用于三维医学图像到图像转换的自适应潜在扩散模型：多模态磁共振成像研究
Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study
AI总结：
本文提出基于潜在扩散模型和MS-SPADE可切换模块的三维医学图像转换方法，实现单模型一对多模态MRI合成，并在多模态脑MRI和IXI数据集上优于现有模型。
摘要：
多模态图像在医学图像分析的综合评估中发挥着关键作用，为识别临床上重要的生物标志物提供互补信息。然而，在临床实践中，由于扫描成本、扫描时间有限以及安全考虑等原因，获取多种模态可能具有挑战性。在本文中，我们提出了一种基于潜在扩散模型（LDM）的模型，该模型利用可切换模块在三维医学图像中实现图像到图像的转换，无需进行图像块裁剪。三维LDM结合目标模态的条件引导，能够在三维空间中生成高质量的目标模态，克服了二维生成方法中缺失层间信息的不足。可切换模块被称为多重可切换空间自适应归一化（MS-SPADE），它动态地将源潜在表示转换为目标潜在表示所需的风格，以辅助扩散过程。MS-SPADE模块使我们能够使用单一模型处理从一种源模态到多种目标模态的众多转换任务，从而无需为不同场景构建多个转换模型。我们的模型在不同源-目标模态场景下展示了成功的图像合成，并在使用四种不同模态的多模态脑磁共振成像数据集以及独立的IXI数据集进行的定量评估中超越了其他模型。我们的模型在各种模态之间展示了成功的图像合成，甚至支持一对多的模态转换。此外，它在定量评估中优于其他一对一的转换模型。
英文摘要：
Multi-modal images play a crucial role in comprehensive evaluations in medical image analysis providing complementary information for identifying clinically important biomarkers. However, in clinical practice, acquiring multiple modalities can be challenging due to reasons such as scan cost, limited scan time, and safety considerations. In this paper, we propose a model based on the latent diffusion model (LDM) that leverages switchable blocks for image-to-image translation in 3D medical images without patch cropping. The 3D LDM combined with conditioning using the target modality allows generating high-quality target modality in 3D overcoming the shortcoming of the missing out-of-slice information in 2D generation methods. The switchable block, noted as multiple switchable spatially adaptive normalization (MS-SPADE), dynamically transforms source latents to the desired style of the target latents to help with the diffusion process. The MS-SPADE block allows us to have one single model to tackle many translation tasks of one source modality to various targets removing the need for many translation models for different scenarios. Our model exhibited successful image synthesis across different source-target modality scenarios and surpassed other models in quantitative evaluations tested on multi-modal brain magnetic resonance imaging datasets of four different modalities and an independent IXI dataset. Our model demonstrated successful image synthesis across various modalities even allowing for one-to-many modality translations. Furthermore, it outperformed other one-to-one translation models in quantitative evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
