【摘要】 MR图像在软组织成像中的优越性使其在医学诊断和放射治疗中不可或缺，但采集成本和禁忌等因素限制了其广泛应用；相比之下CT扫描具有成像速度快、费用低的优点。研究围绕生成式深度学习模型在CT到MR图像跨模态合成领域的研究进展进行综述与分析，从脊柱病变、急性缺血性脑卒中和肿瘤分割等临床场景分析多种MR图像合成方法的技术特性、性能优势及其面临的挑战。最后进一步探讨医学图像合成的应用价值和未来研究前景。更多还原

【Abstract】 The superiority of magnetic resonance(MR) images in soft tissue imaging makes them indispensable for medical diagnosis and radiotherapy, but factors such as acquisition cost and contraindications limit their widespread application. In contrast, computed tomography(CT) scanning has the advantages of fast imaging speed and low cost. Herein, this review summarizes the research progress of generative deep learning models in the field of medical CT to MR image synthesis, and especially analyzes the technical characteristics, performance advantages, and challenges of various MR image synthesis methods from clinical scenarios such as spinal lesions, acute ischemic stroke, and tumor segmentation. Furthermore, the application value and future research prospects of medical image synthesis are discussed.更多还原