【摘要】 核磁共振成像技术已广泛用于脑部、脊髓和心脏等相关疾病的临床诊断。然而，由采样时间、环境、设备质量等多种因素导致的成像噪声制约着诊断精度的进一步提高。综合研究了MRI降噪技术的发展脉络，系统梳理了基于滤波、变换、统计等传统MRI图像降噪方法，并重点分析了当前基于深度学习的MRI图像降噪系列新方法，展望了MRI图像降噪的未来发展趋势。最后，总结了现有医学图像质量评估方法，并指出针对依赖大量数据和人工标注医学图像样本、而可解释性较差的现有深度学习方法，需要探索性研究面向临床实际任务的医学图像质量评估新方法。更多还原

【Abstract】 Magnetic Resonance Imaging(MRI) has been extensively employed as an auxiliary means to diagnose pathological deterioration of brain, spinal cord and heart related diseases clinically. Nevertheless, imaging noise induced by both internal and external impacts restrict further improvement on diagnostic accuracy. This paper carries out a review on technological innovations ranging from earlier conventional approaches based on filter technique o state-of-the-art alternatives utilizing the deep learning network. Finally, some inductive summaries of the medical image quality assessments have been introduced. It also points out that existing deep learning methods,which rely on a large amount of data and manual annotation of medical image samples, are poorly interpretable. It is vital that clinical-oriented evaluation mechanism should be explored for clinical demands.更多还原