包含新提出的切片级指标相似性切片（SIMOS），用于衡量生成3D sCT时横断面间的连续性。结果表明，多通道条件输入和采用cDDPM架构可显著提升生成质量。
原文摘要 · Abstract (English)
Computed tomography (CT) is essential for treatment and diagnostics; In case CT are missing or otherwise difficult to obtain, methods for generating synthetic CT (sCT) images from magnetic resonance imaging (MRI) images are sought after. Therefore, it is valuable to establish a reference for what strategies are most effective for MRI-to-CT translation. In this paper, we compare the performance of two frequently used architectures for MRI-to-CT translation: a conditional generative adversarial network (cGAN) and a conditional denoising diffusion probabilistic model (cDDPM). We chose well-established implementations to represent each architecture: Pix2Pix for cGAN, and Palette for cDDPM. We separate the classical 3D translation problem into a sequence of 2D translations on the transverse plane, to investigate the viability of a strategy that reduces the computational cost. We also investigate the impact of conditioning the generative process on a single MRI image/slice and on multiple MRI slices. The performance is assessed using a thorough evaluation protocol, including a novel slice-wise metric Similarity Of Slices (SIMOS), which measures the continuity between transverse slices when compiling the sCTs into 3D format. Our comparative analysis revealed that MRI-to-CT generative models benefit from multi-channel conditional input and using cDDPM as an architecture.
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