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膀MRI与基于深度学习的重建:使用VI-RADS进行肌肉侵入性的前性评估

Xinxin Zhang¹, Yichen Wang¹, Xiaojuan Xu¹

¹Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.

Abdominal radiology (New York)

|April 23, 2024

概括

深度学习重建 (DLR) 显著提高了膀MRI质量,减少了扫描时间. 这种技术在膀癌评估中保持了膀成像报告和数据系统 (VI-RADS) 的诊断准确性.

科学领域:

放射学放射学是指放射学
医疗成像医学成像
人工智能在医学中的应用

背景情况:

膀癌的诊断依赖于磁共振成像 (MRI).
目前的MRI协议可能会耗费大量时间.
优化MRI序列对于有效和准确的诊断至关重要.

研究的目的:

评估深度学习重建 (DLR) 对膀MRI的影响.
使用膀成像报告和数据系统 (VI-RADS) 评估检查时间,图像质量和诊断性能的变化.

主要方法:

对70名膀癌患者进行前性研究.
将标准T2加权成像 (T2WI_S) 和扩散加权成像 (DWI_S) 与快速DLR增强序列 (T2WI_DL,DWI_DL) 进行比较.
评估图像质量 (SNR,CNR,定性分数) 和VI-RADS性能.

主要成果:

DLR序列 (T2WI_DL,DWI_DL) 将获取时间缩短了45% (5:57分钟到 3:13分钟).
显著改善了SNR,CNR和定性图像质量 (整体质量,清晰度,损伤明显性).
表面扩散系数 (ADC) 或VI-RADS诊断性能 (AUC) 没有显著差异.

结论:

深度学习重建有效地减少了膀MRI检查时间.
DLR显著提高图像质量,而不会影响VI-RADS的诊断准确性.
DLR是评估膀癌肌肉入侵的有希望的工具.

关键词:

深度学习重建的重建这就是为什么MRI是MRI.尿道膀的新生病.VI-WRADS 的使用情况.

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