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基于深度学习的运动补偿重建用于加速四维磁共振指纹

Lu Wang¹, Chenyang Liu¹, Yinghui Wang¹

¹Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.

International journal of radiation oncology, biology, physics

|October 25, 2025

概括

DeepMocor是一种新的深度学习方法,可以将运动补偿4D-MRF重建加速24倍. 这一进步显著提高了肝脏放射治疗计划的效率.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
放射治疗规划放射治疗规划

背景情况:

传统的4D-MRF重建是耗时的,限制了其临床效用.
运动补偿对于准确的4D-MRF在腹部成像中至关重要.
为了有效的治疗计划,需要加速重建方法.

研究的目的:

开发和验证DeepMocor,这是一种基于深度学习的方法,用于运动补偿的4D-MRF.
加速传统的4D-MRF重建,以改善临床工作流程.
为了实现更有效的临床治疗规划,特别是肝癌.

主要方法:

涉及19名肝细胞癌患者的前性研究,使用3T核磁共振扫描仪.
DeepMocor使用运动场初始化,精细化和4D-MRF重建.
对使用PSNR,SSIM,MAPE,CNR,AMD和PCC等指标的替代方法进行性能评估.

主要成果:

DeepMocor实现了高图像质量 (PSNR: ~25.5,SSIM: ~0.86) 和组织属性准确性 (MAPE: 3.1%-15.8%).
准确的瘤运动跟踪被证明具有低平均运动差异 (AMD:0.32-0.62毫米) 和高相关性 (PCC:0.94-0.96).
在大多数评估指标中,DeepMocor显著超过了替代方法.

结论:

与传统方法相比,DeepMocor可以在4D-MRF重建中加速24倍.
该方法显示了显著提高肝脏放射治疗规划效率的潜力.
DeepMocor代表了癌症治疗医学成像学的有前途的进步.

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